Multi-Disciplinary Engineering Design Conference
Kate Gleason College of Engineering
Rochester Institute of Technology
Rochester, New York 14623
Project Number: 11227
FORMULA EXHAUST ACOUSTIC TUNING
Kyle Desrosiers/ Project Manager (ME)
Bradley Fiedler (EE)
Christopher VanWagenen (EE)
Greg Wodzicki (EE)
May 13, 2011
ABSTRACT
To be the best formula team in the nation, all the
components of the racecar and the members must
work together. The RIT FSAE Formula team has
become one of the elite teams because of the
chemistry the members have and the highly designed
components of the car.
A problem that seems to be recurring was the
exhaust and the sound level that was being emitted.
To be able to compete, the sound level from the
exhaust has to be below a certain level. The SAE team
has been forced to throw more and more glass pact
into the muffler to reach this level and compete.
To eliminate this problem of putting more glass
pact in, a proposed Active Noise Cancellation (ANC)
system was devised to replace the entire muffler. The
ANC system should be more cost effective, weigh less
or equal to, and be within the current measurements of
the current muffler.
INTRODUCTION
The exhaust system for the RIT FSAE team has
caused delays numerous times and limits the time the
FSAE team has to practice and race. While there is a
maximum for the sound level to race and the RIT team
meets that rule regularly, every time the racecar is
driven, the glass pact used in the muffler deteriorates
and moves within the muffler, which causes more
noise to come out of the exhaust. When the RIT team
cannot meet the maximum sound level, they will pack
in more glass pact to quiet the exhaust. More glass
pact translates to more weight to the racecar which
will slow it down. Also, adding more glass pact
would create more back pressure which would
decrease the performance of the engine. The idea of
an ANC system replacing the muffler gives hope that
the exhaust system is at a constant weight, provides
the necessary sound reduction, and maintain optimal
engine performance. Having the necessary sound
reduction provides more time for practicing and
racing, and without any concerns about losing
performance from the exhaust, the RIT FSAE team
can concentrate on other components of the car.
The goal of an ANC is to use a processed sound
from a speaker to physically cancel a source sound. A
perfect ANC system would diminish the source noise
to zero dB. The system uses a DSP control board to
input the source noise and outputs a near perfectly
inverted signal. A speaker is driven to provide the
process sound to cancel the source sound. With the
source noise and the processed sound from the output
of the DSP merging together by a piping system, the
result should be a noticeable reduction in sound. This
sound reduction will be drop the engine exhaust to
below the FSAE ruling.
Copyright © 2011 Rochester Institute of Technology
Proceedings of the Multi-Disciplinary Engineering Design Conference
Page 2
NOMENCLATURE
DESIGN PROCESS
ADC – Analog to Digital Converter
ANC – Active Noise Cancellation, using speakers to
actively cancel noise without any absorption
DAC – Digital to Analog Converter
dB – Decibels, units to measure sound levels
DSP – Digital Signal Processor
FIR – Finite Impulse Response, type of algorithm
FSAE – Formula SAE, collegiate formula car racing
Hz – Hertz, unit of frequency
LMS – Least Mean Square, type of algorithm filter
used in the ANC system
PVC – Polyvinyl chloride, widely used plastic in
construction and piping
RIT – Rochester Institute of Technology
SAE – Society of Automotive Engineers
SPL – Sound Pressure Level
TI – Texas Instruments, electronic company
Top-down/Bottom-up Design
Top-down and bottom-up designs are methods
used by companies worldwide for their own projects.
The top-down method starts with specific design need
and then works around this need to design smaller
components. The bottom-up method starts with the
basic information to design a system. Usually the
bottom of a project is research or the mental
knowledge, whereas the top is the application stage of
the project. Both methods were used while devising
the ANC system. The research at the beginning of the
project helped sort out the parts needed to build an
ANC system, whereas building a basic system that
would work helped with the physical design of the
system.
OVERVIEW
After starting the research of ANC systems, the
topics were split up within the team. One group was
devoted to researching about signal processing and the
other group was devoted to sound properties. With
this research, the groups followed their path with the
building and testing stages of the project. The group
that researched signal processing concentrated on the
DSP and the algorithm needed, and the group that
researched sound properties concentrated on building
the design of the ANC system and collected the data.
As the groups expanded their knowledge in their
specific area, calibrations occurred to design and build
the final ANC system. Even though the main goal was
to have a fully functional ANC system to replace the
muffler exhaust on the FSAE racecar, the research
proved that building a reliable and functional system
within twenty-two weeks was near impossible. It was
decided then that the end product should be more of a
proof of concept. This was going to be the first step
towards a fully functional ANC system for a FSAE
car.
Figure 1 displays the basic flow chart of an ANC
system using two microphones.
Figure 2: Diagram of the top-down bottom-up
methods
Figure 2 is a diagram of the top-down bottom-up
methods. This is the basic path the team took to have
a finished ANC system.
Needs
For the first eight weeks of the project, it was not
clear what the needs were for the customer. Through
the beginning of research and calibration with the
customer, the needs for the customer changed several
times. The evolving requirements delayed the
progress of the project and forced the team to adjust to
the changing environment. It was not until research
was done and discussions between the team and
customer had occurred that the needs were finally
established.
Figure 1: Simplified flow chart of an ANC system
Copyright © 2011 Rochester Institute of Technology
Proceedings of the KGCOE Multi-Disciplinary Engineering Design Conference
Specifications
The specifications are derived from the needs and
wants from the customer. Once the needs for the
customer were sorted out, the specifications were
clearly defined. When everyone agreed that the needs
of the customer could not be accomplished within
twenty weeks, the project team provided their own
specifications to fit with the realistic goal of the
project.
Primary Design
The main design we are using for the ANC system
is using PVC piping connected to two speakers. The
two pipes from the speakers are then merged into one
using a wye connection. The wye connection provides
enough interface between the two sound waves that
the emerging waves become one single sound wave.
Since the sound waves are emerging as the primary
signal source is processed and outputted, the inverted
sound would physically cancel a percentage of the
primary sound. Two microphones were placed in the
piping, one near the primary source and the second
near the single opening, to allow the DSP to capture
the noise and process it for the secondary speaker.
Alternative Design
The second option for an ANC system design was
to use a dipole box. The idea of using a dipole box
would be that it would work better than a wye
connection if the source was an engine and decrease
the probability the speaker would be damaged from
the intense temperature and pressure from the exhaust
gases. After testing and recording the data, the dipole
box seemed to work, but not as well as the wye
connection. The dipole box created dead spots
because the two sound waves were not exiting as a
single wave. Similarly to the primary design, there
would be two microphones placed near the primary
source and the opening, and would be used as the
inputs for the DSP.
Feasibility Assessment
As the weeks passed on the project, it was more
and more evident that a four-person group of student
engineers could not complete the task as initially
described in twenty-two weeks. Though the primary
goal was to be able to use an ANC system on the
FSAE team, the actual goal became to have a
functional ANC system in any size. It was realized
that a functional ANC system for an automobile still
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has years of research, testing, and new technologies
before a group of engineering students can
manufacture one.
PRELIMINARY DESIGN
Hardware
Since the project was mainly split between
hardware development and software development, the
hardware had to be picked out as early as possible so
the software could be implemented. Most of the
hardware parts were easily accessible from nearby
stores, i.e. RadioShack or Lowes, or the turnaround
was relatively quick.
One model of microphone was selected to the two
microphones used within the piping of the ANC
system. Though the selection process was fairly easy,
the discussion about using different microphones
occurred throughout the testing of the system.
The important hardware portions that had to be
selected were the speakers and the DSP. The size of
the speaker determined the frequency range for
possible cancellation. From data collected from the
lawn mower engine and noise cancellation of pure
harmonic tones, the range for which the best
cancellation occurs below 800 Hz. A ten inch speaker
was selected as the best fit speaker for noise
cancellation.
The DSP selection had to be the most important
part of the project because if it was too slow or could
not process the algorithm that was needed, then there
would be no sound cancellation.
Software
The C5505 DSP was provided with sample code
for many different applications. One of these
applications was an active noise cancellation system
for headphones, much like the systems used by Bose
and other audio companies. This was the basis of our
ANC algorithm program, and provided a necessary
foundation to ease the programming. The algorithm
used in the headphone app is similar to that used for
the general noise cancellation but differs in some key
application that limits its viability for other programs.
The software suite, Code Composer Studio is free for
the C5505 and comes with the other DSP.
Copyright © 2011 by Rochester Institute of Technology
Proceedings of the Multi-Disciplinary Engineering Design Conference
ENGINEERING MODEL
Hardware
The task of choosing an appropriate DSP for this
project was difficult. Texas Instruments manufactures
two large, multiple-channel development kits based on
two different DSPs, with one focused on a very wide
range of applications, and the other specifically
designed for audio processing. Since this project
consisted entirely of audio processing, filtering, and
algorithm design, the TI TAS3108EVM2 evaluation
module was selected.
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powered from a single USB port, eliminating the need
for an external power supply. Extensive
documentation, a huge code base, and PowerPoint
learning materials made programming this DSP a
much more reasonable task.
Figure 4: VC5505 DSP Evaluation Kit
Software
Figure 3: TAS3108EVM2 evaluation kit
This module contains 8 inputs and outputs, and
also comes with a graphical design tool to make
programming easier. Since most DSPs are
programmed using assembly language, this tool was
embraced as a quicker and simpler way to implement
any required filters and the LMS algorithm. However,
after several days of experimentation and
communication with TI, it became apparent that the
graphical programming tool was much less useful than
we had hoped. Only simple FIR filters, volume
controls, and basic mathematical functions could be
performed. Additionally, there was no option to
program in a high-level language such as C, and the
only way to program the DSP was to use assembly
language for the entire program.
After discussion with TI, it was recommended
that our team switch to a VC5505 low-power DSP.
This development module is shown in Figure 3. This
processor included only one stereo input and output,
which meant that only a single ANC system could be
driven at one time; this was not a problem, as a
modular system was being designed for using the
system with different sources. This DSP also came
with teaching materials, including a very basic
headphone ANC program, to get us started. The
processing speed and sampling rate of this smaller,
low-power DSP were much lower than the
TAS3108EVM2, but did not cause a significant delay
due to processing time, and the entire board could be
Programming the DSP to perform active noise
cancellation was somewhat difficult. Due to the speed
requirements of this type of system, high-level
programming languages cannot be used (upon
compiling, the program will be too slow). This limits
the programming language to assembly, which is very
difficult to follow and comprehend. Sample code
provided with the kit implemented a single-input ANC
system designed for headphones. Using this code as a
starting point, we were able to create a system that
takes two input signals (a primary microphone and
secondary microphone), performs LMS calculations
on these signals, and outputs an appropriate signal to
cancel the primary noise source. The LMS algorithm
was programmed in assembly language for maximum
speed and minimum delay in the system, while the
main program (mapping inputs, performing FIR lowpass filtering, outputting status) was programmed in C.
A sampling rate of 48 kHz was used to eliminate
aliasing in the microphone signals and preserve as
much of the original sound quality as possible.
To improve the accuracy of the LMS algorithm
and reduce processing complexity, a low-pass filter
was designed and implemented. Previous testing
showed almost no cancellation of pure tones above 1
kHz, so a 1 kHz Hamming-window low-pass filter was
used to significantly reduce high-frequency
components detected by the two microphones. A filter
size of 51 taps was used, enough to produce a
reasonable high-frequency attenuation but not too
large as to slow down the processing of the DSP. This
filter was applied directly to both inputs before
passing to the algorithm. The filter itself was designed
using FDATool in MATLAB, which creates the
appropriate number of FIR filter coefficients based on
several specifications such as necessary stop band
attenuation and cutoff frequency.
Copyright © 2011 Rochester Institute of Technology
Proceedings of the KGCOE Multi-Disciplinary Engineering Design Conference
The goal of this algorithm is to create an out-ofphase signal based on the primary microphone and
detected error and drive the output of the system
(sound detected at the error microphone) to zero. This
“adaptive filter” must process the signal and create its
opposite as quickly as possible, before the wave front
from the primary source reaches the coupling joint that
combines the primary and cancellation sources. In
practice, this was not a problem, as the total
processing time was on the order of nanoseconds and
the time for the wave front to reach the junction was a
few milliseconds.
Figure 5 displays how the algorithm is
implemented within the ANC system. X(k) is the
inputted sound from the source and e(k) is the
produced sound from the DSP outputted to the
speaker. The LMS algorithm would process the two
signals (primary and the output) and implement the
resulting signal to the controller, C(z).
Figure 5: Functional block diagram of the
algorithm used in the ANC system
EXPERIMENTAL SETUP
For the initial testing to determine if ANC is
possible in practice, two smaller computer speakers
were connected to PVC piping and merged in a wye
connector with a single output. Using a waveform
generator, one speaker outputted a sine wave harmonic
tune while the other speaker outputted an inverted sine
wave of the same frequency as the other speaker.
Different frequencies were tested for their noise
cancellation capabilities at different distances from the
speakers. The data that was collected was then
analyzed through spectral analysis. Through these
tests, there was a noticeable increase in sound
reduction as the frequency of the sine wave decreased,
and as the frequency approached and passed 1 kHz,
any noise cancelling effect was not noticeable, which
was expected from the research.
As the project continued, the same test was used
as a preliminary test when using the ten-inch speakers
that were selected. The next step in testing was to
include a DSP, but without microphones.
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Figure 6: Model of the fully assembled ANC system
Figure 6 is the basic design of the fully assembled
ANC system being used. The DSP and microphones
are not in Figure 9. It is also the same design used for
the preliminary testing with the small computer
speakers and the selected ten-inch speakers.
Two additional microphones were installed at
equal distance from the wye intersection in order to
test for output accuracy and delay. The difference in
signal structure shows the accuracy of the algorithm
and the viability of the design. The delay is seen when
the difference in identical signals from the two
microphones is found.
The system was also tested in a dipole setup. This
allowed the setup to be removed from possibly
harmless environmental hazards in the primary path.
At a disadvantage, full wave superposition does not
occur and not all spots are accurately attenuated.
Microphone placement also moves outside the device
and requires precise placement.
RESULTS AND INTERPRETATION
Prior to completion of the DSP, it was important
to test the physical set up of the system to make sure
sound waves were converging in a desirable and
effective way to cancel each other. The following
graphs show results for the Wye connector setup using
small computer speakers playing pure tones. The
speakers are perfectly out of phase from one another
and as such provide the optimal (ideal) reduction value
we could expect out of a perfect ANC algorithm.
Copyright © 2011 by Rochester Institute of Technology
Proceedings of the Multi-Disciplinary Engineering Design Conference
Page 6
Figure 7: Results from preliminary testing at 200
Hz
Figure 9: MATLAB simulations of the DSP
algorithm
Figure 8: Results from preliminary testing at 1000
Hz
As can be seen in the graphs provided, as the
frequency to be attenuated increases closer to 1000
Hz, the attenuation from the ANC system greatly
reduces to almost nothing. At 200 Hz, reduction
achieved is roughly 30 dB. These testing results prove
that the processed signal will not have a noticeable
effect on a sound that approaches and surpasses 1000
Hz in frequency.
Prior to receiving the DSP, an algorithm was
created in MATLAB to simulate the functionality and
feasibility of using the algorithm for an ANC system.
After some debugging, the results of the simulations
came out to be surprisingly excellent. These results
boosted the moral of the team and the guides, and gave
hope that the finished product will have similar results.
Figure 9 displays those MATLAB results. While the
MATLAB results were for ideal conditions, the actual
results using the hardware were not expected to be
exactly like the simulations. This is because of the non
ideall hardware factors that cannot be taken into
consideration easily in simulation.
The first tests that included a DSP used a signal
directly from a media player (laptop, mp3 player, etc.)
and outputted the inverted of that signal. Using a
Sound Pressure Level (SPL) meter, the noise level
being emitted was observed to be lower, but was not
as low as expected. Just listening to the emitted
sound, it was evident the sound reduction was in the
bass area (low frequency range). This proved two
hypotheses; ANC was possible when using a DSP, and
lower frequencies are easier to attenuate.
The next step of adding two microphones within
the ANC system proved to be a challenge. Before
testing, it seemed like cheap microphones would be a
good choice to use because they use the same element
as more expensive microphones and were a fraction of
the costs, but the resulting sound from the two
speakers discouraged this choice.
To debug the resulting sound when using two
microphones, two more microphones were placed
equal distances away from the wye connection. The
signals from the two additional microphones were
monitored when the testing was repeated.
Copyright © 2011 Rochester Institute of Technology
Proceedings of the KGCOE Multi-Disciplinary Engineering Design Conference
Page 7
CONCLUSION
REFERENCES
The final design of the ANC system is the first
step towards applying an ANC system for the exhaust
of the RIT FSAE team. Though the project was
unsuccessful in completing an ANC system that could
be used as an exhaust, the ANC system was able to
cancel out sounds at certain frequencies. It was the
starting point for future projects towards an ANC
system. The whole project also was a teaching tool for
possible real world events and displays the knowledge
and experience of the future engineers that have
worked to complete it.
General
[1]http://paws.kettering.edu/~drussell/Demos/superpos
ition/superposition.html
[2]http://personal.cityu.edu.hk/~bsapplec/natureof1.ht
m
RECOMMENDATIONS
Signal Processing
[5] Elliot, Stephen, “Signal Processing for Active
Control” 2001, American Press.
[6] Kuo, Sen M. and Morgan, Dennis R., “Active
Noise Control Systems” 1996, John Wiley & Sons,
Inc.
The customer(s) needs to be clearer on what the
desired product should be when the project is in its
beginning stages. The fact that the direction of the
project changed a few times within the first eight
weeks delayed the amount of knowledge that was
obtained about ANC systems and DSP boards.
Having mainly electrical engineering students
worked well while designing an ANC system, but
having a student engineer with more experience with
programming and in different languages would have
been helpful. Electrical engineering does not require
as much programming as computer or software
engineering. A more experienced programmer would
have had more success using and figuring out the
software, language, and algorithm that was used with
the DSP boards.
Texas Instruments
[3]http://focus.ti.com/docs/toolsw/folders/print/tas310
8evm2.html
[4]http://focus.ti.com/docs/toolsw/folders/print/tmdx5
505ezdsp.html
Miscellaneous
[7]http://ausweb.scu.edu.au/aw06/papers/refereed/pen
nell/paper.html
ACKNOWLEDGMENTS
The team would like to express its appreciation to
those who made contributions to this project.
The team would like send out their deepest
gratitude to Professor George Slack and Professor Ed
Hanzlik for guidance and support through the whole
project experience.
Thanks to Texas Instrument for donating both
DSP boards and helping the team with their software
and algorithm and Cenco for donating audio
equipment that is essential for ANC systems.
Thanks to Dr. Dorin Patru and Dr. Vincent
Amuso for helping the team with their expertise on
DSPs and algorithms.
Copyright © 2011 by Rochester Institute of Technology