Final Project Proposal - EDGE - Rochester Institute of Technology
Adaptive Noise Cancellation
System
A simple active noise cancellation system to counter a specific noise signal.
Prepared By: Irtaza Ahmed
Michael Foster
Michael Loper
Date: September 20, 2015
Table of Contents
Figure 1: Level zero block diagram representing the FPGA, inputs, and outputs.
Figure 2: Level one block diagram of the Virtex-5 FPGA.
........................................................... 9
Figure 3: Level three block diagram of a general DSP slice used as a Multiply Accumulate
Figure 6: Top-level block diagram of the adaptive noise cancelling algorithm.
Figure 7: Pin configuration of the microphone.
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Figure 8: Block diagram of the I2S hardware needed.
............................................................... 17
Figure 9: USB Controller on the Virtex-5.
..................................................................................... 18
Figure 10: Audio Codec on the Virtex-5.
...................................................................................... 19
Figure 11: User interaction state diagram.
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Figure 12: Prototype of user interface.
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Adaptive Noise Cancellation System P a g e | 1
Overview
Needs Statement
Noise cancellation is a thriving and popular industry in today’s society. As we push forward in this modern era, technology is becoming an ever growing distraction and hindrance in the flow of daily life. The need to unplug and disconnect becomes evident. Noise cancellation headphones can help to provide that relaxing commute and block out the uncomfortable noises surrounding travel via aircraft, trains, or even automobiles. Larger noise cancellation systems can also remove distracting background noises to provide that perfect laboratory or educational working environment. A small and affordable system is needed to counter unwanted noise signals and to create a quieter and more comfortable environment for the customer.
Objective Statement
The objective of this project is to research, design, and implement an active noise cancellation system that will be affordable to a general audience. The device will produce signals to achieve destructive interference with an input signal. The input signal will represent unwanted noise outside of a desired frequency range. The system will be primarily designed for use with headphones, but it will be adaptable for use in more complex applications such as a speaker system. One possible application of the system would be a laboratory environment which contains noisy machines or power tools. In this application, the system would be connected to a set of speakers in the laboratory, and the system would counter the noise produced by the machines or tools.
Headphones could also be connected to the system, and it would function like a set of noise cancelling headphones.
Description
Adaptive noise cancellation systems actively produce counter signals to block out undesirable signals at specified frequencies or frequency ranges. The adaptive noise cancellation system for this project will be built using two microphones, a headset, and a
Field Programmable Gate Array (FPGA). There will be one external microphone that will take in the noise signal to be processed by the FPGA. The FPGA will then use the
FIR (finite impulse response) and LMS (least mean square) algorithms to attempt to produce the necessary counteractive signal. The FPGA in turn sends the counter signal to the headset. The signal from a feedback microphone in the headset will be used to produce the next iterations of the algorithm until a desired noise signal degradation threshold has been reached. If the system is used with a set of speakers, the feedback microphone would be placed in the region where noise cancellation is desired.
Adaptive Noise Cancellation System P a g e | 2
Marketing Pict ure
Adaptive Noise Cancellation System P a g e | 3
Requirements Specifications
Customer/Marketing Requirements
1. The system should be safe.
2. The system should be small.
3. The system should be affordable.
4. The system should correctly cancel all undesired sounds.
5. The system should use standard audio connectors.
6. The system should be easy to maintain and upgrade.
7. The system should be easy to move.
8. The system should be durable and have protection against damage.
9. The system should not produce much heat.
10. The system should have a simple interface.
11. The system should be aesthetically pleasing.
12. The system should be energy efficient.
13. The system should be able to accept input noise.
14. The system should reduce the volume of undesired sounds quickly.
Engineering Requirements
Marketing
Requirements
10
Engineering Requirements Justification
6
3
4,10
5,6
1,2,7,8,11
A. User interface will consist of a single on/off switch.
The system does not need to be configured by the user.
B. The design process will be well-documented.
C. Upon power up, the system will counteract noise signals which fall between 40 Hz and
80 Hz.
jack.
D. The output of the system will be a 3.5 mm headphone
Documentation is important for any future work that may be done on the project. It can also be used as a resource for other projects.
This chosen frequency range signifies all the unwanted noise the system will need to counter-act. This range does not need to be configured by the user.
The 3.5 mm headphone jack is the standard for many electronic devices.
E. The cost of the system will not exceed $1000 per academic unit.
F. The processor and all internal connections must be enclosed in a hard case with dimensions no more than four inches larger than the dimensions of the FPGA on each side.
Keeping the cost of the system to
$1000 per unit will help make the system more affordable and marketable to customers.
Keeping the system enclosed in a case that is about the size of the board used will ensure that the system is safe, small, easy to move, durable, and more aesthetically pleasing to the customer.
Adaptive Noise Cancellation System P a g e | 4
5,8
4
3,9,12
1,12
4
4,14
1,4
13
G. The system must contain two microphones, an FPGA, necessary wires, a headset, and a hard case.
H. The microphones must be able to detect frequencies in the range from 20 Hz to 20 kHz.
I. The system must consume less than 0.3 kWh on average.
The basic components are what the system needs to work.
This is a basic need of the system so that it can detect the noise signals which must be countered.
With a low power consumption, the product is more affordable. Low-power systems also do not produce much heat.
J. The system must not exceed a peak instantaneous power usage of 10 W.
K. A drop in the volume of the noise signal must be detected by 85% of individuals who test the system.
The noise signal will not be completely countered; however, a drop in volume should be detectable by a customer with average hearing capabilities.
L. The delay between the point at which the system is
If the delay is too long, there will be an extended period of unpleasant ringing powered on and the noise signal is cancelled will not exceed 3 seconds.
while the signals are not 180 degrees out of phase. If this period is excessively long, it could become unpleasant to the customer.
M. The volume of the counter signal will not exceed 85 dB.
Putting a cap on the peak power usage of the system will act as a safety feature to keep it from tripping breakers or drawing more than a desired amount of power at any given point in time.
This will also ensure that the system stays energy efficient.
N. The system must be able to interact with two microphone inputs either through line inputs or GPIO pins.
To produce a pleasant output sounds the counter signal must be relatively inaudible by humans. Also, the system should never produce a sound that could be painful or cause harm to the customers’ ears.
This is a fundamental part of the system’s operation. The system must be able to take in the noisy signal and have a feedback loop for the algorithm to work.
Adaptive Noise Cancellation System P a g e | 5
Concept Selection
Brief History
In the 1990s, personal active noise cancelling systems consisted of a pair of headphones connected to a processing unit. This processing unit was either encased in a hand-held module or it was integrated into a stationary object such as an armrest in an airplane seat. Modern noise cancelling headphone designs are now embedding the processors in the headphones. These processors could be high-end DSPs (Digital
Signal Processors) or ASICs (Application Specific Integrated Circuits).
Survey of Existing Systems/Components
Silentium is a company that specializes in noise cancellation systems and solutions. One of its products is the “ActiveSilencer Enclosure.” This noise cancellation device is specifically designed to minimize the noise pollution from servers by up to 10 dB without causing any thermal or cooling degradation. Silentium also has the “S-Cube
Development Kit” that is designed to allow an individual to create their own custom noise cancellation solution. Silentium suggests the S-Cube can be used in applications such as reducing fan noise or irritable noise created by electronics in general. This system costs $4,500, which is well over the cost of our system. Silentium claims that its products can cancel frequencies from low range signals up to 1800 Hz. Its systems can also counteract multiple frequencies rather than just a single tone.
The re are a wide array of noise cancellation headphones in today’s market.
There is a pair of MDR ZX110NC SONY headphones that claims to reduce up to 95 percent of ambient noise produced by the office, airplanes, and trains. These headphones are at a low cost of around $40. These headphones are for the specific use of providing comfort for travel. Most of the headphones on the market that are used to give quality sound and combine the use of passive and active noise cancellation start at $250.
Our system helps to provide an affordable solution to general noise cancellation.
The system we have proposed will cost less than many of the noise cancellation systems available, while still providing the ideal quiet environment. Our device is expandable beyond headphone application, but each system will be more affordable than existing active noise cancellation systems.
‘
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Design
Most noise cancellation systems use microphones, FIR filters, cancellation algorithms, and speakers or headphones. We will be following this model that has succeeded in the past.
Figure 1 shows the two inputs and outputs for microphones and one output for the headphones. The board will be programmed through a PC using the JTAG to USB communication. The board will also be connected serially to the computer for testing purposes, and oscilloscopes will be used on the microphone communication lines. The inputs for the microphones will be the inputs and outputs for the bidirectional communication required by I2S.
Figure 1: Level zero block diagram representing the FPGA, inputs, and outputs.
Overall system and Subsystems
Since we will not have the funds to produce an ASIC design, the only viable processor solutions for this project are a DSP or an FPGA. One advantage of an FPGA is that it can perform multiple operations at a time due to its use of parallelism, where a
DSP processor performs one instruction at a time. However, as filter order increases, the delay in the FPGA increases due to register-to-register delay, and the speed increase due to parallelism with be overcome. Research showed the tradeoff here and compared an implementation of a filter of order ten implemented on an FPGA processor versus on a DSP processor of similar specifications [8]. The DSP processor took 2.3
µs
Adaptive Noise Cancellation System P a g e | 7 to update the weights on the adaptive filter and the FPGA took 67 ns [8]. This difference is astounding for a filter of order ten.
From research it is has been determined that we will require a filter order in the range of 8 to 64 [8]. Most FPGAs will have the resources to do the computations for filters of these orders faster than similarly specified DSPs. Also, if the chosen FPGA has on-board DSP slices, it will produce the desired results very rapidly. For these reasons, we have decided an FPGA will be the right fit for our implementation of an adaptive noise cancellation system.
We also believe that an FPGA implementation will give our design a higher degree of flexibility. This flexibility will become especially important if we are able to expand the application of our design to noise cancellation in complex environments such as small rooms. This complex implementation would likely require a large number of parallel computations in order to counter complex combinations of noise signals in real time.
There are a wide variety of FPGAs in today’s market. The following table was produced by obtaining a subset of FPGAs that have successfully been used in existing adaptive noise cancellation systems or projects.
Board Number of
Multipliers
Slices LUTs DSP
Slices
Cost
Xilinx Virtex-2 Pro
XC2VP30
136 (18 x 18) 13,696 27,392 No $1,799 or $399
(Academic)
64 (25 x 18) 17,280 Xilinx Virtex-5
XC5VLX110T
Xilinx Spartan-3E
XC3S500E
(Starter Kit)
20 (18 x 18) 4,656
69,120 Yes $2,199 or $799
(Academic)
9,312 No $199
ALTERA DE2
CYCLONE-II
EP2C5
13 (18 x 18) 2304 (est) 4,608 No $137
NI cRIO-9104 96 (18x18) 14,336 28,672 No $3,559
The consensus of the existing research is to use an LMS adaptive filtering algorithm that utilizes FIR filters [2, 11, 13, 15, 16]. The desired FPGA must have enough slices to hold a high order FIR filter implementation. The higher the filter order, the more slices will be required. The desired FPGA must also be able to multiply and add rapidly to perform the LMS filter iterations at a desired rate. From the research, it
Adaptive Noise Cancellation System P a g e | 8 was determined that for a filter length of 512, around 1600 slices are needed for a board with no dedicated DSP slices [2, 5, 8]. Dedicated DSP slices on an FPGA contain at least one multiply and accumulate unit. These units are capable of performing consecutive multiplication and addition operations quickly and efficiently. These units will therefore directly aid in the speed of an LMS algorithm due to the numerous multiply and accumulate operations required. The desired filter length will be based on the input frequency range of the noise signals, which the system is capable of countering.
Generally, an FIR filter of order 8 to 64 was used in the successful projects to cancel a
60-Hz signal. This gives a range of around 180 to 330 slices required for the general boards and even fewer slices needed for a board with dedicated DSP slices. The research also suggests that the reduction of resources utilized by the board from using
DSP slices will reduce the total power consumption by up to 50% [8]. Speed is the most important requirement our system needs, and the Xilinx Virtex-5 provides the extreme speed and efficiency needed to satisfy all the system requirements while leaving room to successfully grow into a more complex cancellation system.
Figure 2 shows that the FPGA will take in digitized data points from the external
MEMs based microphone and give them to the DSP slices inside the FPGA (The MEMs microphones have analog to digital converters to give the digitized data desired.). The
FPGA will use the LMS algorithm to produce a series of coefficients, W
N
' s , from the input signal and the feedback signal. These coefficients will be given to the DSP slices to perform calculations on the input signal to produce the counteractive signal. The combination of the signals will be given to the audio codec, which sends the analog version of the output signal out to the headphones. The other MEMs based microphone inside the headset will act as a feedback system to send the signal back into the FPGA, which will then use the LMS algorithm to generate, if necessary, the next iteration of the coefficients to produce even less error. This process is repeated until the error reaches the desired threshold.
Both microphones use the I2S protocol to talk to the FPGA through the GPIO pins. This produces the need for two microphones and one line output for the headphones.
Adaptive Noise Cancellation System P a g e | 9
Figure 2 : Level one block diagram of the Virtex-5 FPGA.
Figure 3 shows how the DSP slices can be set to easily multiply and accumulate results, which is what is needed for the LMS algorithm to work efficiently. The DSP slices will calculate the filter error from the new filter weights generated by the processor and the input signal.
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Figure 3 : Level three block diagram of a general DSP slice used as a Multiply Accumulate Unit.
External Hardware
Two microphones are needed for the system, one to detect the noise, and another to collect the feedback signal. Current noise cancelling headphone systems include these microphones mounted on the inside and outside of the headphone earcup. The external microphone picks up the noise signals such as traffic or conversations. The feedback microphone is placed inside an ear-cup of the headphones to create a feedback loop for active noise control. In this project, the outer noise detection microphone will be an external, handheld microphone. The table below shows some of the microphones that are being used in the noise cancellation applications today.
Adaptive Noise Cancellation System P a g e | 11
Microphone Frequency Response Output SNR
High SPL Analog Microphone 6 Hz -20 kHz Analog 63
Ultra-low current-low noise microphone 75 Hz - 16 kHz
Omnidirectional MEMS Microphone with
Bottom Port
60 Hz - 15 kHz
Analog
I2S
64
61
Low-Noise Microphone 45 Hz – 20 kHz I2C 65
The best-suited microphone for this project is the Omnidirectional Microphone with Bottom Port and I2S output from InvenSense. The frequency response and the signal-to-noise-ratio (SNR) were two major factors in deciding which microphone would work best for this project. Based upon our specifications, we decided that a microphone with a frequency response from 60 Hz to 15 kHz would be sufficient. In addition, the
SNR should be around 60 dB to sufficiently pick up the sound of normal human voice over three feet [31]. Therefore, the Omnidirectional Microphone from InvenSense was a good match for this project. The microphone’s sensitivity to a sound source does not change with the position of the source due to its omnidirectional nature. The I2S output interface also allows the microphones to be connected directly to the FPGA without the need for an audio codec in the system. Finally, the frequency response of this microphone is between 60 Hz to 15 kHz, which is sufficient enough to produce an accurate representation of the original sound.
The headphones needed for the noise cancellation system should have the capability of handling a frequency response from 20 Hz to 20 kHz. The ear-cup of the headphones should also be able to fit the feedback microphone. The table below shows some of the headphones which were considered.
Headphones Frequency
Response
Max Power
(mW)
Sensitivity
(dB)
Superlux HD668B Dynamic Semi-
Open Headphones
10 Hz – 30 kHz 300 98
Superlux HD681
Superlux HD661
10 Hz
– 30 kHz
10 Hz – 20 kHz
300
200
98
102
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The Superlux Dynamic Semi-Open Headphones have been chosen for this project. They have the capability of reproducing frequency signals from 10 Hz to 30 kHz. This will allow them to produce an output signal with low noise distortion.
Moreover, since the microphone is only 4.72 mm x 3.76 mm x 1 mm, it should be able to be hidden inside one of the headphones’ ear-cups.
Adaptive Algorithms
Adaptive noise cancellation algorithms are implemented in many different types of products. These products range from small hearing aids to the large systems used in vehicles such as cars or airplanes. Although these applications seem quite different, the backbone of the algorithms is very similar. Two of the main algorithms which were encountered during the research process were the IIR (infinite impulse response) filter and FIR filter algorithms. The IIR filter algorithm is not often used in current products
[12]. After more research and development, however, this algorithm may prove to be the best solution. The vast majority of the articles on adaptive noise cancellation found during the research process relied solely upon the FIR filter algorithm. All of these articles also used some variation of the LMS algorithm to allow the FIR filter to be adaptive [2, 11, 13, 15, 16].
An algorithm must be selected based upon its ability to counter-act the expected noise signals. All of the algorithms found during the research process seemed to be capable of counter-acting input frequencies within the human audible frequency range,
20 Hz to 20 kHz. The algorithms were generally ranked by the speed at which they could converge upon the frequency of the noise signal. In this project, the output counter-signal can be muted at start-up until the algorithm converges. Therefore, the start-up convergence delay should not be perceptibly long, but it is not required to be imperceptible by the user. The speed at which the system reacts to impulsive noise during normal operation could become an issue. The selected algorithm should have the fastest possible convergence time given the resources available in the selected processor.
The selected algorithm must have been previously documented and tested. In order to deliver this product by the prescribed deadline, a large portion of the work on this project must be allocated to the implementation process. Some research will be necessary, but there is not a sufficient amount of time to develop and test a new algorithm.
The FIR filter algorithm combined with the LMS algorithm has been selected for this project. The extensive amount of available documentation for these algorithms made them the optimal choice. These algorithms have been shown to be robust in similar products. The LMS algorithm can also be expanded and improved for more complex implementations.
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The success of this project relies heavily on a correct application of the FIR filter and LMS algorithms. The remainder of this section will introduce the concepts and mathematical computations behind these algorithms.
The input of the FIR filter algorithm is a continuous stream of sample amplitude values. Figure 4 shows a sampled representation of a sinusoid signal.
This “stem” plot shows the spacing and values of the samples. The circles at the end of each stem mark the amplitude value, which will be sent to the FIR filter.
Figure 4: Stem plot of a sinusoidal signal
The FIR filter algorithm calculates a new amplitude for the most recent sample.
This new amplitude is based on the value of the most recent amplitude and the amplitude of several samples processed before the most recent sample. The number of previous samples used in the calculation is called the order of the filter. A third order filter would use Equation 1 to calculate the new amplitude value. 𝑦[𝑛] = 𝑊
0 𝑥[𝑛] + 𝑊
1 𝑥[𝑛 − 1] + 𝑊
2 𝑥[𝑛 − 2] + 𝑊
3 𝑥[𝑛 − 3] (1)
In Equation 1, x[n] represents the most recent input sample, and x[n
– 1] represents the sample before the most recent sample. The updated sample amplitude is represented by y[n]. The “W” values are the scaling factors. Notice that every term in the FIR filter equation requires one multiplication and one addition. When an algorithm is tuned for a specific computational application, the number of operations per iteration
Adaptive Noise Cancellation System P a g e | 14 is very important. The number of operations directly affects the overall speed of the implementation. The order of the FIR filter, therefore, must be carefully selected in order to meet the engineering requirements of the project. Research suggests that the order of a FIR filter used in this type of application could be from eight to sixty-four [8].
In this project, the order of the filter will likely be set as a parameter, which can easily be changed during testing.
Figure 5 shows a block diagram for a hardware implementation of a third-order
FIR filter.
Figure 5 : Block diagram of a third-order FIR filter implementation
In an FPGA, flip-flops can be used as unit delay blocks, and MAC (multiply accumulate) modules can be used for the multiplication and addition operations.
Multiplication operations will likely be slower in FPGAs that do not have available MAC modules. These FPGAs would therefore need to use lower order FIR filters in order to achieve the same computational speed as FPGAs with MAC modules.
If the filter did not need to be ada ptive to the input signal, the “W” values could be constants saved in a memory module. Adaptive filters, however, require an algorithm
Adaptive Noise Cancellation System P a g e | 15 which can adjust these scaling values until the system converges on the desired output.
The least mean square algorithm is commonly used for this function. Figure 6 shows a top-level view of the full adaptive filter algorithm.
Figure 6: Top-level block diagram of the adaptive noise cancelling algorithm .
In Figure 6 , x[n] represents the input signal from the noise source, and y[n] represents the output counter-signal of the noise cancellation system. The error signal, e[n], is taken from a sensor placed in the region where the noise is to be cancelled. The
LMS algorithm attempts to minimize the amplitude of the error signal by adjusting the
W n
scaling values. The standard form for the LMS algorithm is given in Equation 2.
𝑊[𝑛 + 1] = 𝑊[𝑛] + 2 ∗ µ ∗ 𝑒[𝑛] ∗ 𝑥[𝑛] (2)
Notice that this standard form requires three multiplications and an addition.
Since multiplications can be computationally expensive, several variations of the standard LMS have been developed which minimize the number of required multiplications while maintaining an acceptable adaption speed. Two common LMS variations are the sign-data least mean squares (SDLMS) and the sign-error least mean squares (SELMS). The SDLMS algorithm replaces x[n] with the sign of x[n], and the
SELMS algorithm replaces e[n] with the sign of e[n].
The step size µ is often chosen to be a small power of two in order to turn its multiplication operation into a simple bit shift.
Some articles also suggested that µ should vary with the input in order to improve performance. Many descriptions of these types of modifications were found during the research process [16]. Specific modifications were often mapped to specific types of
Adaptive Noise Cancellation System P a g e | 16 complex noise. For example, some suggested algorithm modifications specifically targeted impulsive noise [14].
Since the exact algorithm performance requirements for this project are not yet known, some experiments will need to be performed in order to find the level of algorithmic complexity necessary to meet marketing requirement four. The selected algorithm will also need to be tailored to exploit the strengths of the selected processor.
These strengths will be better understood after the results of the FPGA risk investigation have been presented.
Circuits and Assemblies
An essential part of the project is to understand the pin configuration and the functional descriptions of the microphone that will be used. Figure 7 shows the pin configuration of the Omnidirectional MEMS Microphone.
Figure 7: Pin configuration of the microphone.
Pin 1 is a serial data clock used for I2S interface. Pin 2 is a serial data output pin that is also used for I2S interface and requires a 100 kΩ pull-down resistor. This pin tri-states when not actively driving the appropriate output channel. Pin 3 is a serial data-word for
I2S interface. Pin 4 is a left/right channel select. The microphone outputs its signal in the left channel of the I2S frame when pin 3 is set low, and the microphone outputs its signal in the right channel when pin 3 is set high. Pins 5, 6 and 9 are required to be connected to ground on the PCB. Pin 7 is a VDD pin that is used to provide power between 1.8V and 3.3V. The datasheet specifies that pin 7 should be decoupled to pin
6 with a 0.1µF capacitor. Pin 8 is a microphone enable. When set low (ground), the microphone is disabled and put in power-down mode. When set high (VDD), the microphone is enabled.
As can be seen in Figure 8, the microphones will communicate with the board using the I2S protocol. Each microphone will be connected to GPIO pins on the board
Adaptive Noise Cancellation System P a g e | 17 by three wires: SCK (clock), WS (serial-data word select), and SD (output data pin).
There will also be a 100kΩ pull down resistor on the SD wire.
Figure 8 : Block diagram of the I2S hardware needed.
Figure 9 shows the schematic of the USB controller on the Virtex-5. These
GPIO pins will be used to connect the microphones to the FGPA. These pins could also be configured to use the I2S protocol if needed. There are two pins dedicated for communication purposes, pin 39 and 40. Pin 39 will act as the SCK clock for our I2S communication, and pin 40 will act as the data communication pin. The data word select will be connected to another of the GPIO clocks.
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Figure 9 : USB Controller on the Virtex-5.
Figure 10 shows the schematic of the on-board Audio Codec of the Virtex-5
FPGA. This audio codec will use the line out to send the counter signal to the connected headset. The schematic also shows two line-in inputs and two microphone inputs. This provides the ability to use standard microphones through the audio codec if needed.
Adaptive Noise Cancellation System P a g e | 19
Figure 10 : Audio Codec on the Virtex-5.
User Interface and Controls
Figure 11 : User interaction state diagram.
The user interface is designed to be very simple as can be seen from Figure 11 .
The user will be to take the product as is and press a single button to turn the system on. The system is designed to work when powered on based on the programming done in factory. If something were to go wrong or the system were to freeze, the user has access to another button to reset the system. The system will reset within ten seconds
Adaptive Noise Cancellation System P a g e | 20 upon the reset button being pressed. The user can press the same switch that powered on the system to power off the system when desired.
Figure 12 : Prototype of user interface.
A prototype of the system can be seen in Figure 12 . This prototype demonstrates the basic user interface. The actual placement of the buttons may be different in the finished product. The casing will also have holes to allow for the power cable, headphones, and the wires connecting the microphones.
Engineering Standards
I2S is an electrical serial bus interface that is used for connecting digital audio devices together. The I2S design handles audio data separately from a clock, which precludes time-related errors that cause jitter. The I2S design consists of three serial bus lines: a line with two time-division multiplexing (TDM) data channels, a word select line, and a clock line.
We will be using a 3.5-mm headphone jack to interact with the product ’s provided headset. The tip of the 3.5-mm male jack from the headset represents left audio, the center portion represents right audio, and the base represents ground.
We will also be using a 120-V rms
, 60-Hz AC source through an AC adapter to provide the 5 V necessary to power the FPGA through a DC barrel jack.
We will be using two main software tools, the Xilinx ISE tool suite and MATLAB.
MATLAB will be used to simulate the algorithm and provide us with simulation results that can be used for testing and debugging purposes. The Xilinx ISE tool suite will be used to write the algorithm in VHDL, place the algorithm on the board, and assign the input and output pins.
Multidisciplinary Aspects
This project helps to incorporate our foundation of Computer Engineering and our understanding of Electrical Engineering fundamentals to produce a working adaptive noise cancellation system. We will utilize our Electrical Engineering circuit design knowledge to design and test a printed circuit board in order to use the MEMs based microphones. We will use surface mounting techniques to place and solder the components needed to have the microphones function properly. Our knowledge and
Adaptive Noise Cancellation System P a g e | 21 understanding of digital signal processing will help us design and create the necessary
FIR filters for adaptive noise cancellation. Our Computer Engineering background will be useful for designing optimal VHDL descriptions of state machines for programming various portions of the FPGA.
Background
The Digital System Design, VHDL, Digital Signal Processing, and Interface and
Digital Electronics courses helped give the strong foundation of knowledge required to implement this project. Members of the project team also have recreational and work experience in the implementation of digital designs in FPGAs.
Outside Contributors
Dr. Marcin Lukowiak has donated a Virtex-5 FPGA until the end of the project.
Dr. David Borkholder has given us a supply of MEMs based microphones.
Rick Tolleson has donated a pair of Superlux Dynamic Semi-Open headphones until the end of the project.
Jeffrey Lonneville will be aiding in the surface mounting process for the microphones and other surface mounted parts.
Dr. Vincent Amuso has offered his services to act as an audio consultant for the duration of our project.
Constraints and Considerations
Extensibility
The project is about designing a system to counteract single tone frequencies of a desired range. The project may expand to counteracting multiple tones inside the desired frequency range for the microphone and headset scenario. This will be adaptable to work in a scenario using a microphone and speakers like the ones used in cars or airplanes. The system may also be adaptable in the sense that it can be optimized to be used well in an array of environments, from a noisy lab environment to a noisy street environment. The portability, competitive price, and ease of use of the system could make it a viable option for almost any scenario where one would want to use adaptive noise cancellation.
Manufacturability
The system does not have very many components. Each system contains an
FPGA, two microphones, and a headset. Once we have created the PCB layout for the microphones, the system will be very easy to manufacture on a large scale. The manufacturing process will consist of ordering the PCB boards, doing the surface mounting, programming the FPGAs, and placing the systems in a hard encasing. Once this is done, each system should go through a series of defined tests. This carefully
Adaptive Noise Cancellation System P a g e | 22 targeted testing will ensure maximum reliability with minimal testing required. Once they have passed these tests, they will be in a shippable state. The cost of each unit will be under $1000 dollars for a customer in academia and $2500 for non-academic customers. The cost estimate will be reduced when the systems are manufactured in bulk. Our system as a whole will be very easily manufactured at a minimal cost while maintaining high system reliability.
Societal Context
Noise cancellation applications are growing in popularity in our society in recent years. These systems help to reduce or remove distractions from an individual looking to concentrate on other tasks. Noise cancellation can provide a quieter work environment or can provide that extra external noise reduction that gamers seek.
Unfortunately, noise cancellation systems are not cheap, whether it is a headset or a full system with speakers. Our active noise cancellation system will give people the opportunity to take advantage of such systems for a reasonable price.
Passive noise cancellation systems are also growing in popularity. These nonintrusive designs can be implemented into the architecture of a home while keeping the home aesthetically pleasing. Many of these designs are used to cancel railroad noises or general traffic noises in congested areas.
Reliability
The reliability of the full system is determined by the individual reliability of the components and interconnections. The FPGA, microphones, and headphones have been deemed reliable by their respective manufacturers. Since this system will not be subjected to any unusual environmental conditions, the lifetime of these components will be the same as any marketable processing units or audio electronics.
The PCB will be manufactured by a reputable manufacturer with proven reliability, and the components will be soldered onto the PCB by an experienced engineer using state-of-the-art equipment. All internal and external PCB connections will be thoroughly tested before it is integrated into the system.
The FPGA board contains an integrated headphones jack and general purpose
I/O pins. The reliability of headphones interconnection is guaranteed by the quality control personnel of the FPGA and headphones manufacturers. The microphones will be connected to the FPGA’s general purpose I/O pins using jumper wires designed for this application.
Health and Safety Issues
One goal of our project is produce a safety conscientious product that prevents any potential injury. There are many electrical components in our system that can become hot and may contain sharp edges or points. To avoid the risk of damage when
Adaptive Noise Cancellation System P a g e | 23 falling on the product or accidentally touching a potentially dangerous surface, the system will be enclosed in a hardened case.
Another safety concern would be having the device emit signals that are harmful to the human ears. We have provided a requirement that no audible tones will be produced that could potentially harm or produce discomfort to the user’s ears.
Economic Context
Many of the existing noise cancellation systems are expensive. We aim to provide an economically system that a more general audience can afford. The product will be an affordable home system that can be used to provide a quiet work environment by cancelling out any of the noisy electronics running in a room or any other noise sources. This system will also be very affordable to universities or schools to create quiet work areas where students will be able to study without the distraction of outside noise.
Intellectual Property
A preliminary patent search revealed several existing patents which are related to the system designed in this project. These results are given below.
United States Patent 8,750,531 entitle d “Active Noise Cancellation” covers digital and analog systems, which generate a signal to counter-act a noise signal. The LMS algorithm is mentioned as a possible part of the adaptive filter in the system. This patent does not cover the use of the algorithm, but the described algorithm implementation is very similar to the system described in this project. This patent would likely block the transformation of this project into a marketable product.
United States Patent 8,4 11,872 entitled “Adaptive control unit with feedback compensation ” covers a wide range of adaptive filters. The LMS algorithm and its variations are mentioned as possible control algorithms. A system with a feedback microphone is also mentioned. This patent would also likely block this project team from selling the system built in this project.
Adaptive Noise Cancellation System P a g e | 24
Cost Estimates
Component
Description
Cost
Omnidirection al Microphone
$3.23
Our Cost
$0.60
Availability
Surface-mount
PCB
Capacitors
$0.43
Surface-mount
PCB Resistors
$1.15
Superlux
HD668B
Headphone
$43.82
$0.43
$1.15
$0.00
In stock from InvenSense. http://store.invensense.com/Produ ctDetail/INMP441ACEZ-
InvenSense-Inc/485883&pid=1135
Part Number: INMP441ACEZ
Lead Time: 4-5 business days
In stock from DigiKey. http://www.digikey.com/productdetail/en/0/1276-1506-1-ND
Part Number: 1276-1506-1-ND
Lead Time: 3 business days
In stock from DigiKey. http://www.digikey.com/productdetail/en/0/MCS0402-100K-CFCT-
ND
Part Number: MCS0402-100K-
CFCT-ND
Lead Time: 3 business days
In stock from Amazon. http://www.amazon.com/Superlux-
HD668B-Dynamic-Semi-Open-
Headphones/dp/B003JOETX8
Part Number: NA
Lead Time: 3-5 business days
Adaptive Noise Cancellation System P a g e | 25
Xilinx Virtex-5
XC5VLX110T
$2,199.00 or
$799.00
(Academic)
$0.00
$2.18
In stock from Digilent. http://www.digilentinc.com/Product s/Detail.cfm?Prod=XUPV5&Nav1=
Products&Nav2=Programmable
Part Number: 6003-410-008P-KIT
Lead Time: 2-3 business days
Total Cost $2,247.63 or
$847.63
Adaptive Noise Cancellation System P a g e | 26
Testing Strategy
Engineering
Requirement
C
C
Test Description
Unit Testing
The purpose of this test is to test the
Matlab version of the LMS algorithm.
Since the Matlab software has already been verified, the algorithm will be verified by code inspection by a team member who did not write the Matlab code.
The purpose of this test is to test the
Matlab version of the FIR filter. Since the
Matlab software has already been verified, the filter will be verified by code inspection by a team member who did not write the Matlab code.
Expected Result
The Matlab code should accurately model the operations required by the
LMS algorithm.
The Matlab code should accurately model the operations required by the FIR filter.
Pass/Fail
C
C
C, D
N
N
The purpose of this test is to test the HDL description of the FIR filter using a VHDL testbench. Stimulus and expected output data can be generated by the Matlab model.
The purpose of this test is to test the HDL description of the LMS algorithm using a
VHDL testbench. Stimulus and expected output data can be generated by the
Matlab model.
The purpose of this test is to test the
Audio Codec on the board. A sinusoid will be generated and passed through the
Audio Codec to verify that it is functioning as expected.
The purpose of this test is to test the I2S protocol library using a VHDL testbench.
The testbench will act as a SLAVE to ensure the communication protocol is functioning properly.
The purpose of this test is to test the microphones use of the I2S protocol.
This will be accomplished by applying a
2.82 MHz clock to SCK, applying a 44.1
KHz clock to WS, driving L/R high, and driving CHIPEN to high. The clocks will be generated by the FPGA. The output will be monitored on an oscilloscope.
The output HDL description matches the expected output.
The output HDL description matches the expected output.
The sinusoid will be heard through the plugged in headset.
The I2S protocol functions as expected.
The microphone is using the
I2S protocol as expected.
Adaptive Noise Cancellation System P a g e | 27
C
C
N
C, K
D
A
B
D
Integration Testing
The purpose of this test is to test the hierarchical MATLAB model. This will be accomplished through testing all the modules of the system by driving the input signal.
The purpose of this test is to test the integrated HDL description of the noise cancellation module using a VHDL testbench. Stimulus and expected output can be generated by the Matlab model.
The purpose of this test is to test the integration of the microphone and the
Audio Codec. The FPGA will be
The system as a whole functions as expected.
The output HDL description matches the expected output. configured to send the audio data from the microphone directly to the Audio
Codec.
The purpose of this test is to ensure that the algorithm works on the FPGA. The adaptive noise system as a whole will be connected and tested for noisy input signals in the frequency range of 40 Hz to
80 Hz. At least 20 individuals will be polled to determine if there is a noticeable drop in volume.
Acceptance Testing
Audio detected by the microphone is passed through the Audio Codec without distortion.
There has been a noticeable drop in volume.
The purpose of this test is to test the successful output of a signal to the headphones. The input signal will be sent through the Audio Codec, through the 3.5 mm jack, and to the headset.
A clear audio signal is produced by the headset.
The purpose of this test is to test the accessibility of the on/off switch. The switch will be pressed to test it.
The purpose of this test is to review the system documentation for completeness.
This includes comments in the MATLAB code and the VHDL description.
The purpose of this test is to ensure the
3.5 mm headphone jack is accessible to the user. Plug the headphones in to ensure the 3.5 mm jack is accessible to the user.
The system powers on/off as expected.
The document is thorough.
The 3.5 mm jack is accessible to the user.
Adaptive Noise Cancellation System P a g e | 28
E
F
F
F
H
I, J
L
M
Review the necessary components and ensure the total cost of the system does not exceed $1000.
This test verifies that the system dimensions are within the required dimensions. The dimensions of the case will be measured to ensure the system will fit in the enclosure.
This test verifies the safety of the system.
The enclosure will be checked for sharp edges or exposed wires.
This test verifies that the system is aesthetically pleasing to a majority of potential users. At least 10 individuals will be polled to gather feedback on the aesthetics of the system.
The purpose of this test is to test that the
20 Hz sine wave and a 20 kHz sine wave can be detected by the microphones and monitored on an oscilloscope.
This test verifies that the system's total energy usage is within the required usage. A watt-meter will be used to monitor the instantaneous power and the total energy in kilowatt-hours over a
12 hour period.
This test verifies the noise range countered by the system. The system will be tested with frequencies in the band 40
Hz to 80 Hz, in increments of 5 Hz, to ensure cancellation of the signals within 3 seconds. This will be measured using a stopwatch.
This test verifies that the gain of the system output is below the required threshold. Sensors will be used to measure an ambient noise and the output signal for the frequency band of
20 Hz to 20 kHz. This will be used to calculate the output dB of the system.
(The sensor type is still to be determined, but we could possibly use a standard computer microphone.)
The system cost does not exceed $1000.
The enclosure is no more than two inches larger than the FPGA on any side.
The enclosed system has no sharp edges or exposed wires.
At least 80% of the individuals polled agree that the system is aesthetically pleasing compared to a board with no enclosure.
The sine waves are successfully viewed on the oscilloscope.
The instantaneous power is less than ten watts and the total energy in kilowatt-hours is less than 0.3 kilowatt-hours.
The system successfully counters all tones in the frequency band within the
3 second window.
The system produces output signals that are below 85 dB.
Adaptive Noise Cancellation System P a g e | 29
C This test verifies that the functional algorithm counter frequencies in the band 40 Hz to 80 Hz. This will be accomplished by sending these frequencies through our functional
MATLAB simulation and listening to the output file.
There has been a noticeable drop in volume.
Initial algorithm testing will be performed in MATLAB. MATLAB has the necessary tools to build and test DSP algorithms quickly and comprehensively. Also, due to current coursework, all members of the project team will have specialized training in the implementation of FIR filters in MATLAB by the end of the first phase of the project.
Initial MATLAB models will be used to ensure that the noise cancellation system can successfully be implemented in a virtual environment. These initial models will focus on simply modeling the necessary mathematical operations. If necessary, more complex models can then be created, which more accurately model the FPGA implementation of the algorithms. This model could be used to generate files containing the expected output values of sub-modules given a generated input signal. These simulation results may be helpful during the hardware implementation debugging process.
The initial MATLAB model will be constructed in the first phase of the project.
Additional, more complex models will be built in support of the hardware development.
In general, algorithm modifications will be simulated in MATLAB before a hardware implementation is attempted.
The algorithm will be written in VHDL and placed onto the board using Xilinx ISE tools. The actual VHDL implementation of the algorithm will be tested pre-synthesis and post-synthesis with ideal test benches using a set of carefully selected test cases.
This will be done upon completion of the VHDL code for the algorithm. Once this is in place, a computer will be connected serially to the board to capture data at all the input and output locations. A user readable interpretation of the data will be designed to observe the original signal coming in, the error signal coming out, and the feedback signal for each iteration. This strategy will ensure that any errors at any stage of the process can be found. If the data coming in originally are not what is desired, it may be a reflection of an incorrect use of I2S. If the signal output of the FPGA does not have corrected weights reflected on the LMS algorithm, it will show an incorrect implementation of the algorithm. Lastly, if the feedback signal appears to be incorrect, it will again point to an issue with the I2S protocol. The signals will also be captured by an oscilloscope to help monitor the analog response of the system.
Adaptive Noise Cancellation System P a g e | 30
Risks
Algorithm
Although the general structure of the algorithms needed for this project is known, the exact variation of the LMS algorithm which will be used is uncertain. This decision cannot be made until the information found in the investigation of the FPGA risk has been combined with the findings of this investigation. The LMS algorithm variation will be selected based upon the strengths and weaknesses of the target FPGA.
Board
The testing strategies proposed for the interaction with the FPGA have not been confirmed to provide useful feedback that will help to ensure the project is functioning properly or for debugging purposes. This uncertainty will be handled during actual project implementation. The board also uses fixed point arithmetic, which will lead to small round off errors. Research has shown a potential fix to this issue by shifting the data left and performing the operations and shifting back right the same amount after the computations are finished [9]. The error should be small enough that it will not affect the signal enough to be noticeable, but it is something to keep in mind.
Interface
The user interaction with the system could also provide some difficulty. The object here initially is to make the interface very simple. The system should automatically work for a desired frequency range without user configuration. As the project grows, the user may interact with the system in such a way that the user will be able to choose the correct environment for optimal conditions. This more complex interface could pose more risk to the project. Therefore, this will be monitored and noted in the later progressions of the project.
Adaptive Noise Cancellation System P a g e | 31
Milestone Chart
Milestone
Board Choice : Choosing the correct board.
Finished Project Proposal : Finishing the project proposal documentation.
Board Functions : Learning all the functions of the board needed for our system.
Hardware : Researching and acquiring necessary hardware.
PCB Layout : Finishing and ordering the PCB layout for the microphones.
Functional Algorithm Modeling : Modeling the functional algorithm in MATLAB
Learn Audio Codec Functions : Determining how to enable and interact with needed registers to accept digital input and output sound through the line output.
Surface Mounting : Contact EE faculty members to arrange surface mounting for PCB.
Surface Mounting : Complete surface mounting for
PCB.
Generate Test Data : Use MATLAB to generate potential test data for the HDL descriptions of the
LMS and FIR filters.
Determine DSP Slice Functionality : Learn if it will be necessary to tell the FPGA to use the DSP slices and how to do it.
Behavioral Microphone HDL Description : Finish
HDL description of the microphone.
FIR Filter HDL Description : Finish the HDL description of the FIR filter.
LMS Module HDL Description : Finish the HDL description of the LMS Module.
I2S HDL Description : Finish HDL description of
I2S protocol.
Audio Codec HDL Description : Finish HDL description of Audio Codec.
Top Level ANC Module : Design the top level state machine to control all the modules.
Testing PCB : Testing the PCB for desired behavior and issues with soldering.
Audio Codec : Test Audio Codec with Sinusoid
Due Date Team Member
Responsible
1 May 2015 Michael Loper
18 May 2015 Michael Loper
18 May 2015 Michael Loper
18 May 2015 Irtaza Ahmed
27 Aug 2015 Irtaza Ahmed
8 Sep 2015 Michael Foster
18 Sep 2015 Michael Loper
22 Sep 2015 Irtaza Ahmed
29 Sep 2015 Irtaza Ahmed
29 Sep 2015 Michael Foster
3 Oct 2015 Michael Foster
3 Oct 2015 Michael Foster
3 Oct 2015 Michael Foster
3 Oct 2015 Michael Loper
3 Oct 2015 Michael Loper
3 Oct 2015 Michael Loper
3 Oct 2015 Irtaza Ahmed
6 Oct 2015 Irtaza Ahmed
10 Oct 2015 Michael Loper
Adaptive Noise Cancellation System P a g e | 32
I2S Testing : Testing the I2S communication between the board and microphones are working properly.
Testing HDL Descriptions : Finish testing the HDL descriptions using Testbenches.
Board Implementation : Getting the HDL descriptions implemented on the board.
19 Oct 2015
20 Oct 2015
20 Oct 2015
Michael Loper
Irtaza Ahmed
Michael Foster
Board Testing : Testing the HDL descriptions on the board.
27 Oct 2015 Michael Foster
System Testing : Testing the system as a whole. 3 Nov 2015 Michael Foster
Finalizing Poster Board and Documentation : 1 Dec 2015 Michael Loper
Ensuring all documentation for the project as a whole is ready and checked over.
Adaptive Noise Cancellation System P a g e | 33
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Adaptive Noise Cancellation System P a g e | 34
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Adaptive Noise Cancellation System P a g e | 35
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