W2008 EECS 452 Project Active Noise Cancellation …gowtham/bellala_EECS452ppt.pdfActive Noise...
Transcript of W2008 EECS 452 Project Active Noise Cancellation …gowtham/bellala_EECS452ppt.pdfActive Noise...
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Active Noise Cancellation HeadsetsKuang-Hung liu, Liang-Chieh Chen, Timothy Ma, Gowtham Bellala, Kifung Chu
W2008 EECS 452 Project
4 / 15 / 2008
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Outline
Motivation & Introduction Challenges Approach 1 Approach 2 Demonstration Conclusion & future work
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Noise levels in human settings have come under scrutiny for reasons including health concerns and improvement of the quality of life.
For low-frequency noises, passive methods are either ineffective or tend to be very expensive or bulky.
Active Noise Cancellation(ANC) systems have become an effective technique for designing ANC headphones.
Motivation
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Introduction Application of adaptive signal processing. Use destructive interference to cancel out unwanted
noise. ANC headsets works best for cancelling lower frequency
sounds that are continuous and periodic. Higher frequency and impulse are hard to control.
0 10 20 30 40 50 60 70 80 90 100-1
-0.5
0
0.5
1
0 10 20 30 40 50 60 70 80 90 100-1
-0.5
0
0.5
1
unwantednoise
generated by ANC
0 10 20 30 40 50 60 70 80 90 100-1
-0.5
0
0.5
1
overall effect
+
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Adaptive Filter Framework
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Equivalent Model
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Challenge Difficulty:
(1) The acoustic superposition in thespace (from the cancelingloudspeaker to the error microphone)is sensitive to phase mismatch.
(2) ANC system is sensitive touncorrelated noise.
Solution: (1) Compensate for the secondary-path
transfer function S(z), which includesthe D/A converter, reconstructionfilter, anti-aliasing filter, A/D converter.
(2) Use FPGA to reduce system delay.(3) Add protection to the algorithm.
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0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-50
0
50noise
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-20
0
20observe noise
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-20
0
20generate inverse waveform
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-20
0
20error
Matlab Example
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Approach 1 : off-line estimation of S(z)
)(ˆ zS
Off-line estimation:Send a sequence of training data to estimate S(z) before Noise Cancellation.
Challenge:It’s better to train the filter with white noise. But because of the limited number of coefficients of the filter, we just train the filter with 200Hz sine waveform.
S(z) ++
-
e(n)Training data
Adaptively adjust
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Adaptive Algorithms
FxLMS FULMS FeedbackHybrid
W2008 EECS 452 / ANC headset project
P(z) ++-
e(n)x(n)
Adaptively adjust
y(n)
d(n)
W(z)
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FxLMS algorithm
)()(ˆ)(' )()(' )()1(:
)()()(
nxnsnxwherenenxnwnwweightsUpdate
nxnwny T
⊗=+=+
=µ
Advantages: Simple and neat Incorporates secondary path effect Tolerant to errors made in estimation of S(z) offline estimation sufficient
Disadvantages: Higher order filters slow Acoustic feedback Convergence rate depends on accuracy of
the estimation of S(z)
Equations:
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FxLMS algorithm
)()(ˆ)(' )()(' )()1(:
)()()(
nxnsnxwherenenxnwnwweightsUpdate
nxnwny T
⊗=+=+
=µ
Advantages: Simple and neat Incorporates secondary path effect Tolerant to errors made in estimation of S(z) offline estimation sufficient
Disadvantages: Higher order filters slow Acoustic feedback Convergence rate depends on accuracy of
the estimation of S(z)
Equations:
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Acoustic Feedback
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FuLMS Algorithm
)1()(ˆ)1('ˆ )()1('ˆ )()1(
)()(' )()1(: )1()()()()(
−⊗=−−+=+
+=+−+=
nynsnywherenenynbnb
nenxnanaweightsUpdatenynbnxnany TT
µµ
Advantages: Feedback Neutralization Feedback path designed using IIR filter IIR filter – lower order sufficient
Disadvantages: IIR filter – can become unstable Global convergence not guaranteed
Equations:
Acoustic Feedback
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Feedback ANC
)()(' )()1(:
)(ˆ)()(
)()()(1
0
nenxnwnwweightsUpdate
mnysnenx
nxnwnyM
mm
T
µ−=+
−+=
=
∑−
=
Advantages: Requires only one microphone Additional filter not required for acoustic
feedback neutralization Computationally less complex
Disadvantages: Same issues with IIR filter
Equations:
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Hybrid ANC
)1()(ˆ)()(ˆ
)(ˆ)(ˆ)('ˆ
)()('ˆ )()1(
)()(' )()1(: )(ˆ)()()()(
−⊗+=
⊗=
+=+
+=++=
nynsnend
ndnsndwhere
nendncnc
nenxnanaweightsUpdatendncnxnany TT
µ
µ
Advantages: Combines advantages of feedforward and
feedback systems lower order filters relatively more stable than feedback ANC
Disadvantages: High computational complexity
Equations:
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How to adaptively adjust the filter coefficients? Considering the computation time, we use LMS:
W(n+1) = W(n) + u * e(n) * W(n)u: step sizee(n): errorW(n): filter coefficients
To make our system more stable, we use variations of LMS:
(a) Leaky LMS: Introducing ‘a’ makes W(n) not change toorapidlyW(n+1) = a * W(n) + u * e(n) * W(n), where a < 1
(b) Normalize the coefficients W(n) to stabilize the outputW(n+1) = W(n+1) / sqrt(sum(W(n+1)))
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Approach 2:System architecture
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Input analog/digital interface
-
+
-
+
3.3 V
3.3 V
3.3 V
330Ω
330Ω 5kΩ
10kΩ
10kΩ
10kΩ
10kΩ
10kΩ
1 MΩ
150Ω 150Ω
47 μf
2.2 μf
0.1 μf
0.2 μf
OPA2340
OPA2340
MIC input output
MIC amplification circuit Single-Supply Sallen Key Low Pass Butterworth Filter with cut-off freq 7.5k
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Output digital/analog interface
-
+20kΩ
1kΩ
10Ω
0.05 μf
235 μf
0.1 μf LM386
3.3 V
to headsetD/A output
Amplification with gain 20
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MIC amplification circuitLow pass filter
Headphone driving circuit
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x[n]
e[n]
x[n]
e[n]
y[n]to
headset
to FPGA
from FPGA
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adaptively adjustfilter coeff w using
LMS algorithm
ct = ct + 1accumulate error_ac
Adaptive filter
W2008 EECS 452 / ANC headset project
Initialization
ct > 64
error_ac >error_last
w ← w_bk
error_ac >3*error_min
w_bk← werror_last = error_ac
update error_minreset_ct++
reset_ct >60
reset w
yes no
yes no
yes
no
yes
no
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Sampling rate & filter size design consideration Sampling rate determines processing speed constraint. Fix sampling rate
Large filter size: fine freq resolution, slow processing, slow response.
Small filter size: low freq resolution, fast processing, fast response.
Fix filter size High sampling rate: low freq resolution, less artifact in the D/A
output. Low sampling rate: high freq resolution, more artifact in the D/A
output.
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W2008 EECS 452 / ANC headset project
10 20 30 40 50 600
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x[n]w[n]
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x[n]w[n]
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2.5x 10
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freq
mag
700 Hz
x[n]w[n]
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Experiment result (1)
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10 20 30 40 50 600
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freq
mag
300 / 500 / 700 Hz
x[n]w[n]
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Experiment result (2)
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10 20 30 40 50 600
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1.5
2
2.5x 10
5
freq
mag
300 Hz
x[n]w[n]
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Experiment result (3)
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10 20 30 40 50 600
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1.5
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2.5x 10
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freq
mag
500 Hz
x[n]w[n]
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Experiment result (4)
W2008 EECS 452 / ANC headset project
10 20 30 40 50 600
0.5
1
1.5
2
2.5x 10
5
freq
mag
700 Hz
x[n]w[n]
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Incorporate music source
Assume music source is uncorrelated with the noise, and noise is a zero mean w.s.s random sequences.
Adaptive algorithm will adjust filter coefficient so as to minimize MSE.
Since does not depend on filter coeff, adaptive algorithm will select coeff to minimize .
W2008 EECS 452 / ANC headset project
E[e2] = E[(d ¡ Y )2]= E[(m + y ¡ Y)2]= E[m2] + E[(y ¡ Y )2] + 2E[(y ¡ Y )m]
= 0 Y,X uncorrelated with mE[m2]
E [(y ¡ Y )2]
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W2008 EECS 452 / ANC headset project
ANC system demonstration part 1:
Single tone and multiple tone artificial noise
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ANC system demonstration part 2:
Real engine noise
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ANC system demonstration part 3:
Noise with music source
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Future work (1)
W2008 EECS 452 / ANC headset project
Our ANC system
input
output
LPF (1kHz)
LPF
from microphone
to headset
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Future work (2)
Use more precise microphone and ADC, DAC to acquire more accurate measurement.
Integrate the design components into a build-in embedded system to avoid feedback interference.
Implementation in assembly language to save computation time.
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Conclusion
A workable ANC headset for both artificial and real world noise.
Works for noise frequency ranging from 100 to 800 Hz. Incorporate music source. Implemented and compared LMS, FxLMS, Feedback,
FuLMS and Hybrid algorithms: For stability, Hybrid is the best. For simplicity, FxLMS is recommended.
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Thank you
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References S.M. Kuo, D.R. Morgan, Active noise control: a tutorial review, Proc. IEEE 87 (6)
(June 1999) 943-975. S. M. Kuo and D. R. Morgan, Active Noise Control Systems – Algorithms and DSP
Implementations. New York: Wiley, 1996. A. Miguez-Olivares, M. Recuero-Lopez, Development of an Active Noise Controller in
the DSP Starter Kit. TI SPRA336. September 1996. S. Haykin, Adaptive Filter Theory, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1991.
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