Acoustic Echo cancellation in speech processing by different adaptive algorithm

1. Acoustic Echo CancellationAcoustic Echo Cancellation
Using NLMS Adaptive AlgorithmUsing NLMS Adaptive Algorithm
Presented byPresented by
Ranbeer TyagiRanbeer Tyagi
10.10.2010

2. ContentIntroduction
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.
10.10.2010

4. Basic setup of a hands-free communication
system
Near End Room
Direct
Coupling
Reflection
Far End Room
10.10.2010

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
10.10.2010

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
10.10.2010

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.
10.10.2010

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
10.10.2010

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.
10.10.2010

10. 0 50 100 150 200 250 300 350 400
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4Amplitude
Sample Number
Acoustic Echo Path Impulse Response
10.10.2010

11. 0 1 2 3 4 5 6 7 8
x 10
4
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Amplitude
Sample Number
Far End Speech
10.10.2010

12. 0 1 2 3 4 5 6 7 8
x 10
4
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2Amplitude
Sample Number
Far End Echo+Noise
10.10.2010

13. 0 1 2 3 4 5 6 7 8
x 10
4
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15Amplitude
Sample Number
Residual Echo By NLMS Algorithm
10.10.2010

14. 0 1 2 3 4 5 6 7 8
x 10
4
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Amplitude
Sample Number
Far End Echo+Noise
Residual Echo By NLMS Algorithm
10.10.2010

15. 0 1 2 3 4 5 6 7 8
x 10
4
-90
-80
-70
-60
-50
-40
-30
-20
MSE of NLMS Algorithm
MSE[dB]
sample number10.10.2010

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.
10.10.2010

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.
10.10.2010

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.
10.10.2010

19. Thank You
10.10.2010