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Transcript
Department of Electrical Engineering | University of Texas at Dallas
Erik Jonsson School of Engineering & Computer Science | Richardson, Texas 75083-0688, U.S.A.
Design and fabricate a new microphone array system which will:
Minimize the feature size of the array system by integration of the
microphone and preamplifier circuit.
Reduce noise factors and electro magnetic interferences
Be a portable microphone array and power regulation system
Determine the array configuration and array processing method
that will give the optimum speech to noise ratio.
Implement a speaker recognition system to determine who is speaking
among a closed set of known drivers.
Explore the use of Wireless Transmission, VoIP, and Packet Loss
Concealment
Power Regulation
Microphone Array
Microphone Array Circuit Design
Power Regulation Circuit Design
• Designed and simulated using TinyCad and
PSpice
• Integrated Microphone & Preamplifier
• Bandpass filtering through 100Hz to 20kHz
• Gain 45V/V
• Designed and simulated using TinyCad and
PSpice
• Design for ±5VDC output power for Multiple
Power Outlets
• Used Positive Voltage Regulator and Voltage
Converter
• Bypass Capacitor: Stable Output Power
Create PCB Layout
•
•
•
•
•
Create PCB Layout
Layout design by FreePCB
Feature size: 24mm x 24mm
Edited Footprint for SMD type
Left side: Top view of Mic. PCB
Right side: Bottom view of Mic. PCB
•
•
•
•
Layout design by FreePCB
Feature size: 66mm x 45mm
Green Trace: Top view
Red Trace: Bottom view
Fabricate Microphone Array
• Used LPKF ProtoMat PCB Milling Plotter
• Yields: 3 Arrays (15 Microphones)
• Components:
• Op-Amp (MC33204DTB),
• Microphone (EK-23024),
• SMD Resistors and Capacitors
Microphone
Preamplifier
Circuit
Power
Regulator
Microphone &
Preamplifier Circuit
Data
Acquisition
(DEWTRON)
•
•
•
•
•
Used LPKF ProtoMat PCB Milling Plotter
Five power outlet in one line
Input Power: 12VDC and 3A
Output Power: +5VDC and -5VDC
Components:
• Positive Voltage Regulator(7805)
• Voltage Converter(LMC7660)
Performance
•
•
•
•
•
Bandpass filtering frequency: 50Hz-10kHz
Gain: 45V/V
Diode indicators for power/signal
Input Power: +5VDC and -5VDC
Feature Size: Same size as a dime (24mm x 24mm)
Data Acquisition
(DEWTRON)
DASB
Segmented SNR
Power Regulator
Bandpass Filter for Human
Voice (20Hz – 20kHz)
Fabricate Power Regulator
Differential Amplifier
Gain: 45 V/V
CSA-BF
close talk
Channel 3
close talk
Channel 3
Linear Sample
18.0166 dB
28.6192 dB
27.9125 dB
28.0366 dB
Logarithmic Sample
18.1457 dB
28.0393 dB
28.1584 dB
28.1943 dB
CSA-BF showed significant improvement over DASB in the segmental signal-to-noise ratio test and the
logarithmic array setup showed a small increase in the segmental SNR over the linear array setup.
Test Previous
Microphone
Array System
Design New
Microphone
Array System
0.04
50
0.03
48
46
44
0.02
40
0.01
-0.04
• Design microphone
circuitry
• Design power supply
circuitry
• Create microphone
PCB
• Create power supply
PCB
• Design new microphone
enclosure
• Construct physical array
• Fix problems with old
array.
Sean Stixrud
Corrupted
• Array configuration to
yield an optimum
speech to noise ratio
• Signal processing
methods to yield an
optimum speech to
noise ratio
Jeffery Leinbaugh
Kevin Derischebourg
• Set up and test speaker
recognition software
• Evaluate speaker
recognition
performance with in car
audio with engine noise
• Evaluate speaker
recognition
performance using high
pass filter to remove
engine noise
• Study Wireless
Transmission
• Study Voice over IP
(VoIP)
• Practice and determine
the best Packet Loss
Concealment Algorithm
15-inset EER
4 seconds
6 seconds
8 seconds
15-inset EER 44.213
42.963
40.833
47.500
30-inset EER 45.000
45.833
45.833
43.333
45-inset EER 46.944
48.889
45.833
41.667
Frame
Repeat Frame (Recovered)
Interpolation (Recovered)
Given a corrupted signal, we used two
different PLC Algorithms to “fill” the gaps.
Chance Kelly
49001
48951
48901
48851
-0.03
Jeong Hee Kim and Tae
Hwan Kim
2 seconds
2 seconds 4 seconds 6 seconds 8 seconds
-0.02
Assemble and
Test
30-inset EER
36
0
-0.01
Design and
Construct PCB
Enclosures
45-inset EER
38
48800
Amplitude
Populate
Circuit Board
Fabricate
Microphone
Array Circuitry
42
Speaker recognition test with UTDrive corpus
database using 2-8 seconds of speech
MICROPHONES
In the future, audio collected during UTDrive may be sent wirelessly for collection rather than recorded to a hard drive
physically present in the vehicle.
VoIP allows transmission of speech over the Internet real-time, which can be effective for recording to a hard drive remotely.
VoIP audio, however, suffers quality loss when packets are dropped from poor connections. The received audio has gaps
where the packets are dropped and causes the audio to have significantly degraded quality.
As quality continues to drop, it becomes harder to understand what the speaker is saying. A further loss in quality makes it
difficult to understand who is even speaking, let alone what they are saying. To help understand these premises we:
Created a survey to determine a minimum quality of voice that a listener was willing to listen to and could understand.
Surveyed listeners to see what minimum quality is needed to at least understand who the speaker is, given an
unaffected voice sample.
Surveyed listeners to see which packet loss concealment scheme (a simple one) is useful for recovering lost
quality. Packet loss concealment is used to make up for times when packets are dropped---trying to fill in the "blanks" can
potentially recover lost quality.
Speaker recognition is a process by which the identity of a speaker can be determined. This project is only concerned with
closed set speaker recognition, meaning the speaker is assumed to belong to a known set of people whose voices have
already been collected and processed to produce Gaussian Mixture Models (GMMs). The speaker recognition software will
perform feature extraction on short voice samples and compare them to the existing GMMs to determine a best fit.
PREAMPLIFIER
DATA ACQUISITION
MICROPHONES WITH PREAMPLIFIER
POWER
SIGNAL BOX
POWER
Because the close talk microphone sample was not very clean, the comparison of the beam formed sample to
channel 3 resulted in a higher segmented SNR value than that of the beam formed sample and the close talk
microphone. This problem with the close talk microphone will be addressed in the next phase of the project.
HTK was used to perform speaker recognition on clean out of car data. Results for in car recordings could not
be obtained for various reasons, including engine noise, interference, and competing speakers in the vehicle.
These problems will be addressed next semester with the new microphone array and beam forming software.