1. Acoustic Echo Cancellation Using Digital Signal Processing. Presented by :- A.Manigandan(31308106052) B.Naveen Raj (31308106062) Parikshit Dujari (31308106066)

2. The “ECHO” Phenomenon ?

3. The “ECHO” Phenomenon. An echo (plural echoes) is a reflection of sound, arriving at the listener some time after the direct sound. A true echo is a single reflection of the sound source. Typical examples: The echo produced by the bottom of a well. The echo produced by the walls of an enclosed room and an empty room.

4. The “ECHO” Phenomenon.(contd.) Spectral distortion/Reverberation- Reflecting wave arriving after a short time of direct sound. A distinct echo- Leading edge of reflected wave arrives after “few milliseconds.” Types of echo : Acoustic Echo Electrical/Hybrid Echo

5. Acoustic Echo Hybrid Echo

6. Acoustic Echo Hybrid Echo  Impedance mismatch in analog local loop  PSTN-main source of electrical echo  Cause ‘signal leak’ to transmission path  Must be controlled on long distance calls  Coupling between the loudspeaker & microphone.  Round trip delay- time taken to reflect an echo.  Echoes<30db- unnoticed  Echoes>30db- annoying, steadily more disruptive

7. +s of an Echo. (Applications) Used to find distance between two surfaces – SONAR. Called as Echo Sounding.

8. -s of an Echo. (Disadvantages) Need for Echo Cancellation. Distortions in voice signals reduces the quality of the signals. Necessary to reduce the distortions and hence the echoes. Done by developing an echo cancellor.

9. The PROCESS of “Echo Cancellation.” It involves first recognizing the originally transmitted signal that re-appears, with some delay, in the transmitted or received signal. Once the echo is recognized, it can be removed by 'subtracting' it from the transmitted or received signal.

10. Block Diagram – Acoustic Echo Cancellation.

11. Adaptive Filter :  A filter that self-adjusts its transfer function according to optimization algorithm driven by error signal  Uses feedback in the form of error signal to refine its transfer function to match the changing parameters  Involves use of a cost function  Cost function- Criterion for optimum performance of filter, determine how to modify transfer function to minimize cost on next iteration

12. Block Diagram – Adaptive Filter

13. Filter Implementations  Least Mean Square filter (LMS)  Normalised Least Mean Square filter(NLMS)  Block Frequency Domain Adaptive filters(FDAF)

14. Least Mean Square (LMS) Algorithm. Developed by Widrow & Hoff in 1959 A type of adaptive filter known as Stochastic gradient based algorithm Utilizes the gradient vector of the filter tap weights to converge on optimal solution Filter tap weights are updated as, w(n +1) = w(n) + 2 μ e(n)x(n)

15. Least Mean Square (LMS) Algorithm. (contd.) w(n +1) = w(n) + 2 μ e(n)x(n) x(n)-i/p vector of time delayed i/p values x(n)=[x(n) x(n-1) x(n-2)…..x(n-N+M)]T w(n)-coefficients of adaptive FIR filter tap weight vector at time = n w(n)=[w 0 (n) w 1 (n) w 2 (n)….w N-1 (n)]T μ -step size parameter

16. Features Of LMS : Most popular in adaptive filtering Computational simplicity Easier implementation than other adaptive algorithms Each iteration requires 2N additions Each iteration requires 2N+1 multiplications