Acoustic Echo CancellationAcoustic Echo Cancellation Using NLMS Adaptive AlgorithmUsing NLMS Adaptive Algorithm

Presented byPresented by

Ranbeer TyagiRanbeer Tyagi

10.10.2010

ContentIntroductionAcoustic Echo Problem and SolutionWorking of Acoustic Echo CancellerAdaptive Filtering AlgorithmNecessity For Better Performance of AECSimulation ResultsConclusionFuture WorkReferences

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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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Basic setup of a hands-free communication system

Near End Room

Direct Coupling

Reflection

Far End Room

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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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Acoustic echo canceller( )x n

( )y n

( )d n

Far End Signal

- + Far End Echo

Adaptive Filter

Far End SpeakerNear End Room

( )e n

( )w n

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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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NLMS AlgorithmNLMS Algorithm

( ) ( )( 1) ( )( ) ( )T

x n e nw n w nx n x n

( ) ( )( 1) ( )( ) ( )T

x n e nw n w nx n x n

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

( ) ( ) ( )( ) ( ) ( )

Ty n w n x ne n d n y n

0 1 1

( ) [ ( ), ( 1),..., ( 1)]

( ) [ ( ), ( ),......, ( )]

T

TM

x n x n x n x n M

w n w n w n w n

is a step size parameter for stability0 2

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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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Acoustic Echo Path Impulse Response

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Far End Speech

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Far End Echo+Noise

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Residual Echo By NLMS Algorithm

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Far End Echo+NoiseResidual Echo By NLMS Algorithm

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MS

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ConclusionThe 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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Future WorkThe 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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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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Thank You

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