Sound warning system and its effect on cyclists’
awareness in traffic
Group 406
Group members:
Anastassia Andreasen
Amina Arshad
Gediminas Stankevicius
Yang Ou
Ran Wang
Sandy Phi Duong
Supervisor:
Amalia De Goetzen
MED4 Project, Aalborg University Copenhagen, 2013 spring
1
Semester:: 4th
Aalborg University Copenhagen
Title:
A.C. Meyers Vænge 15
Project Period: 4th february -24th may 2013
2450 København SV
Telefon: 9940 2500
Semester Theme:
Sonic Interaction: Design and Evaluation
Semester Coordinator:
Secretary:
Supervisor(s):
Project group no.:
Members:
Abstract:
This report covers the 4th semester project, by six
Medialogy students, group 406, made in the
spring/summer of 2013, at Aalborg University
Copenhagen. The project is created in relation to
the semester theme “Sonic Interactions: Design and
Evaluation”.
The
project
seeks
to
answer
the
problem
statement: To what extend could a sound warning
system increase cyclists’ awareness of incoming
traffic
while
listening
to
music
through
headphones? Testing of the warning system
prototype shows, that with help of sound
computing techniques, a warning system could
potentially decrease time needed for the cyclist to
know about approaching vehicles, which also
means an increase of traffic awareness.
The
paper
will
considerations,
guide
ideas
the
and
reader
choices
through
made
throughout analysis, design and implementation
Copies:
Pages:
Finished:
to testing and discussing the product, where ideas
for improving the product in a future perspective
will be introduced.
2
Table of Contents
1.
Introduction ........................................................................................................................................................ 5
1.1.
2.
Initial problem statement .................................................................................................................. 5
Pre-analysis......................................................................................................................................................... 6
2.1.
Target group ............................................................................................................................................ 6
2.2.
Target traffic environment ................................................................................................................ 7
2.3
Music listening and traffic awareness........................................................................................... 9
2.4.
Headphones............................................................................................................................................ 10
2.5.
State of the art ....................................................................................................................................... 13
2.5.1.
Bone Conduction Headphone ......................................................................................................... 13
2.5.2.
Microphones helping cyclists ......................................................................................................... 14
2.5.3.
Sound-Based Traffic Incident Detection .................................................................................... 14
2.5.4.
Traffic congestion estimation ......................................................................................................... 15
2.5.
Possible solutions for a warning system ................................................................................... 15
2.5.1.
System Input .......................................................................................................................................... 16
2.5.2.
System Output ....................................................................................................................................... 21
2.6.
Conclusion............................................................................................................................................... 23
2.7.
Final Problem Statement .................................................................................................................. 24
3.0. Analysis ............................................................................................................................................................... 25
3.1.
Sound properties from road traffic .............................................................................................. 25
3.1.1.
Frequency domain............................................................................................................................... 25
3.1.2.
Amplitude domain ............................................................................................................................... 26
3.1.3.
Target frequency and amplitude detection range ................................................................. 27
3.2.
Sound computing techniques ......................................................................................................... 28
3.2.1.
Additive synthesis ............................................................................................................................... 28
3.2.2.
Subtractive synthesis ......................................................................................................................... 28
3.2.3.
General knowledge about filters ................................................................................................... 29
3.3.
Envelope follower................................................................................................................................ 30
3.4.
MAX MSP ................................................................................................................................................. 30
3.5.
Audio feedback ....................................................................................................................................... 31
4.0. Design .................................................................................................................................................................. 34
4.1.
Requirements ........................................................................................................................................ 34
4.2.
Short description of the system ..................................................................................................... 35
4.2.
Overall structure of the system ..................................................................................................... 36
4.2.1. Input .......................................................................................................................................................... 36
4.2.2. Filtering and envelope follower .................................................................................................... 36
4.3. Sound feedback........................................................................................................................................... 37
3
5.
Implementation............................................................................................................................................... 38
5.1.
Sound source delimitation ............................................................................................................... 38
5.2.
Development.......................................................................................................................................... 39
5.2.1.
Sound file play ....................................................................................................................................... 39
5.3.
Soundscape............................................................................................................................................. 40
5.3.1.
Lowpass filter ........................................................................................................................................ 41
5.4.3. Envelope follower ........................................................................................................................................ 42
5.4.
6.
Feedback ....................................................................................................................................................... 43
Methods .............................................................................................................................................................. 45
6.1.
7.
T-test ......................................................................................................................................................... 45
Testing................................................................................................................................................................. 48
7.1. Test objectives.............................................................................................................................................. 48
7.2. Environmental settings ............................................................................................................................ 49
7.3.
Pilot testing............................................................................................................................................. 52
7.4.
Hypotheses test .................................................................................................................................... 53
7.5.
First hypothesis data analysis ........................................................................................................ 55
7.5.1.
Calculation .............................................................................................................................................. 56
7.5.2.
Interpretation ........................................................................................................................................ 56
7.6.
Second hypothesis data analysis ................................................................................................... 57
7.6.1.
Calculation .............................................................................................................................................. 58
7.6.2.
Interpretation ........................................................................................................................................ 58
7.7.
Presentation of hypothesis tests ................................................................................................... 59
8.
Discussion .......................................................................................................................................................... 60
9.
Conclusion ......................................................................................................................................................... 63
10. Future perspectives ....................................................................................................................................... 64
11. Appendix ............................................................................................................................................................ 65
11.1. Methods ........................................................................................................................................................ 65
11.2.
Questionnaire ........................................................................................................................................ 67
Bibliography ............................................................................................................................................................... 69
4
1. Introduction
Usage of portable electronic devices is rapidly increasing in the modern society. Such
devices are mp3 music players, smartphones, tablets, etc. They gives us an
opportunity to listen to music, talk to friends, and connect to the internet, social
websites and many other things at any time and place. People tend to use them on
the go, while cycling, driving a car or waiting for a bus. Such devices sometimes
engage us to a degree that we pay less attention to what is going on around us what
can be very dangerous in a traffic environment, especially while crossing the street
or cycling. While driving a car, walking or cycling it is unsafe to use certain portable
devices, as they distract attention. Attention in traffic could be distracted in a
physical, visual, cognitive or auditory manner [1]. Listening to music through
headphones could be an example for such, because the headphones can block the
incoming sound and makes it more difficult to hear the traffic. This problem is
especially relevant for cyclists, who are moving at the higher speeds than
pedestrians and find it difficult to see the incoming traffic from behind.
The research done by C. Goldenbeld in the Netherlands in 2009 [1] included 5505
participants, who used a bicycle minimum once a week and showed that almost
70 % of Dutch cyclists used some portable electronic device at least sometimes while
cycling [1]. 17% of the participants used electronic devices during almost every bike
trips [1]. Listening to music was on the top among other device usage, with 15% of
the cyclists doing this almost each trip. Abuse of electronic devices in the traffic
often results in road accidents. Further evidence of device use as a risk factor comes
from the studies in the Netherlands by de Waard in 2010 [2]. In this research, 3.4%
of the crash involved cyclists mentioned listening to music, 0.3% mentioned talking
on a hand-held mobile phone, and 0.2% mentioned texting a message [2].
These serious facts draw our interest to find a solution that is able to prevent cyclists
from becoming less aware of potential traffic dangers while still enjoying the music.
Therefore we would like to research opportunities for creating a system that would
enable users to hear music while cycling and get warnings about incoming traffic at
the same time.
1.1.
Initial problem statement
To what extent is it possible to warn cyclists who listen to music through
headphones about potentially dangerous situations in traffic?
5
2. Pre-analysis
In the preliminary analysis chapter, the target group will be specified and the
targeted traffic environment will be defined. Potential causes of cyclist accidents
involving music listening will be investigated, as well as different types of
headphones used by cyclists. State of the art shall contain research regarding
existing products that help to increase traffic awareness of primarily cyclists.
Furthermore, possible solutions of the warning system will be explored in regards to
both hardware and software. A Final Problem Statement will be made based on the
research of this chapter.
2.1.
Target group
According to the initial problem statement, the preliminary target group is all cyclists
using headphones and listening to music. The next step is to investigate whether
there is a pattern regarding these statistics in order to possibly narrowing down the
problem statement further, which might result in a better goal-oriented project. This
will be accomplished by researching the age and gender of cyclists who are involved
in traffic accidents while listening to music through headphones.
C. Goldenbeld’s made a research in the Netherlands in 2012, which included cyclists
using electronic portable devices on a road not only headphones but also handheld
devices [1]. He found out that the most frequent and indiscriminate portable device
users when cycling in the Netherlands are teens between 12-17 and young adults
between 18-34 (see Figure 2.1) [1]. The research also stated that there were no
distinctive differences between genders [1].
Figure 2.1 Percentages of cyclists by age group who at sometimes listen to music or use phone while
cycling [1].
6
The higher percentage of cyclists who use electronic devices in those age groups
mentioned above was more likely to be involved in traffic accidents where electronic
devices were a factor (see Figure 2.2 below).
Figure 2.2 Percentage of bicycle crashes which were preceded by portable electronic device use or
other factors [1].
Even though minors are more likely to be involved in traffic accidents, it would be
much more difficult to access this target group for the test purposes. Besides,
children and teenagers do not have as much experience in traffic as adults and their
attention abilities to concentrate are still developing, so that by age 20 they are able
to concentrate up to 15-20 min, but not before [3].
Therefore based on this data, the target group could be narrowed down to young
adults between 18 and 34 years old who listen to music through headphones while
cycling.
2.2.
Target traffic environment
According to statistics published by Organization for Economic Co-operation and
Development (OECD)1 more than 60% of all road fatalities in OECD countries occur
on rural roads. This number indicates that being in traffic on rural roads are not safer
than on urban roads, considering the fact that the traffic flow intensity is generally
higher in urban areas compared to rural areas, due to being more populated.
Specific research aimed for cyclists showed the same tendency in severe accident
occurrence when comparing urban and rural environments. A study by UK Transport
Research Laboratory found that the risk of being killed on rural roads is much higher
than on other roads, partially due to higher speed limits [4]. Figure 2.3 shows that
1
An international economics organization consisting mostly of European countries including Denmark.
7
nearly half of fatal cyclist crashes occurred in the UK from 2005-2007 happened on
rural roads, while the majority of the slightly injured cyclists was involved in accident
in urban areas.
Figure 2.3 Pedal cyclists casualties by road type 2005-2007 [4].
The study also suggests that in UK, the casualty rate for cyclists is greater in 10%
most deprived areas than in 10% least deprived areas [4]. This finding is in
accordance with the conclusion reached by Jacobsen (2003) [5] in his study about
inter alia walking and cycling in 47 Danish towns. The study stated that a driver is
less likely to collide with a person or cyclist when there are more people walking or
cycling.
When choosing the target traffic environment, road properties also have to be taken
into consideration. On many Danish rural roads, the width of the roads is relatively
narrow in comparison to city streets. Usually they do not offer separated bicycle
lanes, which can be a dangerous combination for cyclists in the presence of
bypassing vehicles. Furthermore, rural roads often follow the shape of the landscape,
meaning that there are many elevation changes and - or twist and turns. The
shortened distance of view due to these crests and blind corners is more likely to
add to the danger factor of the country roads.
From both the driver’s and the cyclist’s psychological point of view, traffic is
generally less expected on rural minor roads than in urban areas. Therefore when
traffic does occur, drivers and cyclists are less prepared and therefore more at risk.
Due to the discoveries made by comparing urban and rural traffic accident risks, this
project will focus on providing warning signals about incoming traffic for cyclists in a
rural environment.
8
2.3 Music listening and traffic awareness
3.4 % of cyclists who listened to music through headphones were involved in the
accidents, as mentioned in introduction to the report. Therefore the following
question arises: in which possible way listening to music might hinder the cyclists´
awareness of incoming traffic?
To be aware of what is happening in surrounding traffic, cyclists use two
perceptional senses: vision and hearing. However, without paying attention, the two
senses might fail to detect potentially dangerous situations.
Attention could be described as a selective process in the brain, which involves
several mechanisms that are able to restrict processing to a subject of things, places,
ideas or moments in time [6]. Cyclists are paying attention on the incoming traffic
primary using visual attention. Vision has the highest spatial resolution, meaning
that it allows observing everything very quickly in small details [7]. However vision
sense in traffic is used in combination with hearing in order to gather more
information about the situation on the road. Therefore the auditory sense is also
very important for the cyclist’s traffic safety. Attention is divided in case of other
extra activities apart from sustained focus on traffic. Cycling and listening to music at
the same time reduces the ability to respond to emergency situations, due to
decreased attention on traffic.
Dr. Richard Lichenstein in his study regarding the relation between headphones,
handheld device usage and pedestrian death or injury in USA during 2004-2011,
came up with two phenomena that are likely to be the contributors to the possible
association between headphone usage and pedestrian injury. They are - distraction
and sensory deprivation [8].
The first phenomenon - distraction has partially been caused by inattentional
blindness, which basically means “failure to perceive a stimulus that isn’t attended,
even if it is in full view” [7].
The second phenomenon described by Dr. Lichenstein’s study is sensory deprivation,
which is directly related to the defined target group. Sensory deprivation in terms of
hearing means “the inability to hear sounds emanating from the local surroundings”
[8]. Sensory hearing deprivation would occur when ears are blocked with some
devices such as hoods, earmuffs or headphones, or when surrounding sounds are
masked by the music. The same study showed that in 29% of cases where cyclists
have been involved in the traffic accidents a warning sound had been heard before
crash [8].
The two phenomena, divided attention, which reduces sustained attention on the
traffic, and sensory deprivation, which prevents from hearing traffic sounds, are
probable causes of cyclist crashes while wearing headphones.
9
Further down headphones are going to be described in general, as they are the main
cause of hearing deprivation for cyclists.
2.4.
Headphones
The target group has headphones on as it listens to music while cycling. Knowledge
of different types of headphones would be useful for the testing purposes where a
specific type of headphones will be chosen for the test subjects. As there are no facts
stating, which type of headphones the target group is using it would be appropriate
to get a general information how the headphones work and which types of
headphones exist.
In this section, different types of headphones are reviewed to find out if some of
them block incoming sounds as much as the others. Knowledge of different types of
headphones would be useful for the testing part where a specific type of
headphones will be chosen for the test subjects.
Headphones in general are a pair of loudspeakers that are placed very close to
human ears in order to transmit sound into the ear canals. Some of the headphones
are totally blocking the surrounding environment sounds, some of them are blocking
it only partly and some of them allow the users to hear surrounding sounds perfectly
well.
Headphones can be connected to MP3 players, TV, home theatres, CD players,
sound amplifiers etc. by the wire or they can be wireless.
There are different types of headphones that can be found nowadays. Users
determine the types of headphones that suite their requirements better. Some of
the headphones are made specifically for Apple devices; some are made for
Androids the others are of universal usage2.
1. In ear headphones are isolating the surrounding environment totally and do
not leave a possibility to hear any other sounds except for the sounds
produced from the device it is used with3.
Figure 2.3 In-ear headphones
2
3
http://www.earphonesolutions.com/earphones-by-type.html
http://www.earphonesolutions.com/in-ear-headphones.html
10
2. Earbuds are not very good mini headphones as they very often come with a
cheaper price. Most of them do not block the whole surround environment.
Sometime their bass system does not have the same quality as the other type
of headphones4. Some of the earbuds have a special purpose of noise
consolation, especially if the users need privacy like in the airplanes.
Figure 2.4 Earbuds
3. Circumaural headphones5 or full size headphones fully surround ears and
that is why they exclude almost all surrounding noises.
Figure 2.5 Circumaural headphones
4. Supra aural headphones are smaller than circumaural headphones but in
general they also block the surrounding noises.
4
5
http://www.earphonesolutions.com/earbuds.html
http://www.headphone.com/selection-guide/full-size-headphones.php
11
Figure 2.6 Supra aural headphones
Both circumaural and supra aural headphones can be with opened - and
closed back6. Opened have the purpose to open the back of the ear cups
giving the sound an opportunity to come out of the headphones making the
soundscape more spacious by creating the feeling of the distance to the
sound source. They do not isolate users totally from ambient noise, which
give a possibility to be aware of the surrounding environment. On the other
hand the closed type of the headphones have a smaller soundscape and give
the feeling that the sound comes from the users head, as the sound is
reflected back to the ears. They have acoustically sealed ear-cups.
Noise cancellation can be achieved by two ways:
a) Passive noise isolation (in ear headphones work like a plug)
b) Active noise isolation (cancelling constant noise only, like a plane engine,
but not people talking)7. These types of headphones usually are fully sized
headphones.8
It could be summarized that ear buds block the incoming sound partly, while in-ear,
circumaural and supra aural headphones block the sound almost totally. The last two
headphones can also have opened back of the ear cups, letting some sound in. This
means that there are some options for cyclists who want to enjoy the music while
cycling and still be safe. However, such headphones also have some drawbacks.
Most of them are expensive and sometimes produce a lower quality sounds because
of the sounds that leave the ear cup.
The blockage of surrounding sounds could contribute to cyclists’ unawareness of
dangers.
http://www.headphone.com/selection-guide/full-size-headphones.php
http://www.earphonesolutions.com/noise-cancelling-headphones.html
8 http://www.headphone.com/selection-guide/full-size-headphones.php
6
7
12
2.5.
State of the art
In this section different possibilities that already exist on the market and are relevant
to the problem will be analyzed. These products might give some inspiration for this
project.
2.5.1. Bone Conduction Headphone
Bone conduction headphones are specially designed headphones made for cyclists
who listen to music in traffic. As a variety of headphones are to be found in the
market, these headphones make it possible for
the users not to only listen to the music but also
to the surroundings. The Bone conduction
headphones works by using ones cheekbones as
the source to transfer the auditory signals to the
cochlea. By doing so, the whole ear will be
opened which means that the music will not be
the only sound which will be possible to listen to,
but the surrounding environment as well.
Bone conduction headphones are designed to be
placed around the upper ear with a neckband
around the neck. It can be turned off and on as
well as adjusting the volume. A clip is
implemented on the wire of the headphones, to
making it more manageable.
Figure 2.7 Bone Conduction Headphone
In an interview with Laura Laker9, conducted by
The Guardian, pros and cons were mentioned. In the interview, Laker reported that,
she got struck by how the headphones worked in regard to being able to hear the
surroundings while listening to music, but although in overall she found it positive,
unfortunately the sound quality produced by the headphones was negative; “The
bass was lost”, “tickling sensation on my cheekbones”, “oddly, yawning muted the
music momentarily” and “music was drowned out, even with the volume up” [9].
From this example, the ability of allowing cyclists to hear both music and a warning
signal or traffic sounds could be taken into considerations.
A participant who tested the Bone Conducted Headphones and gave a short review through
the interview with the guardian.co.uk [http://www.guardian.co.uk/profile/laura-laker]
9
13
2.5.2. Microphones helping cyclists
Through a study Timothy J. Landis in 1996 created an invention that reduces wind
noise and wind drags and makes it possible for cyclists to hear the incoming sounds
[10]. The invention is a pair of ear covers that is worn as a headband in association
with another kind of head protective gear like a helmet. It has an opening towards
the rear, where the sound is received. This gives the cyclist a possibility of hearing
the sounds coming from the back. In order to minimize the wind noise and wind
drags the ear covers are made aerodynamically and are divided into two
compartments: an inner and outer compartment, separated by a barrier. Speaker
and amplifier are further apart and hereby it reduces undesirable audio feedback.
The outer compartment consists of an acoustic receiver, an amplifier and a battery,
which is a power supply, while the inner compartment consists of earphones or
speakers. When the sound waves are received, the sound waves go through the
acoustic receiver where the sound waves get reflected to the microphone and
further amplified and transmitted to the earphone in the inner compartment.
This invention could be used in order to reduce wind noise and undesirable audio
feedback for the cyclist, and making it possible to listen and pay more attention to
the traffic especially behind the cyclist.
2.5.3. Sound-Based Traffic Incident Detection
The researchers from Mitsubishi electric research laboratories developed the system
that is able to accurately detect traffic anomalies such as accidents or near-accidents
in road intersections automatically [11]. Because the sounds emitted by the vehicle
accidents are fairly consistent, using them for accident detection is an easier task
than using video processing. Three different approaches have been employed to this
detection problem. The first one is a supervised learning methodology, where the
classifiers are trained to different traffic sounds such as vehicle crashes, brake and
tire squeals. When applied to the sound stream they are able to classify events. The
second methodology is unsupervised learning that detects anomalous sounds and
automatically isolates them from the recordings. Finally, a user interface was
developed. It allows a human operator to browse through the recordings and find
intersections that are the least secure and need more attention.
This is a perfect example of how sound analysis can be successfully used for
surveillance and safety purposes.
14
2.5.4. Traffic congestion estimation
In 2011 Nikhil Bhave and Preeti Rao from Indian Institute of Bombay conducted the
research on vehicle engine sound analysis and applied it to the estimation of traffic
congestion for intelligent transport systems [12]. Such systems are able to inform
drivers about congested traffic and can suggest alternative routes.
By extracting acoustic cues from traffic noise the system identifies vehicle type as
well as the state of motion. Such cues are expected to be prominent in the shorttime magnitude frequency spectrum extracted from the vehicle sound [12]. Traffic
noise database was created from 300 recorded samples, which had predominant
sound of the vehicle in traffic. These samples were pre-processed using spectral
subtraction in order to enhance spectral and temporal features against the
background noise. The pre-processed traffic audio signal is windowed with 180 ms
duration Hamming window10. A feature vector is extracted for each windowed
segment. Formant and Mel-frequency cepstral coefficient (MFCC) feature vectors
are then evaluated. The extracted formants forms the feature vector while MFCC
method labels the sounds to one of the three classes: two-wheeler, three-wheeler
and heavy vehicles. Using these two methods the system was able to accurately
detect up to 96% two-wheeler vehicles, 80% three-wheeler vehicles and 68% of
heavy vehicles. The reason why the detection of heavy vehicles was significantly less
accurate is because of the similar spectrographic structure to two-wheeler vehicles.
This suggests that vehicle sound detection and classification can be a challenging
task as well because of the similar acoustic cues.
2.5.
Possible solutions for a warning system
In this section, possible solutions for a warning system will be explored. Most audio
systems consist of two parts: input, where the sounds come in and output, where
modified sounds come out. The future prototype might also consist of two most
important parts. First part is audio input, which would receive all incoming sounds in
real-time and filter out what is unnecessary, leaving will only traffic sounds in our
case (e.g. car engine, horn, tire slip). Audio input could be achieved by using
microphones. To isolate relevant traffic sounds, audio analysis techniques e.g.
filtering could be utilized.
The second part of the warning system is an output, or in our case a feedback, which
is needed to provide cyclists with a warning. Possible methods for feedback will be
evaluated so that the most fitting method for the project could be selected.
10
https://ccrma.stanford.edu/~jos/sasp/Hamming_Window.html
15
2.5.1. System Input
For acquiring traffic sounds this project will use microphones. Nowadays, various
microphones can be found on the market. Each of the different microphones is used
for a specific purpose and therefore it is important to know what type of
microphone might be used in this project.
All the microphones have something in common. They convert a sound wave into
electric signal [13]. On the other hand, they differ a lot as well. This section will
describe different types of microphones available on the market in terms of sound
recording quality, directionality, size and price. This will let us choose the most
suitable microphone for this project.
In order to keep the system’s price reasonably low, microphone has to be quite
cheap, while still providing enough quality for capturing the traffic sound.
The size of the microphone also has a significance. Smaller microphone would be
preferred as it is more practical and easier to mount on a body. On rural roads there
are not so many cars coming from the sides. This suggests that the project should
focus on warning cyclists about incoming traffic from the back. Therefore a
microphone that can capture sounds from one direction mostly would be preferred.
Firstly, let’s compare microphones by their size. There are two basic types of
microphones by their size: handheld and wearable microphones.
Handheld Microphones
These microphones are well known for their versatility and
durability. Handheld microphones are used in variety of
settings, from musical performances to television interviews.
However, they are not the best choice for reproducing high
and low frequencies and require significant energy [14]. Their
price starts from around 10 dollars.
Figure 2.8 Shure SM58
Dynamic Microphone
Wearable Microphones
These are tiny little microphones, also referred to as lapel or lavalier microphones
that clip to one’s clothes and are usually used for television, theatre or interviews, in
order to allow hand-free operation. They are good for
capturing consistent audio level, as those microphones
do not move that much as handheld microphone [14].
Their price is similar to dynamic microphones, but
Figure 2.9 Sennheiser ME 2
omni-directional lavalier
microphone.
16
wearable microphones are much smaller and usually provide worse quality than
bigger microphones.
Directional Microphones
One more very important feature of a microphone is its directionality. The
directionality of a microphone means the direction from which a microphone is able
to pick up incoming sounds [15]. Sounds can come to a microphone from all
different directions, from front, rear or only sides, and a microphone will record
sounds based on its directionality [15]. There are four basic types of microphones
based on their directionality: omnidirectional, unidirectional, bidirectional and
cardioid microphones [15].
Omnidirectional microphones
These microphones can detect sounds from all directions equally well with good
sensitivity [15] (see figure below).
Figure 2.10 The directionality of omnidirectional microphone. It can pick up sounds
equally well from 0 to 360 degrees all around it [15].
Unidirectional microphones
These microphones can pick up sounds with good gain only from a particular side,
which means that a user has to speak at the correct side in order to get the best
recording sound (see figure below). That is why such microphones are mostly used
indoors. Unidirectional microphones could be useful outdoors only if pointed directly
towards the sound source.
17
Figure 2.11 Unidirectional Microphone Polar Response [15].
Bidirectional microphones
Another type is bidirectional microphones, that can pick up sounds equally well from
two opposing faces, that correspond to the front and rear of the microphone while
rejecting sounds from the sides. These microphones are perfect for face to face
interviews or capturing vocal or instrumental duet. However, mostly they are rather
expensive and huge microphones.
Figure 2.12 Bidirectional Microphone Polar Plot [15].
Cardioid microphones
This type of microphones can pick sounds from front and sides but poorly from back.
They are named so because the sensitivity pattern is roughly heart-shaped in nature.
18
Figure 2.13 Cardioid Microphone Polar Plot [15].
Noise interference with a microphone
When using microphones there is always a possibility that some unwanted noise will
be captured through it and interfere with the system. This can make the process of
detection of incoming traffic sounds more complicated. Since the target
environment of the project is the rural roads, natural ambient sounds such as wind
might be encountered and heavily interfere with the microphone.
Denmark is a windy country due to its flat geographical properties. Cycling at speed
might also cause air to collide with the microphone. Therefore wind noise could
become a problem for the warning system.
One approach for achieving a clean recording is by applying sound processing filters
which cut out unwanted frequencies. Another approach could be adding windresistant screens on a microphone.
Wind noise reduction for microphones is an often discussed subject. Outdoor
recordings require wind noise to be dampened or eliminated. The current types of
wind screen solutions for microphones are going to
be described below.
Foam cover is a common solution to reduce wind
noise for microphones. Made of disposable foam,
the covers are inexpensive and easily replaceable.
The cover consists of small open cells, used to
dampen vibration caused by the moving air.
However these cells might also catch dirt and dust
Figure 2.14 Foam windscreen.
very easily, which would worsen the detection of
the traffic sound and therefore unpractical to be used outdoors by itself.
19
Blimps (also known as Zeppelins) are large, hollow windscreens that surround
microphones and are used for outdoor recording. Blimps are effective for eliminating
wind noise as they can reduce it by up to 25 dB [16].
The blimp has a hollow cage structure outer frame.
Acoustically transparent materials are attached onto
the outer frame. The principle behind the design of
blimps is to create a space of still air around the
microphone. By isolating the microphone from noise
sources like this, friction and vibrations noises
caused by the wind hitting the cage are much less
likely to be transmitted. Though being effective in
wind noise reduction, the blimp is not suitable for
Figure 2.15 Blimp windscreen.
the project due to its large size.
The type of windscreens called windjammer, also
known as “furry”, is made of synthetic fur material
with long and soft hairs. The windscreen is acoustically
transparent, allowing good throughput for the sound
of interest. The hairs act as shock absorbers to any
wind turbulence hitting the windscreen. Windjammers
can be slipped on top of other types of windscreens
such as foam covers. This way, not only are the open
Figure 2.16 Windjammer
windscreen.
cells in foam covers protected from dirt and dust, the
synthetic fur cover can reduce wind noise by an
additional 10 dB [16] and is worth considering for the
project.
A performance comparison shows the effectiveness of a few types of windscreens in
dampening wind noises:
Figure 2.17 The orange colored areas indicate how much wind
noise is being captured by the microphone. The higher the x-axis
and color intensity, the wider frequency range and volume of the
wind noise are recorded [17].
20
According to the test the windjammers are most effective in damping the wind noise
compared to other covers and could be the best option for the project.
2.5.2. System Output
After receiving traffic sounds and detecting only car sounds, the warning system
should inform cyclists about the incoming traffic with a feedback. Therefore, it is
important to explore possible ways for providing the feedback. When choosing
feedback, there are several possibilities. It could be audio, visual or vibrotactile
feedback. In this section, description of these types of feedbacks will be given.
When talking about feedback it is necessary to remember that feedback is providing
a clear visibility for user interaction by sending back information about undertaken
or accomplished actions.
Another important detail when providing the feedback is to keep cyclist’s attention
on the road without distracting it too much. As was mentioned before in section 2.3
Music listening and traffic awareness, cyclists attention is divided already when they
hear music and have to pay attention on the incoming traffic. Therefore the
feedback should not distract any more attention. Three different feedback
approaches: visual, audio and vibrotactile are going to be analyzed below.
Visual feedback
As seen in the section 2.5 State of the Art, visual feedback is widely used in car
warning systems. However, in most cases it helps drivers to pay attention on the
road. Since there is already a lot of visual information for cyclists in traffic
environment, use of visual feedback would add extra workload to vision, which
might distract their attention from traffic.
Audio feedback
Audio feedback is particularly useful when there is no possibility to use a video
screen or non-visual dialogue with the user [18]. As an example to this is a GPS,
providing verbal directions to drivers. Audio feedback provides personalization to
the users engages them and creates emotional distraction.
In some systems, audio feedback is produced by earcons, brief, distinctive sounds.
Nevertheless, special care should be taken when dealing with such sounds, as use of
obtrusive sounds can become annoying very soon [19]. On the other hand, if well
designed, earcon sounds could distract cyclist’s attention from music and produce
clear feedback. Since the target group listens to music through headphones, another
21
option for audio feedback could be an alternation of music, which cyclists listen to. It
means that when such feedback would be triggered, the music would be alternated
by some audio effects, filters, etc. This gives plenty of room for designing pleasant
and interesting feedback.
Furthermore sounds are used in many human-computer interfaces. Sounds can
create affective and emotional reactions. Emotions are creating a dynamic process,
which consists of “several cognitive, neurophysiological, and motor components”
[20]. That is why sound feedback can be used on the same level as other feedbacks.
Emotional reactions could be influenced by the sounds [20]. The quality of the sound
has an influence on the users´ emotions and actions as well [20]. For example an
alarm sound could provide a very emotional feedback without distracting their visual
attention from the road. This signal could make them move in a safer zone of the
bike lane. It is the design of the sound that could make interaction successful and
warning efficient.
Vibrotactile feedback
The human body has tactile receptors that respond to stimulation such as pressure
or vibration [21]. These receptors are sensitive to changes in pressure over time and
that is called tactile vibration [21].
“Vibrotactile feedback stimulates human subcutaneous tissue. It’s been employed in
mobile phones, video console gamepads, and certain touch panels” [22]. Mobile
phones commonly use vibrotactile as feedback to a number of events, for instance,
call and messenger alert. As well as in the gaming industry, vibrotactile is broadly
adopted to a lot of game console devices, such as Wii U GamePad11, Xbox 360
controller12 or PlayStation Move motion controller13.
A study has shown using vibrotactile feedback on one’s body can sometimes be
irritating and annoying, especially when the frequencies are as high as 260Hz [23].
Vibrotactile feedback can be a good choice in many cases. It has been successfully
implemented in many applications. However, it is not very practical for warning
system as a sensor, which causes vibration, requires external platform in order to be
connected to a computer. Furthermore, special care has to be taken when using
vibrotactile sensor, as it can be irritating and cause negative user reaction.
http://www.nintendo.com/wiiu/buynow/
http://www.microsoft.com/hardware/en-us/p/xbox-360-controller-forwindows#details
13 http://ca.playstation.com/ps3/accessories/playstation-move-motion-controller-ps3.html
11
12
22
Choice of Feedback
In the preliminary analysis several topics that are relevant for solving our problem
statement were looked upon. Before defining the target group research had been
made on cyclist’s accidents and traffic conditions. Only then the target group and the
target environment had been defined, which is young adults from 18 to 34 years old
on the rural roads.
To complete the research about the target group causes of accidents among cyclists
were explored. This led the research to a conclusion that a combination of those
senses as attention, vision and hearing is crucial in traffic conditions.
State of the art reviewed many products that improves safety in traffic. Some
creative ideas and ways of thinking should be kept in mind as they could be useful
later in the project, when the ways of creating warning system for cyclists will be
analyzed.
It was decided that the warning system is supposed to have an input through a
microphone that would suite the project the most as well as give output in a form of
feedback. After studying several possibilities and types of feedback it has been
decided to use an audio feedback, as it is the most relevant to the project, when
taking budget, testing conditions and time frame into consideration.
By finishing preliminary analysis, the project has come to a final problem statement:
2.6.
Conclusion
In the preliminary analysis we came upon several topics that are relevant for solving
our problem statement. Before defining our target group we have made a research
on cyclist’s accidents and traffic conditions has been done. Only then we were able
to define our target group and target environment, which is young adults from 18 to
34 years old on the rural roads.
To complete the research about the target group causes of accidents among cyclists
were explored. This led the research to a conclusion that a combination of those
senses as attention, vision and hearing is crucial in traffic conditions.
State of the art reviewed many products that improves safety in traffic. Some
creative ideas and ways of thinking should be kept in mind as they could be useful
later in the project, when we analyze the ways of creating warning system for
cyclists.
23
It was decided that the warning system is supposed to have an input through a
microphone that would suite the project the most as well as give output in a form of
feedback. After studying several possibilities of types of feedback decision has been
taken to use a sound feedback as it is the most relevant to the project, when taking
budget, testing conditions and time frame into consideration.
By finishing the preliminary analysis, the project has come to a final problem
statement:
2.7.
Final Problem Statement
To what extent could a sound warning system increase cyclists’ awareness of
incoming traffic while listening to music through headphones?
24
3.0. Analysis
Based on the areas described in pre-analysis this chapter is going to analyze different
possibilities that can solve the final problem statement. In order to build the warning
system, several steps have to be undergone.
Firstly, the system has to acquire the sound through a microphone and send it to the
software that is capable of processing and analyzing the sound.
Secondly, after processing the sound, the system has to detect approaching car and
determine when to warn a cyclist.
Finally, this warning signal has to be sent through the headphones.
The following sections are going to research these steps in more details and
determine the requirements for a design of the warning system.
3.1.
Sound properties from road traffic
In order to know when to detect a car and trigger the warning signal, it is needed to
find out what traffic sounds are derived from. The traffic sound analysis will use two
important sound features - frequency and amplitude as a starting point of the
research.
Predominant sound sources in traffic are the engine and road–tire friction sounds. At
low speed for passenger cars the engine sound is dominant (<50 km/h) while at
higher speeds the road–tire sound is dominating [24]. At very high speeds,
aerodynamic noise generated around a car may become the main factor of sound
source [25]. However this kind of aerodynamic noises usually occur only on highways.
However this project will focus only on researching the frequencies and amplitude of
engine and road-tire friction sounds.
3.1.1.
Frequency domain
The engine noise frequency rises with engine revs per minute (RPM) - the higher the
RPM is, the higher the pitch is. Due to different types of cars being manufactured for
different purposes, engine RPM varies from one type to another. Sports car engines
have a higher frequency than engines in passenger cars and heavy vehicles. However,
the most common vehicles in traffic are passenger cars, trucks and busses. That is
why the project will primarily focus on these types of cars. The engines for these
types of vehicles produce noise in a relatively low frequency range, mainly between
50 to 100 Hz [26].
25
The tire-road friction noise is produced by interaction between the rolling tires and
the road. The vibrations and impacts as a result of this tire-road interaction generate
sounds at a frequency of around 1,000 Hz [26].
3.1.2.
Amplitude domain
Primary the amplitude of the traffic sounds dependent on the frequencies produced
by different types of vehicles and on their travelling speed. Heavy vehicles generate
more noise than lighter vehicles. Driving at a higher speed increases sound
amplitude compared to when driving at low speeds regardless vehicle type [24].
As shown on Figure 3.1, at frequencies of 50-100 Hz, which correspond to engine
noise, the amplitude is around 65-78 dB. The amplitude is 60-65 dB for tire friction
noise at a frequency of around 1,000 Hz, when the sound source is 10 meters away
[26]. However, when the sound source is 1 km away, the amplitude of engine sound
just slightly drops to 45-58 dB while the tire noise drops to 28-38 dB [26]. Figure 3.3
shows the correlation between distance, amplitude and frequency. As the distance
of sound source increases, the sound volume level (dB) of high frequencies
decreases more than at low frequencies. Simply put, low frequencies are able to
travel longer distances without weakening too much. The reason for this
phenomenon is due to the high frequency being absorbed by the atmosphere, and
the mid frequency being absorbed by the ground. As this project needs to detect
traffic sound before the vehicles arrives close to cyclists, it is there necessary to find
the amplitude levels at the certain frequencies ranges.
Figure 3.1 Typical spectrum of road traffic noise calculated at different distances from the
source.
26
3.1.3.
Target frequency and amplitude detection range
In the whole frequency spectrum, the only range interest for this project is
frequency at around 50-100 Hz and tire friction at around 1000 Hz. Therefore those
frequency ranges should be subtracted out of the whole spectrum while the rest
frequencies should be filtered out.
As Table 1 shows both engine and tire friction sounds. They are at a certain
frequency range with corresponding amplitudes. This would be very helpful to allow
the warning system to trigger the feedback accurately by controlling the sound
amplitude, as there might be some other sounds in the traffic, which have the similar
frequency ranges as engine and tire friction have. The warning system only triggers
the feedback when the amplitudes corresponding to frequency range of interest
reach certain levels found as in the Table 1. Apart from frequency control, the
amplitude control prevents noises in the similar frequency ranges as engine and tire
friction sounds from the interfering with the warning system.
Frequency
Amplitude
Engine noise
50-100Hz
<=78 dB and >= 45dB
Tire friction noise
Around 1000Hz.
<=68 dB and >=28dB
Table 1 Target traffic sound properties
However in real life these frequencies could differ significantly depending on the
weather, car engine, tires, speed, road, wind, etc. What is more, the system is
supposed to warn cyclists some time before a car will approach. It means that when
the system is supposed to detect a car, frequency and amplitude levels are going to
be different. This issue will be taken into consideration during the implementation
process, where different samples are going to be analyzed and specific sound
features for this project are going to be found.
27
3.2.
Sound computing techniques
This section is going to have a look at different sound computing and sound analysis
techniques that would help to implement the warning system and make use of the
frequencies and amplitude ranges defined in the previous section.
The audio warning feedback should probably be with a certain timber rather than a
pure tone. That’s where additive synthesis can be handy. In order to filter the traffic
frequency range, leaving only the frequencies of interest, subtractive synthesis might
be useful. Finally, description of different types of filters should help to choose the
most appropriate filter for this project.
3.2.1.
Additive synthesis
According to Fourier synthesis, one can create any periodic vibration by adding
sinusoids together with frequencies that are integer multiples of a fundamental
frequency at particular amplitude and phases [27]. That is why Fourier synthesis is
also known as additive synthesis where waveforms are added together.
There are several rules that must be followed when applying additive synthesis:
-
Only sinusoids can be combined
Frequency of all sinusoids must be harmonically related [27].
Figure 3.2 Additive synthesis.
Additive synthesis can be implemented by using several instruments or bits adding
them together. However it is the same as to add different frequencies together, as
described before.
3.2.2.
Subtractive synthesis
Subtractive synthesis is a sound synthesis method, in which partials of an audio
signal are attenuated by a filter. These techniques are in contrast to additive
synthesis where sinusoids are added together.
28
When applying subtractive synthesis frequencies are cut off from the sound. For
instance highpass filter cut low frequencies off while lowpass filter cut high
frequencies off. Frequencies could be removed in the beginning, middle or end of
the spectrum, which is a complete cycle of the constantly repeating vibration. Sound
waves could be different as well: sine, saw tooth, triangle, pulse or square.
3.2.3.
General knowledge about filters
Basically filters are devices that boost or attenuate regions of a sound spectrum [28].
That means that we can make any operation on a signal in order to achieve needed
result.
We can summate or subtract the signals,
depending on whether we want perform
an additive or subtractive synthesis.
After the operation a new waveform is
created with a different spectrum. The
main characteristics of the filters are
amplitude and frequency.
Each filter has its own characteristic
frequency response curve. These curves
for four basic types of filters are shown
in Figure 3.4: lowpass, highpass,
bandpass, bandreject or notch [28].
Figure 3.3 Frequency response curves for
different filters.
The subtractive synthesis could be
employed for the traffic sound detection to attenuate the noise while only passing
the vehicle sound on the certain frequencies. Then the signal that was passed
through the filter offers possibilities to manipulate it further. In the next section,
amplitude control will be looked upon.
29
3.3.
Envelope follower
When the filtered signal of the traffic sound is available, an envelope follower could
be used to gain control values from amplitude in a dynamical way. Basically, the idea
of envelope follower is to understand amplitude of audio signal in a dynamical way
over time, but not on a one by one sample level. The follower could generate an
overall shape of a sound in ongoing time (see Figure 3.5 below), and the overall
shape could be used to trigger some events depending on one’s expectation. For
instance the follower could be used to control the filtered traffic sound in order to
trigger the audio feedback.
Figure 3.5 Envelope following technique.
3.4.
MAX MSP
Max is a visual programming language and software for making interactive sound
and video applications. It has been widely used by various composers, performers,
software engineers and artists. MSP provides a set of audio processing tools that can
be used in Max software while Jitter provides tools for video and 3-D graphics14. It
has got a range of features to help people create media-rich interactive programs in
a very structural and graphical way [29].
First of all, Max allows people to build sound programs in a fluid and visual process.
Max programs are created by organizing and connecting “boxes” together. These
programs are called patches, and boxes are objects. Each of these objects is a selfcontained program having inlets and outlets, which can receive input and generate
output. People can hear or see the results in real time once they made some
changes in a patch [29].
Secondly, Max offers a lot of sound computing functions allowing people expand
their own ideas to create sound and music. These functions include “samplemanipulation, synthesis tools, high-quality filters, spectral processing, real-time
14
http://store.cycling74.com/s.nl/sc.2/category.2/.f
30
recording and playback.” As well as it allows users to “process, resample, slice, and
modulate everything in any combination” [29].
3.5.
Audio feedback
After consideration on different possible feedback types in pre-analysis section 2.5.2,
audio feedback was chosen as the most appropriate for this project. Such feedback
could be realized through the cyclist’s headphones and it would not require usage of
sensors and microcontroller as well as would not distract cyclist’s visual attention
from the road.
A driving alert system in motorized vehicles typically warns the driver by playing a
warning message, a series of high urgency tones similar to those from an alarm clock,
or both. The effectiveness of auditory warning signals and messages in an in-vehicle
information system is dependent on the level of both perceived urgency and
annoyance for the driver. The perceived urgency could affect how quickly a driver
reacts to the warnings, and annoyance might have an effect on whether or not the
user ignores the warning or simply turns off the system [30]. The relation between
perceived urgency and annoyance – as well as the factors affecting them when used
in auditory alerts and warning messages will be explored to find inspirations for
designing warning system audio feedback.
Audio feedback is also commonly used in other areas such as computer software
interfaces, featuring a variety of sounds, which are played in response to user inputs.
These sounds are called earcons. Even though these sounds are not used to indicate
dangerous situations in real life, the concept behind their designs could prove to be
useful for the project.
Auditory alerts
Vehicle warning system alerts
Alert used in vehicle warning systems is a pulse of sounds that users interpret as
warnings to a dangerous situation.
The sonic urgency is a perceived urgency of an auditory alert. It should correspond to
a degree of danger. This principle is called urgency mapping and is important in
auditory alert design.
The parameters that affect sonic urgency include inter-pulse speed (IPS). IPS is the
time interval between the end of a sound pulse and the beginning of the next pulse.
IPS is related to density. Density is the percentage of sound presence during each
31
burst and speed is the time interval from the beginning of one burst to the next one.
Figure 3.6 The effect of sound parameters on perceived annoyance and urgency [30].
Figure 3.6 demonstrates the effect of both density and speed on perceived urgency
and annoyance. The higher the density and speed are, the more urgency and
annoyance is perceived by users, as those parameters correlate. However, density
affects more than speed. While the incline in urgency and annoyance are almost
identical to density, speed seems to increase urgency, though it does not annoy
users too much.
These results suggest that auditory parameters do not affect urgency and annoyance
equally. Furthermore by manipulation of sound parameters such as density and
speed, it is possible to create a balance for urgency and annoyance levels. Besides
this, a relatively high-perceived urgency alert might be created, which could also
minimize annoyance.
The context in which the alert is played also affects the users´ annoyance level. Test
participants tended to rate highly urgent sounds as more appropriate when they
were presented in the descriptive context of highly urgent driving scenarios.
Earcons
Earcons are featured auditory alerts in the computer operating system or software.
They function as audio cues for the users. Earcons can be a simple beep or more
often, a customizable alert with pitch and rhythmic changes. They can also contain
some other audio effects, heard in newer Microsoft Windows operating systems
when emptying the trash bin, for example, or plugging in a USB device.
Due to earcons’ ability to have a complex structure, they could be more
distinguishable for the users than a simple beep, making it easier to associate the
sound with a certain event. If using earcons for providing audio feedback in the
sound warning system, cyclists will immediately understand that a car is approaching.
32
Some of the parameters that could be used to make the earcons more
extinguishable are for example timbre, pitch, rhythm, tempo and intensity. The use
of timbre with multiple harmonics instead of having pure sine waves in a warning
tone can help increase perception and reduce masking of the alert sound. Changes in
pitch are effective to increase alert sound perception when combined with use of
other parameters, but it is less effective if used by itself. Rhythm, duration and
tempo changes are more noticeable to the user even if no other parameters are
used. The intensity of the tones should not be consistent throughout playback of an
earcon either. The guideline suggests that an earcon should be created with the first
note being slightly louder and the last note should be quieter but slightly longer.
33
4.0. Design
4.1.
Requirements
The requirements for the microphone, the overall functionality of the warning
system and the audio feedback for design of the warning system will be established
and described in this section.
4.1.1.
Functional requirements
Functional requirements show what the system can do.

The warning system should be able to detect incoming traffic and give a
feedback before any vehicle approaches the cyclists.
4.1.2.
Non-functional requirements
Non-functional requirements represent the constraints in the process of both
usage and development of the prototype.







Requirements for the microphone:
The microphone should be able to detect sounds only from one direction
(unidirectional).
Frequency response of the microphone should be at least in the range of
50Hz – 1000Hz.
The microphone should belong to Lavalier microphones type.
The microphone should have a windjammer cover in order to eliminate wind
noise.
Feedback Requirements:
The total length of the audio feedback should not exceed 3 seconds. Even
though the cyclist needs to be warned of incoming traffic, they will prefer
continuing listening to music as soon as possible. Therefore the audio
feedback should be short and efficient.
The audio feedback should make use of alerts/earcons, due to of their ability
to attract attention. Verbal messages should not be used, since the
functionality of the warning system is simple and verbal information are
unnecessary for the user.
34



The audio feedback should make use of alerts/earcons, due to of their ability
to attract attention.
The audio feedback should be able to get user’s attention immediately.
The perceived urgency of the audio feedback has to match the situation
urgency level, so that the warning urgency level is contextually appropriate in
order to reduce annoyance. The music’s volume level should be turned down
30% when the alert sound is triggered to increase sound/noise ratio in favor
of the audio feedback.
4.1.3.

The prototype is designed for the countryside roads without bicycle lines.
4.1.4.

Environment requirements
Technical requirements
Use of MAX MSP to compute incoming traffic sounds.
4.2.
Short description of the system
The design of the warning system is represented on Figure 4.1. A microphone is
placed on the back of a bicycle. It acquires the surrounding sounds and send them to
Max MSP. When the frequencies and amplitude of an incoming car are detected, an
audio feedback is triggered through user’s headphones.
Figure 4.1 Short description of the system.
35
4.2.
Overall structure of the system
In this chapter an overall structure of the system including hardware and software is
going to be described. As the starting point, the system is supposed to acquire traffic
sounds through the microphone. Secondly, the traffic sounds are going to be sent to
Max MSP software where low pass filter will be applied on the whole frequency
spectrum and leave only the range of vehicle frequencies. Thirdly, envelope follower
is going to be used to ensure that these frequencies have specific amplitude which is
typical to vehicles in the rural areas. Finally, when frequencies and amplitude meet
the target range and threshold respectively, the system triggers the feedback. An
overall structure of the system is shown in the figure below.
Figure 4.2 Structure of the system.
4.2.1. Input
Requirements for the microphone that would suit this project’s purpose were
established in the section 4.1 Requirements. However for the testing purpose it was
decided to simulate the traffic sounds through the soundscape made in Max MSP.
This soundscape would consists of different vehicle samples as well as background
noises. For this reason, specific microphone will not be chosen neither tested.
4.2.2. Filtering and envelope follower
As discussed in section 3.2 Sound computing techniques, filter could be used to
eliminate the noises while only allowing the certain range frequencies (see 3.1.3) of
vehicle sound to pass, so that the signal entering envelope follower is mostly traffic
sound. The envelope follower is needed to blur the small details of the original
sound wave, and turn it into a smooth overall shape over time. By controlling the
smooth shape of the traffic sound signal, the feedback could be triggered on an early
stage of the envelope.
36
4.3.
Sound feedback
The context in which the audio warning is played is when a car approaches a cyclist.
Since the objective of the warning system is only to make users aware that the car is
approaching, and since the approaching car is presumably still distant when the
warning is triggered, the urgency of the situation is very low. Therefore the design of
the auditory alert aims to produce a warning that gives users low to medium
perceived urgency, and a low annoyance level.
According to the analysis 3.5, when the alert sound is consisted of a single tone that
is repeatedly played, the alert from the sound warning system should have a
relatively low density, and medium to high speed – as the increase of annoyance is
higher with increase of density than speed. However repetitiveness itself is a source
of annoyance, therefore the alert sound for the warning system will be a multiplepitch alert that will only be played once. The difference in pitch of the tones should
be relatively small, because large changes of pitch in a short time period might
create annoyance.
As also mentioned in analysis section 3.5, a higher S/N ratio is needed to make the
audio alert stand out in comparison to the music that is also playing through the
headphones. Instead of playing the alert sound so loud that it eventually it can be
clearly heard over the music, the volume of the music will be gradually lowered to 30%
and gradually increased back to its original level when the playback is finished. This
could be achieved by manipulating the music through MAX software.
In order to make the alert sound more distinguishable for the user and also more
pleasant, the design will follow the findings from analysis about guidelines for
making earcons 3.5, and focus on manipulating the following parameters of the alert
sound: timbre, pitch, rhythm/tempo and intensity. By adding harmonics to a
fundamental frequency, the timbre of the sound will change, making the alert sound
more three dimensional. A frequency pass filter should be used in order for
harmonics to get different amplitude. Pitch alteration could be accomplished by
creating different fundamental frequencies and thus different tones, or adding audio
effects such as tremolo to the alert sound. Finally, the first tone of the alert sound
should be slightly louder than the others, while the last tone should be prolonged
and gradually fading out.
37
5. Implementation
This chapter describes how to realize the design using sound computing technology.
As discussed in the Design section 4.2.2, Max MSP will be used to filter the frequency
range of vehicle sounds out of the whole spectrum, smooth this sound signal by
using envelope follower, and trigger sound feedback based on a certain level of
amplitude. During the implementation, sound analysis in frequency and time
domains is conducted first. Thereafter additive and subtractive synthesis, and
enveloping sound computing techniques are employed to accomplish the design
decisions.
5.1.
Sound source delimitation
According to the design of the warning system, the traffic sounds are supposed to be
transmitted from an omnidirectional microphone to Max. However, the initial focus
of the sound warning system prototype is to detect car sounds without too much
noise interference. In order to keep the external programming environment stable
and consistent, the sound sources are delimitated from live microphone recording to
three different samples of commonly heard vehicles’ sounds in rural area (passenger
car, bus and truck 3.1.1). This benefits the project by keeping the focus on sound
computing techniques and removing some issues, which might affect the usage of
the prototype in real world. Such issues could be wind noise, different modules of
other vehicles, and some sounds from nature remain fluctuated energies in the
filtered frequency rang across the time domain, which might be extremely hard to
be taken into consideration while programing in this project. Therefore the
prototype will use the soundscape made in Max MSP as a sound source.
38
5.2.
Development
Figure 5.1 below describes the flowchart of the implementation. Traffic sounds are
simulated through the soundscape in Max. Then the low pass filter is applied, which
passes through only the vehicle sound frequencies. Envelope follower analyzes the
amplitude of these frequencies and if the amplitude is increasing and reaches a
certain level it sends a bang, which triggers the feedback.
Figure 5.1 System flowchart.
5.2.1. Sound file play
The first step to do in the Max patch is to play traffic sound by using sfplay~ object
(see Figure 5.1). The left inlet of sfplay~ connects to an “open” message with the
name of the sound file which is going to be played, and a Toggle object with an
output of 1 or 0 to start or stop playing. The right inlet of
sfplay~ is used for controlling the speed of sound playing18.
Every sound sample in the soundscape is triggered using this
method.
Figure 5.2 Sfplay~
object.
18
http://cycling74.com/docs/max5/refpages/msp-ref/sfplay~.html
39
5.3.
Soundscape
Soundscape is a set of sounds arising from a specific environment, which is the rural
environment in the case of this project. In order to simulate the rural environment
soundscape, three different vehicles sound samples were used: car, bus and truck19.
Besides these samples, background noise sound samples such as bicycle bells, birds
and general noise were used. Wind noise couldn’t be used as it aggravates the
detection of the vehicles and sometimes causes false behavior of the system.
Soundscape is implemented by using a line object to generate timed ramp, which is
set to 2 minutes and 10
seconds (see figure on the
left). Sounds are played by
using if statement. When a
certain period of time have
passed, specific sound sample
is triggered. For instance,
when 5 seconds have passed,
metro object gets a bang and
begins playing background
noises at random time
intervals and order.
Figure 5.3 Part of the soundscape implementation. A car
sample is triggered after 20, 56 and 117 seconds.
Usual personal computer’s sound card is not able to play music through more than
one external device without using an external sound card whereas this project needs
to play music and audio feedback through headphones, and soundscape through
speakers. Therefore a solution is needed to divide these sounds to two output
devices. For this reason, two computers containing the same soundscape are used.
According to within subject testing, one participant has to test two conditions in this
project (see 7.4). In order to avoid the participants having learned the pattern of the
only one soundscape, another soundscape is needed to randomize the order and
interval of the vehicles’ sounds. So that one participant would never experience the
same soundscape twice. When one of two soundscapes is triggered, a respective
message is sent to the second computer to play the same soundscape at the same
time through the speakers (see Figure 5.4). This is done with a help of Maxhole
object. Maxhole allows sending and receiving data wirelessly from other computers
running on a local network. Everything that goes into Maxhole object is received by
19
Downloaded from http://www.freesound.org/.
40
the same object on the other computer. This is a very simple and efficient way to
start two soundscapes at the same time.
Figure 5.4 Implementation of wireless communication between two computers running on
the local network. Sender on the left, receiver on the right.
5.3.1. Lowpass filter
According to Figure 5.1, the traffic sound is filtered before going further to envelope
follower. As discussed in Analysis and Design, the low frequencies of vehicle sound at
the range around 100 Hz should be passed by using subtractive synthesis technique.
Onepole~ object is a low pass filter, which has a slowly
attenuated cutoff20. A couple of onepole~ objects are
employed to get this frequency range. As Figure 5.5 shows
the first object has a cutoff at 40 Hz, the original signal minus
this low passed signal is actually the rest frequencies in the
spectrum, which go to another onepole~ with a low pass
cutoff at 150 Hz. After the calculation, the finally output is
somewhere between 40 Hz and 150Hz.
Figure 5.5
Onepole~ lowpass
filter.
20
http://cycling74.com/docs/max5/refpages/msp-ref/onepole~.html
41
5.4.3. Envelope follower
Before the signal that passed through the filter goes to the envelope, it firstly has to
be converted to only positive numbers. Therefore the signal to the abs~ object,
which takes any signals and outputs only rectified wave- the non-negative values21. It
is because this envelop drawing needs positive numbers.
Rampsmooth~ object is to smooth an input signal through the two arguments of this
object defined as ramp-up and ramp-down samples. This object provides a hill-like
curve with ramp-up on its left side and ramp-down on its right side. In order to
assign those samples to rampsmooth~, another object mstosamps~ is used to
convert milliseconds to samples. However, the number of samples converted
completely depends on the setting of sampling rate of Max, which can be seen on
Audio Status (See Figure 5.7). Those samples are visualized on a multislider as
continuous little black hills (See Error! Reference source not found.). This project
uses 1000 milliseconds that equals to 44100 samples, which means each time the
input signal changes, a linear ramp bridges to this value with a mount of samples
depending on the sampling rate.
Moreover the snapshot~ is used to convert the signal values to numbers, as in order
to draw the envelope in multislider, the input mush be numbers. 1ms interval is used
for snapshot~, which means that this object converts one number from the input
signal every millisecond. By doing this, the object provides a smooth curved hill,
which is easy to recognize while the vehicles are approaching.
21
http://cycling74.com/docs/max5/refpages/msp-ref/abs~.html
42
Figure 5.6 Implementation of the envelope follower.
Figure 5.7 Audio status.
43
5.4.
Feedback
The output number from envelope follower is a fluctuated number going up and
down constantly. As discussed in Design, the feedback should be triggered when the
vehicle just starts approaching to the cyclist. Apparently the triggering point is on the
left foot of the hill.
The triggering point should meet two circumstances in
order to trigger the feedback. First of all, the
amplitude should be going up while it is triggered.
Secondly, the amplitude should be on a certain levelhill’s left foot. To get the advantages of the amplitude
going up and down, the change object is employed. It
outputs a message with number 0 if the amplitude is
decreasing and a message with number 1 if it is
increasing (see Error! Reference source not found. on
the left). The right inlet of route object gets the 0 or 1 as its selector, which is going
to compare with the route number. If the left input matches the selector, the left
output gives a bang. The left input is determined by a switch object with three inputs
referring to three amplitude thresholds. As seen in Figure 5.9 these thresholds are
different depending on the vehicle’s type (passenger car, bus and truck). Setting
different amplitude thresholds allows to trigger the feedback as soon as the vehicle’s
sample begins.
Figure 5.8 Change object.
Figure 5.9 Switch object and different amplitude thresholds.
The audio feedback is created using additive and subtractive synthesizes techniques,
and envelopes. Six harmonic signals are put together, which afterwards goes
44
through a band pass filter and an envelope. The reason for using band pass filter is
that the very low of the original signal give some clipping sound, but they could not
be completely cancelled, since they contribute some timbres to the audio feedback.
That is why the band pass filter is used to attenuate the clipping sound.
After the system was implemented, it became possible to test the hypotheses that
are defined in the following chapter and prove them wrong or right.
6. Methods
In this chapter of the report methods are going to be described, which will be used
for testing the final problem statement. DECIDE framework is a helpful to navigate
through the test procedure.
The first step in test according to DECIDE framework is to set up a main goal, which is
to find out if the prototype could help cyclists be more aware of incoming traffic in
case of hearing deprivation.
Experimental design approach would help to find out the cause of the relation
between two set of data, which is exactly what the test is supposed to do.
To be able to accomplish the goal, the project has to set up hypothesizes according
to our expectation. When setting up hypothesis independent and dependent
variables have to be bared in mind. As well as one tail and two tails conditions have
to take into consideration when establishing hypothesis, because this is crucial for
the later calculation and interpretation of t test. In order to interpret the t test it is
also necessary to compare the results through paired t test.
Qualitative data will be collected as well through questionnaire after the testing of
each participant. Its purpose is to collect subjects´ opinion on the feedback of the
sound warning system. Subjects´ opinions about sound feedback could be applied in
the future for any kind of improvements of the sound warning system.
45
6.1.
T-test
The T-test22 is a classical statistical method, which is commonly used for detecting
difference between average scores of two groups.
The first one-tailed T-test will help to answer how many cars the subjects could
detect with and without the sound warning system, which will prove or disprove the
hypothesis, if the prototype could help the subjects to detect more cars rather than
those who don’t use the prototype.
Controlled group will be expected to detect fewer cars than treatment group. That is
why one-tailed T-test in the positive direction23 is going to be used for the
experiment [31], as it is expected that the mean of the treatment group is greater
than controlled group. This test will disregard the possibility of a relationship to the
negative direction. Reason for this is that the system is expected to detect more cars
than the subjects who don’t use the prototype.
In the second two-tailed T-test it would be interesting to find out how the reaction
time of the subjects differ in case of the sound warning system usage. The second
test can show the reaction time difference of the prototype usage by the subjects.
The independent variable is: feedback (if the test is with or without it).
The dependent variable is: reaction time (of the subjects).
Paired t-test
Paired t-test is going to be conducted on the same subjects, which will save the time
and help to compare the results in a more efficient way, as one population mean
sample is going to be paired and compared to the other sample[32], as treatment
and controlled groups are presented. It is not necessary that a humanly built system
will give better results. Although in case of the project subjects´ hearing sense are
deprived, which gives a perfect opportunity to build a system providing warnings
quicker and be useful.
Designing the experiment
This research is going to be based on a true experiment and validate only one
testable research hypothesis [31].
English chemist William Sealy Gosset developed student test, when he worked for the Guinness brewery
in Dublin, Ireland, under the restriction in the contract that did not give him any possibility to publish his
work. research. Therefore, the publication of his articles on the t-test Gosset did in 1908 in "Biometrics"
under the pseudonym "Student". The simplicity of calculating Student t-test, as well as its presence in most
statistical packages and programs has led to be widely used around the world.
22
23
http://www.psychstat.missouristate.edu/introbook/sbk25m.htm
46
Two groups will be involved in the experiment: a treatment group and a controlled
group. The first group will be used to test a warning system. The second group will
be involved to prove a significant difference of not using the sound warning system.
As the test would like to exclude individual differences, a larger number of
participants are needed. To escape a larger amount of the participants for T-test, it
would be preferred to apply within – group design approach.
However, within group has potential bias- learning effective. That is why two
random soundscapes were created for two subject groups.
Random sampling is going to be applied to the choice of testing subjects, as it is the
most commonly used sampling method for true experiment. Another very important
factor concerning random sampling is that it would be more practical to apply
convenient sampling, as the test subjects are going to be chosen from the one’s
availability [33]. And the last problem of within-group design is escaping fatigue of
the test subjects [31], which could be done by shortening the testing time [31].
Participants
Test subjects will be recruited from Aalborg University, M.C. Meyers 15. There will
be 30 participants in a whole. Participants will be picked up randomly. However they
have to be between 18-34 years old and hear the music through headphones while
cycling in traffic.
Before testing the participants will be informed about the procedure of testing,
about goals and purpose and asked their permission to use the results in the further
scientific research. This form can be found on the exam CD.
47
7. Testing
7.1. Test objectives
The data that testing of the prototype product collects should be able to answer
whether or not - and to what extent the prototype could increase cyclists’ awareness
of incoming traffic while listening to music through headphones. However, the word
“awareness” is too general to design a test and the project should specify the exact
arguments that will be used to indicate an increase or decrease in awareness.
Therefore it is determined, that the test will have two specific objectives:
1. Time needed to detect a vehicle.
In many studies regarding traffic awareness for drivers, reaction time is used
as the gauge for how aware the driver is about the surrounding environment.
The reaction times measured in those studies are for example the time
interval from an obstacle appearing on the road until the driver presses the
brake pedal. For the cyclist warning system prototype, the test could be
designed similarly to a driver reaction experiment. The “time needed to
detect a vehicle” could be interval from the time playback start for a car
sound sample in the Max soundscape, until the user recognizes the incoming
car sound and takes action by pressing a button. If the reaction time is
shortened by using the cyclist warning system, it could be argued that the
system helped to increase awareness.
2. Incoming traffic identification ability measurement.
48
It is questionable whether cyclists are able to identify incoming cars in time
and take actions such as steer towards the side of the road when necessary.
Therefore another objective of the prototype product test is to examine the
presumable increase in the number of cars that each participant is able to
recognize and respond before the car is alongside. Since the test is not taking
place in a real life situation, the theoretical point where the car is alongside
the test participants would be the peak in amplitude of the car sound
samples that are played to the participants. When not using the warning
system, a participant presses the button when he/she hears the car samples;
when using the system, the participant presses the button when given the
audio feedback. If a test participant presses the button before the amplitude
peaking in a sound sample, then it means that they have recognized the car
early enough to be able to respond with an action.
The ways of collecting and analyzing testing data are similar for the two test
objectives. For time measurement, the results analysis compares calculated time
interval between start of the car sample playback and button press from test
participants when using and not using the warning system. For incoming traffic
identification ability measurement, the time of amplitude peak in of the car sound
samples are compared to the time of corresponding button presses; missed
identification of car sample by test participants will be treated as recognizing too
late.
The calculated results will differ from each other, one set of the results will show
changes in reaction time, and the other set of results will indicate changes in the
ability to identify incoming cars and take action before the cars reaches alongside.
The first set of results will be calculated in time/seconds, and the second set
calculated in number of cars, but in the end, analysis of both sets of results will
indicate whether the warning system could potentially increase traffic awareness for
cyclists who are wearing headphones.
According to traffic sound properties research in analysis 3.1.3, the sound amplitude
of traffic 10 meters away is around 78 dB, which was further confirmed prior to
testing by us using a sound level meter standing beside a rural road to measure
amplitude of cars passing by. The measurements from the sound level meter read
roughly between 72 dB to 84 dB. Both speakers are therefore adjusted to play the
car sound samples to the test participants at about 78 dB to simulate traffic sound
loudness in real life.
7.2. Environmental settings
Controlled testing environment was set up in a sound laboratory in the Aalborg
University Copenhagen. The test needed to be conducted in this laboratory to isolate
49
the test from potential outside noises. Sound laboratory has the best conditions as
the walls contain sound isolation materials to avoid sound wave reflections from the
walls as well as to avoid unnecessary noises.
Figure 7.1 Testing environment.
Equipment for testing
-
Training gym bicycle;
IPhone chronometer app;
Circumaural headphones without total sound isolation;
2 Computers (1 for plying the traffic sounds through speakers, 1 for playing
the music and the feedback);
2 Loudspeakers;
Since the testing took place in a sound lab and the purpose of testing was to figure
out when participants reacted to the feedback it has been decided that the
equipment used for the testing should be slightly different from what has actual
been designed for the prototype.
Instead of a real bicycle, training bicycle from a gym was used.
In order to know when the participant reacted to the audio feedback an IPhone app
was used, where the participants had to respond by pressing a button. Each time the
participants pressed the button on the app, the time was registered. The IPhone was
placed closed to the cycle’s handlebar so it would be easier and quicker for the
participant to press the button.
50
Besides, two computers were used: one was used to play the soundscape through
speakers and the other to play the music and produce feedback through headphones.
In order to play the traffic sounds two loudspeakers were used where one was
placed in front of the bike and the other was placed behind the bike.
Sony-MDR xB500 headphones26 were chosen for this test. They belong to
circumaural, closed back headphones type. They are capable of producing high
quality sounds in extremely wide frequency range: 4 – 24,000 Hz as well as very loud
sound output reaching up to 104dB.
Even through, as described in section 2.3 circumaural headphones usually block the
most of the incoming external sounds, practical experience using these headphones
outside in the traffic revealed that traffic sounds are still audible depending on the
music volume.
Instructions
Test subjects were tested two times, according to the design of the experiment.
1. Controlled group
The first group listened to the music through the headphones while the
traffic sounds were played through the loudspeakers. This group did not get
any feedback from the sound system. Instead the participants had to detect
the potential danger from the approaching traffic by simply trying to hear the
surrounding sounds. As soon as they heard the sound of an incoming vehicle
they were asked to press the button (measure application on the IPhone),
registering the time.
2. Treatment group
The second group was using the same equipment: headphones with music
playing and the traffic sounds playing through loudspeakers. However, in this
test participants got the warning signal when a vehicle was detected through
the headphones, which they had to react to. Their reaction time was
registered the same way as the first group, by pressing a button. The only
difference was that they had to respond as soon as they heard the warning
signal instead of the vehicle sound from surroundings.
26
http://www.sony.co.uk/product/extra-bass-dj-headphones/mdrxb500l.ae
51
7.3.
Pilot testing
A pilot test is conducted before the actual test, in order to avoid problems later on
when conducting the T-test. Often problems might occur when collecting the data,
giving instructions to the participants, setting the environment, choosing methods or
designing the test itself.
There were 10 subjects that were chosen randomly for the pilot test. Each subject
was asked to give a feedback about the sound warning system, testing procedure
and audio feedback signal. Several technical errors occurred during the testing
procedure. Those errors were corrected straight after they were detected. General
improvements of the sound warning system was made after the pilot test as well.
By comparing two types of most commonly used headphones- earbuds and Supra
aural headphones, we found out that due to the difference of participants´ pinna,
sometimes the earbuds fell off the ear while subjects were cycling. As well as people
using Supra aural could still hear outside sound depending on music loudness and it
was more comfortable for the participants (see 7.2). Therefore it was decided to use
supra aural headphones. Furthermore it would contribute the T- testing with more
reliable results.
During the pilot test the sound volume level had been tested as well. The reason for
this was to find out an optimal sound volume that was the same as the subject used
to hear on. 10 subjects listen to music through headphone while giving a gesture to
increase or decrease the volume. The average volume was 20% among these
subjects.
Because MacBook Pro was used in the testing the audio output was adjusted in the
sound preferences settings. Down below it will be referred to as the percentage of
the audio output of MacBook Pro.
The participants were irritated about constant interruption of the music when the
feedback was provided. It had been decided that the sound volume level of the
music should not be lowered to 0% but to the level of 30% in Max sound level
instead.
Above all the participants were unsatisfied with the given instructions as they were
unclear. That is why it was decided to leave one person in the room together with
the participants and changed the instructions for the T-test. Beside this person
would be giving a signal to a participant when the test would be finished. If
52
participants have pressed the button unintentionally, he was asked to raise a hand
so that the tester would be able to take the note of the mistake.
As the instructions about feedback and its sound were confusing it was decided to
let the participants hear feedback first before starting the actual test.
Furthermore several technical errors appeared during testing. Software didn’t work
optimal throughout the test, as MAX crashed several times that is why the particular
computer that was playing files from Max was changed to another one, which did
not have problems with this program.
7.4.
Hypotheses test
For the final hypotheses test 30 participants have been picked randomly. There was
21 male and 9 females with an average age of 21 years old. Null and alternative
hypotheses for two tests are defined below.
First t-test (one tail)
Null hypothesis: The average number of vehicles detected in time without prototype
is greater or equal to the average number with prototype.
Alternative hypothesis: The average number of vehicles detected in time without
prototype is less than the average number in with prototype.
Second t-test (two tails)
Null hypothesis: There is no significant difference in the time when the vehicles are
detected between with and without the prototype conditions.
Alternative hypothesis: There is significant difference in the time when the vehicles
are detected on time between with and without the prototype conditions.
Data preparation
The actual data collection provides a set of primitive data, which maintains some
errors and inconsistencies. So that the primitive data should be deciphered,
formatted and organized before it could be used for statistical calculation. As
described in the test design, a chronometer is used for the participants to trigger a
feedback as soon as they get either an audio feedback through headphone or the
vehicle approaching sound through the speakers. So that the data is in an ordered
list in a multi-time format looking like “hh:mm:ss:msms”.
However, in order to test the first null hypothesis, the only interesting element of
the data is how many times the participants pressed the chronometer before the
vehicles passed by, which is considered valid button press. By analyzing each of the
53
vehicle sound waveforms, the time correspond to the waveform’s peaks are
recognized, which enables us to eliminate the invalid button press. For the without
prototype condition, a number of invalid button presses are recognized, but none
form with prototype condition.
In order to utilize the data to do the second t-test, which has to do with the time
difference of vehicles approach between with and without prototype conditions, the
original data need to convert to a single time unite- second. In form of single time
unite the data could be put directly into some statistical analysis programs to
generate the results.
Number of
participants
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Mean
Ratio
Standard
deviation
1st t test
With feedback
9
9
9
8
9
9
9
8
9
8
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
8.9
98%
0.3051
Without feedback
2
2
3
2
0
4
3
3
3
2
2
1
7
2
3
7
5
6
3
4
4
3
6
9
3
4
2
2
0
4
3.36
37%
2.0424
2nd t test
With feedback
1.57
1.29
1.54
2.4
1.62
1
1.63
1.85
1.31
1.42
1.39
0.89
1.02
0.96
0.51
0.7
0.37
0.75
1.34
1.19
0.94
1.24
0.61
0.28
1.06
0.69
1.47
1.33
0.94
0.65
1.13
Without feedback
5.18
4.18
3.95
4.05
5.21
4.3
4.8
4.28
4.82
5.11
4.62
5.34
3.45
4.43
4.35
3.56
3.7
3.76
4.51
4.07
3.88
7.82
3.92
3.07
4.42
4
5.04
4.34
5.58
3.76
4.45
0.4664
0.8776
Figure 7.2 prepared first hypothesis test data
54
7.5.
First hypothesis data analysis
Distribution of data corresponding to the first hypothesis.
Figure 7.3 Distributions of with prototype (left) and without prototype (right) for the 1st
hypothesis test
Figure 7.4 Quantile-quantile plot of with (left) and without (right) prototype
Apparently, Figure 7.3 and Figure 7.4 show that the without prototype condition
almost fits normal distribution, but with prototype condition is not normal
distributed. Because number 9 on X axis is much more prominent than the rest
numbers on X axis. Actually, except number 8, 9 is the only column among the whole
graph. This could be caused by the small sampling size, which are only 30
participants. However, as the inferential statistics always assume that the data fits
normal distribution, so we can use this data to do t-test.
55
7.5.1. Calculation
By using the Matlab27, some code is entered to conduct the t test to exam the null
hypothesis. The following is a piece of pseudo code for analyzing t-test:
[h,p] = ttest (without feedback, mean of with feedback, significant level, tail left)
𝑡=
𝜇1−𝜇2
2
2
√𝜎1 +𝜎2
= 15.06 ;
𝑛−1
h =1
The part of the test report generated by GraphPad28 shows as following:
Paired t test
P value
Significantly different? (P < 0.05)
One- or two-tailed P value?
t, df
Number of pairs
< 0.0001
Yes
One-tailed
t=15.06 df=29
30
7.5.2. Interpretation
The returned values of h = 1 and p< 0.0001 determine the t test to reject the null
hypothesis at the 5% significant confidence. The rejection further means that the
probability of obtaining the sample means completely by chance is too small that
there is a backup for the alternative hypothesis.
Another way to reject or accept null hypothesis is to check the t table, in order to see
if the t value calculated from this data falls into the rejection area or not. In this data,
the degree of freedom is 29, significant level is 0.05, and one tails, so that the
corresponding t critical value associated with that is 1.699, which is much less than
the t value 15.6, so the t falls into the rejection area. Thus the null hypothesis should
be rejected on the significant level of 0.05.
According to the considerably big t value, the mean of with prototype is significant
27
28
http://www.mathworks.se/index.html
http://www.graphpad.com/guides/prism/6/statistics/index.htm?stat_checklist_pairedt.htm
56
higher than the mean of without prototype, based on which we can infer that with
prototype, the cyclists can detect 5 (8.9-3.36) more vehicles out of 9 than those
without prototype on the statistical significance of 5%.
7.6.
Second hypothesis data analysis
The average time difference in with prototype condition is 1.13 seconds, and this
difference is 4.46 second in without prototype condition.
Distribution of data corresponding to the second hypothesis
Figure 7.5 Distributions of with prototype (left) and without prototype (right) for the
2nd hypothesis test
Figure 7.6 Quantile-quantile plot of with (left) and without (right) prototype for the 2nd
hypothesis test
57
7.6.1. Calculation
𝑡=
𝜇2−𝜇1
√𝜎1
2 +𝜎22
= 21.44;
𝑛−1
h =1
The part of the test report generated by GraphPad shows as following.
Paired t test
P value
Significantly different? (P < 0.05)
One- or two-tailed P value?
t, df
Number of pairs
< 0.0001
Yes
Two-tailed
t=21.44 df=29
30
7.6.2. Interpretation
The calculated values of h = 1 and p< 0.0001 provide a very clear evidence for the t
test to reject the null hypothesis at the 5% significant confidence.
The alternative way to accept or reject null hypothesis is to check if the t value falls
into the rejection area. According to the t table when the degree of freedom is 29,
significant level is 0.05, and two tails, the t critical value associated with this data is 2.045, which is far less than21.44. Obviously, the t falls into the rejection area, the
null hypothesis should be rejected.
The considerably big t value indicates a prominent difference of means between
with and without prototype. The rejection of null hypothesis also hints that to obtain
the sample means by chance is very seldom, so that the alternative hypothesis is
accepted. Such a big significant difference means that the incoming traffic can be
detected 3.32 (4.45- 1.13=3.32) seconds earlier with prototype than without
prototype.
58
7.7.
Presentation of hypothesis tests
The first t test proves evidence that the prototype could help cyclists increase
awareness about incoming traffic to the extent of how many more vehicles the
cyclists could detect with help of the prototype rather than those who don’t use the
prototype.
The second t test evidences that the prototype could help cyclists increase
awareness about incoming traffic to the extent of how much earlier the prototype
could help cyclists detect vehicles than those who don’t use the prototype.
Therefore, based on the two hypothesis tests, the conclusion is that the prototype
could help cyclists increase awareness about incoming traffic rather than those who
do not use it.
59
8. Discussion
Test results of both time needed to detect the vehicles and traffic identification
ability measurement indicated that the sound warning system could potentially
increase the traffic awareness of cyclists. However, further product testing with ideal
settings in a realistic environment is needed to confirm the effectiveness of the
warning system, as several factors could have contributed to the test results.
As mentioned in methods, all test participants are students from Aalborg University
Copenhagen, but it is possible that some of the participants do not have the same
experience and ability to identify incoming traffic from a far distance as other
participants who bike regularly on rural roads. In the city, cyclists have fewer reasons
to be on constant alert of approaching cars due to bicycle lanes on most of the roads.
Slower speed limits in the city also means that the cars could sound slightly
differently than the samples played to the participants during the test – as written in
the analysis that at low speeds, tire and air friction sounds are less dominant
compared to the engine sounds than at higher speeds.
Therefore the participants who ride on rural roads regularly might have had an
advantage when identifying cars that are approaching from far away. For example,
the test conductors learned that participant number 24 bikes regularly on rural roads
and also listens to music while cycling, and the results showed that he could detect
all cars before they arrived without using the feedback. On the other hand, the
warning system was able to shorten the participant’s reaction time due to the fact
that MAX MSP program would detect cars much earlier than the participant could,
therefore traffic awareness was still increased for the participant when using the
warning system.
What can also be discussed is whether there is a difference in testing the prototype
in a lab or testing in a rural road. The sound from the MAX soundscapes, which
composition consisted of pre-recorded audio samples made it difficult to recreate a
very realistic sound environment. Playback of samples through speakers and
headphones mean that there is an unavoidable loss of sound quality. Besides audio
quality, there could be more types of ambient noises in the real world than the
samples used in the MAX soundscape. More importantly, the sound absorbing foam
material in the auditory lab would have changed how sounds are reflected back to
the test participant, and therefore sound acoustics properties would not be the
same. The lab’s foam absorbers, for example, would result in absence of reverb and
echo, in contrast to what happens in reality. A possible solution to improve the
realistic aspect of sound playback in an audio laboratory would be to have very high
60
quality sound samples, as well as multiple speakers that could simulate the direction
of sounds, echo and reverb effects etc.
The test results could also have been different if it have been tested in the real
environment than in a laboratory, since on the rural road, the test participants
would have more distractions to divide their attention such as changing
surroundings and the attention need to riding on a bicycle. There are no distractions
in the laboratory so the participant only had to concentrate on traffic sounds or
audio feedback; therefore reaction times could be higher compared to the test
results. The participants that were fast enough to press the button before the cars
passed by when using the warning system might not have been able to identify as
many cars in time rather than if the test had been conducted on a rural road.
Limitations during implementation meant that the prototype in many ways did not
resemble how the final product should detect traffic sounds. An obvious issue with
the prototype was that it was only analysing pre-recorded sound samples, instead of
actual sounds captured through a microphone in real time, as originally intended in
product design. Even though in theory, the prototype would be still efficacious if the
microphone would work as intended, further testing would be needed to ensure
that the extra process and hardware would not affect the performance of the
product.
The sound warning system prototype detected cars from the samples by first
filtering out most of the frequencies and only allowing sound waves within a narrow
range of low frequency to pass. This limited frequency range is where the car engine
sounds would be, and once the amplitude of the filtered audio reaches a certain
threshold, the audio feedback is triggered. This way of detecting incoming cars has
an obvious drawback, as if there are other noises within the car engine frequency
range, they are likely to trigger the audio feedback and create false warnings.
Therefore it is necessary for the study to go back to the drawing board and explore
alternative ways to detect cars, which should reduce the potentiality of false
warnings being triggered.
The frequency filter of the warning system prototype was able to filter out noises
such as bicycle bell rings and bird tweets. But as an example to the less than ideal car
detection method mentioned above, wind noise was found to trigger the system
unintentionally, as some of its frequencies overlap with the car frequency range
where the prototype was set to trigger audio warnings.
Another matter that should be investigated for further improvement of the sound
warning system design is whether the low frequency band pass filter would be able
to detect all types of cars. The filter currently detects sounds emitted from internal
combustion engines. But due to evolving technology, many cars on the road in the
future will be powered by electric motors, which would not trigger the warning
system as its noise is outside of the filter’s current frequency range. Even if the filter
is changed to match the electric motor frequencies, the sound volume emitted from
61
the electric motor might still be too quiet to be detected. Therefore the warning
system should also consider using other sounds emitted by cars to trigger the audio
warnings, such as friction noises from the tires and cars travelling through air at
higher speeds.
The design and implementation of the audio feedback warning is another topic of
discussion, as the implementation of audio feedback did not entirely comply with its
design. For example, the prototype was originally implemented to shut off the music
completely when the audio feedback is triggered. But during the pilot test, it was
understood that the interruption of the music was an annoyance factor and
therefore the warning system was then adjusted to lower the music volume to 30%
instead of simply stopping the music every time the sound feedback played.
According to results of the questionnaires (see 11.2) answered by the test
participants after testing, 8 of 30 participants found the audio feedback annoying,
even though one of the requirements for the audio feedback design was not to
annoy its users. Similar to the music interruption issue, the actual implementation of
the audio feedback did not follow all aspects of the design. For example, it was
stated in design that the feedback tone should not be repetitive, and that the
rhythm of the sound should vary. But the implemented audio feedback was a single
harmonized tone that had a typical vibrato effect, which means that it was somehow
repetitive and the rhythm did not vary enough either. Even though the majority of
the test participant either found the audio feedback to be neutral or pleasant,
making the audio feedback more compliant to the design and the research findings
in analysis might further reduce its annoyance level to the user.
62
9. Conclusion
The project is based on the problem that when cyclists are listening to music through
headphones, traffic sounds are masked to an extent by the headphones and the
music. The preliminary analysis contains general research about the risks of cycling
in the rural roads while listening to music through headphones and ear buds. It was
found that the risks of cycling in rural areas are higher than urban areas, and the
target group research showed that cyclists in the age group from 18-34 year-olds has
a high risk of being involved in a traffic accident. Therefore cyclists within this age
group were chosen as the target group for the project; and the project was further
narrowed down to rural roads as the target environment.
In state of the art, a current solution to help cyclists becoming more aware of
incoming traffic by reducing wind noises heard by the cyclist using ear covers, and
amplifying other ambient sounds through microphones gave inspiration to the
product design. Microphones were also found as an affordable solution for inputting
relevant data to the warning system. A drawback of using microphone is that it is
easily interfered by wind noises; however the study found that windjammer such as
foam covers and “furies” could effectively eliminate wind noise interference.
Different options to deliver feedback to inform the cyclists about incoming traffic
were explored. Within the options of audio, visual and vibrotactile feedback, it was
determined that audio feedback would be the most appropriate choice for the
project due to the fact that it could be utilized without high annoyance to the user,
and at the same time allow the user to keep the visual attention on the road.
In the analysis chapter, it was discovered that typical vehicles in traffic generate
noise around 70-80 dB in the frequency range from 50-100 Hz. This data would later
be used for implementing the sound warning system. Guidelines for audio feedback
design helped the project in making an effective audio warning without the feedback
being annoying to the user. An audio warning sound similar to an earcon was made
in MAX by manipulating parameters such as pitch, harmonics and duration. The
sound warning system itself utilizes sound processing techniques including envelope
following and frequency pass filtering. A working prototype was tested on AAU
students in Copenhagen.
The test was conducted to see if the prototype could answer the final problem
statement. Results measured from both vehicle detection time test and vehicle
identification ability test indicated that the prototype succeeded in increasing traffic
awareness of the test participants. In vehicle detection time testing, all participants
were able to detect incoming traffic earlier using the sound warning system than
without. In vehicle identification testing, the majority of the test participants were
able to detect more vehicles before the vehicle passes by using the warning system
as well, with an exception of one participant who was able to detect all cars without
63
using the system. However several factors were found to have possibly induced the
results being favorable towards the product prototype. Therefore an improved
product, test settings and testing procedure are required to confirm the results of
the conducted prototype testing.
10. Future perspectives
The sound warning system prototype is not the final product, therefore it has not
been fully developed and many things could be improved, which will be described in
this chapter.
Regarding the audio feedback, some of its aspects also have to be improved. One of
the requirements was not to annoy the users, as seen from the questionnaire results
in section 11.2, almost 27% of the participants found the feedback annoying. If the
audio feedback avoided repetition and having consistent rhythm, it might have
reduced annoyance.
In order to detect the cars in the real environment, the microphone has to be
implemented into the warning system. The microphone will be placed on the back of
the bicycle, as cyclists are mostly not aware of cars that are approaching from
behind. Once the microphone is implemented, the sound detecting software could
be converted onto mobile phones as an application, making the sound warning
system light and portable.
The software is not yet fully developed and improvements are needed in terms of
function and performance, such as how effective the sound warning system is to
detect incoming traffic. The way the sound warning system in MAX MSP detects the
cars is currently dependent on whether engine sounds are above a certain threshold
at a specific frequency. This means that noise within the same frequency will trigger
the feedback as well. In the real environment, a lot of noise might occur and
therefore it is important to find a solution so that the warning system is able to only
detect cars.
Another problem is the variance in types of cars. Different types of cars have their
own frequencies and amplitude levels, and some cars almost emit no audible sounds
such as for instance electric cars, making it very difficult to program such a patch in
Max MSP.
Finally, there is a possibility that several cars are approaching at the same time
although it is made for rural roads, therefore the ability to detect several cars have
to be implemented as well.
64
11. Appendix
11.1. Methods
When calculating the results several terms has to be taken into consideration such as
one-tailed, null hypothesis, alternate hypothesis, p- and t- values, type 1 error, type
2 error, rejection region, critical value and t-table.
T-value
T-value represents the difference between the mean or average scores of two
groups, while taking into account any variation of the results. It is the value that is
going to be calculated and compared to the critical value in order to reject or accept
the hypothesis.
One-tailed
If a certain outcome is expected like for instance that the treatment group is
expected to give better/worse results than controlled group, a one-tailed t-test
would be the approach29. This is due to a significant difference of the mean results is
expected before hand. This test will disregard the possibility of a relationship to the
negative or positive direction depending on what is expected19.
Two-tailed
A two-tailed test is used when the null hypothesis has the possibility of going both
ways20. This means that a value will be on both sides of the normal distribution. An
example of a two-tailed null hypothesis could be “there is no difference in terms of
gender” while a one-tailed would be “men is worse than women”. This said the onetailed probability value would be half the two-tailed probability value19.
Rejection region
When having a one-tailed t-test it means that the rejection region will only be one
sided when doing normal distribution; either negative or positive30. In this case it is
expected to be on the positive side of the normal distribution as dealing with only
positive numbers.
If the t-value calculated is going to appear in the rejected region, then the null
hypothesis will be rejected. Else if the t-value appears not to be in the rejection
29
30
http://statpac.com/manual/index.htm?turl=onetailedandtwotailedtests.htm
http://www.mathsrevision.net/alevel/pages.php?page=64
65
region then it will be in the so called acceptance region, which means that the null
hypothesis might be true31.
The rejection region differs, therefore to be able to find out what the rejection
region is according to the data that is collected then it is necessary to take a look at
the critical value and the t-table.
T-table
The t-table or t distribution table is a table that given the degree of boredom and pvalue shows the critical value in percentages32. The critical value is the value that will
determine whether to reject the null hypothesis or not. If the t-value calculated is
smaller than the critical value from the t-table, the null hypothesis cannot be
rejected but if the value is bigger, the null hypothesis is rejected.
Null hypothesis
A null hypothesis is the one assuming that there is not a difference between the
measured variables. It is the hypothesis, which results are by chance33.
The reason for making such a hypothesis is because it is easier to disprove and
hereby support the alternative hypothesis, for instance “Using our system is worse
than without the system.” By rejecting this hypothesis, it is likely that the results are
influenced by a non-random cause and hereby automatically supports the
alternative hypothesis to be true34.
Alternative hypothesis
Alternative hypothesis is the hypothesis that states that there is a relation between
the measured variables. It is the hypothesis which results are influenced by nonrandom causes, meaning that it is not a coincidence21. The alternative hypothesis is
what is thought as being true, although one can never prove but only assume that it
is true22.
Sometimes it happens that errors occur, that is when assuming something although
the results show something else. This is the kind of errors called type 1 error and
type 2 error or known as respectively alpha (p-value) and beta as these describes the
probability of that specific kind of type error to occur21.
Type 1 error
Type 1 error is the error that occurs when the null hypothesis is true, but is being
rejected21.
Type 2 error
http://stattrek.com/hypothesis-test/region-of-acceptance.aspx
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
33 http://stattrek.com/hypothesis-test/hypothesis-testing.aspx
34 http://www.csulb.edu/~msaintg/ppa696/696stsig.htm
31
32
66
Type 2 error is the error that occurs when the null hypothesis is false but is being
accepted as being true21.
As the type errors are the opposite of each other it means that when reducing one
error, the other will increase.
p-value
p-value represents statistical significance or the probability of error associated with
the spread of the observed results to the entire population. For example, p-level
= .05 shows that there is a 5% chance that the sample found in the relationship
between the variables. This could the only feature of this random sample. By looking
at the t-table a comparison of the real value from the table and the value gotten
from the testing can conclude how reliable the test is and how significant the results
are.
11.2. Questionnaire
Apart from the testing itself, a questionnaire was made after the testing in order to
know if the feedback caught the test subjects’ attention, to what degree the
feedback was found easy/difficult to recognize and to what degree they found the
feedback pleasant. A total of 30 test subjects applied their answers of the
questionnaire.
Making such a questionnaire will help knowing whether the feedback meets the
requirements established in the design chapter.
Did the sound feedback catch
your attention?
0%
Yes
No
100%
For the first question it was asked if the test subjects thought the feedback caught
their attention. 30 out of 30 test subjects (100 percent) answered yes.
67
How annoying / pleasant was the
sound feedback?
6%
7%
Easy
Neutral
Difficult
87%
For the second question, it was asked to what degree they could recognize the
feedback. For this question they got three options: 1. Easy, 2. Neutral and 3. Difficult.
Out of the 30 test subjects, 2 test subjects (7 percent) thought it was difficult to
recognize. Another 2 test subjects (7 percent) found it neutral and 26 test subjects
(86 percent) found it easy to recognize.
How annoying / pleasant was the
sound feedback?
27%
36%
Pleasant
Neutral
Annoying
37%
For the third and last question three options were given to the test subjects as well.
For this question it was asked to what degree the test subjects found the feedback
pleasant. According to the results, 11 test subjects (36/37 percent) found it pleasant
while 11 test subjects (36/37 percent) found it neutral and 8 test subjects (27
percent) found it annoying.
68
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