Acoustic echo cancellation using nlms adaptive algorithm ranbeer
1. Acoustic Echo CancellationAcoustic Echo Cancellation
Using NLMS Adaptive AlgorithmUsing NLMS Adaptive Algorithm
Presented byPresented by
Ranbeer TyagiRanbeer Tyagi
10.10.2010
2. ContentIntroduction
Acoustic Echo Problem and Solution
Working of Acoustic Echo Canceller
Adaptive Filtering Algorithm
Necessity For Better Performance of AEC
Simulation Results
Conclusion
Future Work
References
10.10.2010
3. IntroductionTeleconferencing systems are expected to provide a high
sound quality. Speech by the far end speaker is captured
by the near end microphone and being sent back to him
as echo. Acoustic echoes cause great discomfort to the
users since their own speech (delayed version) is heard
during conversation.
The echo has been a big issue in communication networks.
Hence this presentation is devoted to the investigation and
development of an effective way to control the acoustic echo
in hands-free communications.
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4. Basic setup of a hands-free communication
system
Near End Room
Direct
Coupling
Reflection
Far End Room
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5. Acoustic Echo Problem and Solution
Sound is created by the loudspeaker and after Reflection
return to the microphone and undesirable echo is heard
during a conversation .
Solution is to Develop an algorithm for removing the
Acoustic echo so that transmission to the far-end is echo-
free. This is done by the Acoustic echo canceller
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6. Acoustic echo canceller
( )x n
( )y n
( )d n
∑
Far End
Signal
-
+ Far End
Echo
Adaptive
Filter
Far End Speaker
Near End Room
( )e n
( )w n
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7. Working of Acoustic Echo Canceller
Far end Signal travels out the loudspeaker, bounces
around in the room, and convolved with room impulse
response to produce far end echo .This far end echo is
picked up by the microphone.
The adaptive filter takes far end signal ,generates an
echo replica and subtracts it from far end echo to
generate an error signal .This error signal is
transmitted back to the far-end speaker.
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8. NLMS AlgorithmNLMS Algorithm
( ) ( )
( 1) ( )
( ) ( )T
x n e n
w n w n
x n x n
µ
δ
+ = +
+
( ) ( )
( 1) ( )
( ) ( )T
x n e n
w n w n
x n x n
µ
+ = +
x (n) can be very small due to random behavior and can causes
stability problem hence include a small correction term to avoid
stability problems
( ) ( ) ( )
( ) ( ) ( )
T
y n w n x n
e n d n y n
=
= −
0 1 1
( ) [ ( ), ( 1),..., ( 1)]
( ) [ ( ), ( ),......, ( )]
T
T
M
x n x n x n x n M
w n w n w n w n−
= − − +
=
is a step size parameter for stability 0 2µ< <µ
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9. Necessity for Better Performance of AEC
The selection of step size should be done carefully to
achieve Faster convergence and less steady state error.
The number of Taps in the filter should be large enough
to cover the echo path.
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16. Conclusion
The results show that the LMS algorithm has the least
computational complexity but a poor convergence
rate.
The NLMS algorithm has an improved convergence
rate while maintaining low computational complexity.
NLMS algorithm is the obvious choice for the real
time acoustic echo cancellation system. Additionally,
it does not require a prior knowledge of the signal
values to ensure stability.
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17. Future Work
The high background noise level is annoying to the
listener’s side during a conversation and will affect
the performance of the algorithm.
The acoustic echo canceller assumes that the near end
speaker is silent. So further work can be made to
consider the double talk situation.
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18. Reference
S.Haykin and T.Kailath “Adaptive Filter Theory ” Fourth Edition.
Prentice Hall, Pearson Education 2002.
“Adaptive Filters” Douglas L. Jones , CONNEXIONS Rice
University ,Houston, Texas.
J.G.Proakis,“ Digital Communications” ,Fourth Edition. New
York, McGraw Hill,2001.
Oppenheim, A. V. & Schafer, R. W. 1999, “Discrete Time Signal
Processing”, 2nd edition,Prentice Hall, United States of
America.
S.M.Kuo, B.H.Lee and W.Tian, ”Real Time Digital Signal
Processing”, John Wily & sons Ltd,2006.
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