This document describes a project to develop an accelerometer-based contact microphone system to enable voice communication in high noise environments. The system uses accelerometers placed on the head to capture vocal vibrations, a Teensy board to perform signal processing including fast Fourier transforms and filtering, and voice recognition software to match the vocal signals to text. The goal is to filter out background noise so voices can be clearly understood. Potential applications include military, industrial, firefighting and other fields where loud noise makes communication difficult. The system was tested in various noisy conditions and showed effectiveness in distinguishing voices from background noise.

1. Voice Recognition Accelerometers
Project Advisor: Dr. Martin Kocanda
Project Contributors: Alexander Freeland
Nathan Glatz
Kevin Dotseth
Chad Strick
Tristan Sprowls
Adam Zobrist
Contact: AlexLFreeland@gmail.com

2. Abstract:
Imagine fighting on a battle field, running into a burning building, or working in a steel
mill. What do they all have in common? They are extremely loud environments. Poor
communication can be deadly in these environments. Communication in high noise
environments has always been a challenge. Accelerometers have the potential to change that.
Accelerometers are nothing new and have been around since the early 1900’s; however,
accelerometers have only recently become sensitive enough to be used as contact microphones.
This application uses extremely sensitive accelerometers as contact microphones. Accelerometer
based contact microphones are promising in military and civilian applications. It is used in high
noise environments and filters out all unwanted noise. We developed an accelerometer based
microphone to make communication easier in high noise environments. A sufficient board was
researched and applied which was able to calculate the Fast Fourier Transform and transmit the
outputted data to voice recognition software. One problem with using accelerometers for this
type of application is that the accelerometers add distortion to the voice making it difficult to
understand. In order to counteract this, we coupled our contact microphone with voice
recognition software with high word confidence to ensure the communication is accurate. Once
completed, the project scope was expanded to open a platform for research in efficiency
improvement and word recognition.
Introduction
Motivation and Application:
High noise environments make any communication difficult if the background noise is
too intense. This can be dangerous or just a nuisance depending on the environment. Whether the
application is for a work setting or recreation, communication is essential in almost every
environment. Using an accelerometer to develop sub vocal microphones, the background noise
was minimized to ensure efficient communication was occurring. The noise was filtered, only
allowing the pure voice to be passed so that individuals are able to communicate clearly without
screaming or repeating themselves. This can be crucial in many environments such as industrial
factories, military operations, and commercial settings.
Goal of design:
While contact microphones are effective at reducing outside noise, distortion is
introduced which may cause error in effectively communicating. By combining a contact
microphone with voice recognition software, we aimed to ensure effective communication.
Current existing prototypes have been proven to be ineffective based on their contact microphone
location which will pick up unwanted noise sources. To improve this, our project consisted of
sufficient research on the location of the accelerometer to ensure the best possible vibrations
were being passed to the software.
Background:
The primary focus of the project was to develop a more efficient way to filter background
noise using accelerometers. While examining field uses for this application, there is a clear need
for a solution that more effectively filters noise and allows for precise communication. For field

3. applications, this could be used in a military setting where things such as rotor blades from
helicopters create a large amount of noise, or by firemen inside of a burning building since the
gear along with the noise from the building makes communication near impossible without
having some sort of microphone. The major problem with using a regular microphone is that
they pick up noise and also have a tendency to cut out which was solved by directly attaching an
accelerometer to the cheekbone to measure vibrations. The vibrations were then filtered and
passed to voice recognition software where they were converted to words. This project was
performed by first collecting data using an ADXL 335 accelerometer to take vocal readings and
determine which position gave the cleanest readings. A prototype was then designed which was
worn to continue the testing process. Using a Teensy 3.0 board, a Fast Fourier Transform was
run and the necessary filters were determined to complete this project. Once the filters were
determined, BitVoicer was used for the frequency matching. In conclusion to the project, final
tests were run in various noisy conditions to prove the design is efficient. Overall, the desired
outcome of the project was to be able to successfully distinguish noise from the human voice and
filter the noise. Once the noise had been filtered, the voice sample was converted to a much
cleaner version of the voice, which can be clearly understood even if the surrounding noise
exceeds normal noise conditions. The project was expanded beyond this scope once the final
desired results were achieved which will be continued as ongoing research.
Contribution:
Group Members:
Kevin Dotseth: Responsible for researching and implementing HTK libraries for voice
recognition. Assisted with report, poster design, and researching patents.
Alexander Freeland: Responsible for designing and implementing hardware. Researched
Teensy operation and managed power consumption to ensure a proper design. Assisted
with the final report, poster designs, and patent research as well. Researched
accelerometer options and determined which accelerometer would give the best results.
Nathan Glatz: Responsible for researching the Raspberry Pi and implementing software
for the Raspberry Pi to communicate with the teensy board while utilizing the HTK
libraries. Constructed the failure analysis.
Tristan Sprowls: Responsible for writing the teensy code for the bit stream. Researched
HTK documentation and organized the research.
Chad Strick: Responsible for assisting with the Raspberry Pi and implementing the
software. Researched initial design hardware and options for accelerometers. Assisted
with the failure analysis.
Adam Zobrist: Responsible for constructing the hardware platform. Coded in the Arduino
specific language for the Teensy board. Assisted with poster design and report.
Researched accelerometer options and determined which accelerometer would give the
best results.

4. Global/Societal Impact:
The application of a voice recognition accelerometer solution is crucial to improving
many fields such as military, industrial, or commercial. In the military, not only could this
project be used for environments with rotor blades causing communication to be difficult, but it
could also be used in active war zones. Shouting not only gives away position to the enemy, but
also if shouting is necessary, then the receiving individual will most likely have trouble getting
the message precisely. While at war, many soldiers wear something to protect their ears while all
of the gunfire and explosions are going on around them and so this only increases the difficulty
to hear what a squad is calling out to each other. Along with the military uses, this project could
also be used in a commercial setting such as a steel mill. In a steel mill communications are
extremely difficult due to the ear piercing noise that is being created within the plant. This
project offers a solution to make the communication easier, and the workers can become more
efficient and be safer as well. Another application of this project would be for the firefighters
who selflessly run into burning buildings to save individuals who they have never even met
before. This project would be a good way to assist these individuals and keep them safe. Through
the use of this application, firefighters will be able to hear each other clearly and remain safe
while being able to warn each others of dangers ahead. Helping in so many fields and the clear
demand for this project drove the motivation to complete this project and to produce a thorough
solution that can be used in various applications.
Description of Design:
Our voice recognition system has 3
distinct stages. First, An accelerometer is placed
on the head to capture vocal signals. Second, the
Teensy board performs dynamic FFT calculations
and digitally filters the signal. Finally, a computer
runs software that can match the vocal signal to
text to confirm the communication is clear.
Accelerometers take vocal readings and send
them to the Teensy board for filtering and
spectrum analysis. The accelerometer we chose
for our final design is the ADXL 335
accelerometer. We changed this from our
proposal because the Knowles accelerometers
proved to be too insensitive for our application.
We will use tape to hold the accelerometer on the
forehead, the nose, the jaw, or the chin to do this.
The device will be worn to allow us to prove the
effectiveness of our use of accelerometers. The
different locations represent real life mounting
locations we will use. The forehead device would
be mounted in a helmet; the nose device would be mounted into a facemask, and so on.
Figure
1:
data
results
from
[1].

5. Second, a Teensy board is used to
collect fundamental frequency capture data.
We chose a Teensy board because it uses
Arduino language to code and it has built in
functionality for audio processing. This
makes it easy to take the accelerometer
signal and translate it into an audio wave for
the computer to use. Additionally, The
Teensy board can run concurrent FFT
analysis to allow us to determine the
frequency components of the signal. Using
the frequency components we confirmed
where the vocal range lies and designed our
digital filter to attenuate noise outside the
range of 300 Hz to 3.4 KHz.
The software we have chosen for our voice recognition is called BitVoicer. This software
was chosen for its ease of use and its ability to seamlessly communicate with Arduino devices
like the Teensy board. The software uses HMMs or Neural Networks. To use the software, a
sentence to be recognized is typed into the software before recognition mode is activated. Once
recognition is activated and speech is input, the best match of the given sentences is returned if
any have reached a suitable confidence level. If no sentence reaches the confidence level, the
best fit is returned with an error message warning that the communication failed. BitVoicer is
limited in that it can only recognize sentences given to it instead of general recognition, but such
a system can be designed for future works.
Measurement Methods and Measured Results:
The Teensy board gives
the FFT spectrum analysis. The
spectrum analysis is shown on an
LCD screen. The x-axis is Hertz,
and the y-axis is magnitude. The
FFT shows that our vocal ranges
are at around 400 Hz. This makes
sense since all contributers to the
project are men.
Figure
2:
FFT
spectrum
of
X-­‐axis
of
an
accelerometer
used
as
a
pickup

6. This LCD shows a rolling
FFT. This gives a real time FFT in
the time domain. This can be thought
of as a raw audio output.
The BitVoicer software
gave the speech recognition
results. It measured audio level,
confidence level, and the
recognized text. The audio level
trigger is the magnitude that the
BitVoicer begins recognizing at.
The confidence level is a
probability that the audio input
matches a phrase it knows. The
text shows the phrase it believes
you said. The confidence level
varies from word to word based
on the difficulty of the
phonemes. Also, multiple
syllable words are easier to
recognize. Hard consonants read better. We have also successfully tested on Google search and
Cortana windows search.
Critical Evaluation of Design and Summary
Benefits and Limitations:
This design has the crucial benefit of providing clear communication in high noise
environments. High noise interference can cause many different communication problems in
industrial factories, during military operations, and other high noise environments and situations.
If the design is to work properly and consistently give perfect communication using the
accelerometer there will be little to no environmental interference. The limitations of the design
consists of: durability, reliability, and cost. The durability has become a problem because in most

7. of these high noise and interference ridden environments there is a good chance physical damage
can occur. Some testing has been done with light physical movement and testing concluded that
the wiring durability and sensitivity could be damaged very easily with the Knowles
accelerometer. To fix this problem, the ADXL 335 accelerometer replaced the Knowles
accelerometer in our design, which showed a much better result in durability. Reliability may
become an issue if the voice recognition software does not accurately recognize the vocal inputs.
The software also has a set library of sentences and words that can be recognized. Any other
inputs can cause confusion in the outputs. Finally, the circuitry and voice recognition software
can become costly if not managed correctly. We have researched several alternatives, Raspberry
Pi and Arduino included, for digital filtering and signal analysis to manage these costs.
Work to be Completed / Issues Not Resolved:
While our design is using the BitVoicer software for voice recognition, it does not offer
general voice recognition. It can only give the confidence for pre-set sentences. With more time,
we could write our own program using HTK to recognize a random sequence of phonemes and
match those to text.
Distribution Issues:
Our devices could be produced at low cost, but both BitVoicer and HTK forbid resale of
their products, due to open licensing agreements. To make our device marketable, we would
have to design our own software, which would require knowledge of Bayesian statistics and
advanced mathematics.
Potential Problems:
A failure mode and effect analysis (Appendix A) is blocked into three sections.
Accelerometer, Teensy 3.0, and BitVoicer make up these sections. The main concerns for the
accelerometer are the device could not be reading and/or it could be reading false information.
Getting no readings is a clear and noticeable problem. Most users should be able to visually
notice that the readings are not being taken from the accelerometer. The more serious concern is
if the device gets a reading but the reading is false. This situation could be unpredictable and
could be a danger to users. This is because some false readings can go unnoticed right away and
that is a problem since most users will depend on accurate communication in the field. As far as
the Teensy 3.0 and BitVoicer, both devices have programming issues and can cause inaccurate
communication. Just as described in the accelerometer example, situations are heavily dependent
on accuracy. Some corrective actions are to debug hardware and software before an issue occurs.
In this case the user should test the device before using it in the field. Problems in this device
should only occur if physical damage occurs. This being said, the device should be extremely
durable. Also, programming of the device should be as efficient as possible and be updated
yearly to include new updates in technology.

8. Patent Search:
As of April 2016, we have found one patent that is similar to our design. US publication
number US 2014/0081631 A1 is a patent that uses a contact microphone on the face glass of a
fireman’s helmet to pick up voice for transmission [4]. The difference between this patent and
our own is that our device would be placed against the skin rather than any part of the helmet.
This gives our design the advantage that collisions with the helmet will be ignored. The patented
design would be susceptible to helmet collisions, and would appear as spikes in the
communicated signal. Additionally, the patented design uses an actual microphone as well to
pick up the error signal. We believe this an unnecessary part of their design and have excluded it
from ours. Our searches included USPOC and Google Patent Search. The patent will be
referenced but not included in the appendix due to space constraints.
Other Issues:
There are no health issues since the sensor is non-invasive. There are no environmental
issues since the materials are all environmentally friendly. There are no ethical issues since this
doesn’t involve any groups funding the project.
Budget and Funding:
The budget for this project was limited. We pursued various sources, but ended up using
personal funds for the project. For our project we required extremely sensitive accelerometers.
They were exceedingly expensive, but we did find one that was sensitive enough for our
purposes and reasonably priced. We planned on using a Knowles BU series accelerometer but
ended up selecting the ADXL 335 due to its durability and inexpensiveness while remaining
sensitive. We used a Raspberry Pi for the data acquisition once BitVoice was working, and most
of our remaining funds went to this portion of the project. The final parts budget is listed below
in Table 1.
Table 1: Parts Budget
Description Quantity Cost Source
Raspberry Pi and Cana Kit 1 $69.99 Amazon.com
PJRC.comADXL 335 Accelerometer 1 $13.99
TFT LCD Display 1 $13.00
Teensy 3 series Board 1 $24.95
Headset for HTK training 1 $15.00
Total Cost: $136.93

9. Final Gantt Chart Timeline:
Conclusion
Degree
of
Success:
We
were
successful
in
using
our
ADXL
335
accelerometer
as
a
contact
microphone.
The
Teensy
3.0
board
successfully
filters
the
excess
noise
beyond
the
human
vocal
range
from
the
contact
microphone.
We
successfully
implemented
a
display
that
shows
the
FFT
signals
from
the
contact
microphone.
The
voice
recognition
software,
BitVoicer,
successfully
recognizes
the
output
from
the
Teensy
3.0
board.
This
information
is
shown
through
rejection
or
approval
due
to
the
confidence
of
recognition
of
voice
input.
Important
Lessons
Learned:
We
learned
that
time
management
and
effective
communication
between
group
members
is
vital
to
the
progress
and
completion
of
a
large
scale
project.
Self-­‐study
and
research
skills
are
important
as
an
individual
to
contribute
to
the
group
as
a
beneficial
member.
Future
Work:
We
would
want
to
further
research
using
the
HTK
libraries
to
offer
general
voice
recognition
for
our
voice
recognition
system
instead
of
the
pre-­‐built
word
or
sentence
structures
currently
needed
by
BitVoicer.
We
would
also
want
to
further
research
using
the
Raspberry
Pi
as
a
stand-­‐alone
device
to
run
the
general
voice
recognition
software.
Recommendations:
We
would
recommend
to
research
similar
patents
before
doing
any
work
towards
a
proposed
project.
We
would
also
recommend
talking
to
associated
professors
or
experts
in
the
proposed
field
of
research
for
advice
and
experience
in
encountered
problems.
7
1
14
28
2
28
3
1-­‐Dec
1-­‐Jan
1-­‐Feb
3-­‐Mar
3-­‐Apr
4-­‐May
Obtain
Vocal
Signal
FFT
Signal
vs.
Microphone
Filter
Design
Code
Digital
Filtering
Create
Frequency
Library
Code
Frequency
Matching
Final
TesYng
Gan[
Chart
Days
to
Complete

10. References:
1. Snidecor,
J.
C.,
Rehman,
I.,
&
Washburn,
D.
D.
(1959).
2.
Speech
Pickup
by
Contact
Microphone
at
Head
and
Neck
Positions.
J
Speech
Hear
Res,
2(3),
277-­‐281.
doi:
10.1044/jshr.0203.277.
3. O'Reilly,
R.,
Khenkin,
A.,
&
Harney,
K.
(2009,
February
2).
Sonic
Nirvana:
Using
MEMS
Accelerometers
as
Acoustic
Pickups
in
Musical
Instruments.
Analog
Dialogue,
11-­‐14.
4. Zhu,
Manli,
et
al.
Wearable
Communication
System
With
Noise
Cancellation.
Patent
US
2014/0081631
A1.
20
Mar.
2014.
Print.
5. Young,
Steve.
The
HTK
Book.
Cambridge:
Cambridge
University,
1995.
Print.
6. BitSophia
Tecnologia.
BitVoicer
1.2
User
Manual.
N.p.:
BitSophia
Tecnologia
Ltda,
n.d.
Print.
7. Analog
Devices.
ADXL
335
Datasheet.
Norwood:
One
Technology
Way,
2009.
Print.
8. Arduino.
K20
Sub-­‐Family
Reference
Manual.
N.p.:
Freescale,
n.d.
Print.

11.
Appendices:

12. Appendix
B
The
full
circuit
design
which
contains
two
Teensy
boards,
two
TFT
LCD
displays
and
an
audio
codec
(orange
LED).
M4
processors
are
on
the
Teensy
boards.
The
buttons
on
the
right
are
for
recording
purposes.