15 echo_ leakynlms

Purpose

Simulation of an acoustic echo canceler application using a transversal adaptive filter updated according to the Leaky Normalized Least Mean Squares (LEAKYNLMS) algorithm.

Syntax

echo_leakynlms

Description

Figure 10.29: Block diagram of an acoustic echo canceler implemented using the Leaky NLMS adaptive filter.

The block diagram of the acoustic echo cancellation problem is shown in Fig. 10.29. The simulation considered here uses a transversal FIR filter for the adjustable filter and the coefficients of the filter are updated in time domain using the Leaky NLMS algorithm. The far-end speech signal $x(n)$ (the speech from the remote speaker) is stored in the file infile. The local speaker is assumed to be silent (listening to the remote speaker and not interrupting). The echo picked by the microphone when playing $x(n)$ through the local loudspeaker is stored in the file dfile. First, the variables for the echo canceler $w(n)$ are created and initialized using init_leakynlms(), and the input signals are read from files, then a processing loop is started. In each iteration of the loop asptleakynlms() is called with a new sample from the FES and a new sample from the NES signals to calculate the filter output $y(n)$ (estimated echo), filter error $e(n)$, and update the filter coefficients. The residual signal $e(n)$ is saved in each iteration for later examination. This simulation script uses the standard ASPT iteration progress window (IPWIN). The IPWIN has four buttons which allow you to stop and continue the simulation, show or hide the simulation graph window, break out of the processing loop, and quit the simulation. After processing all the samples, or on pressing the break or stop buttons, the sensor signal $e(n)$ is written to a wave audio file and a graph presenting the echo canceler performance is generated.

Code

clear all;
infile = '.\wavin\aecfes.wav';           % Far-end speech (FES)
dfile  = '.\wavin\aecnes.wav';           % Near-end speech (NES)
rfile  = '.\wavout\resleaky.wav';        % residual signal

M      = 512;                            % adaptive filter length 
mu     = 0.2/M;                          % adaptation constant
b      = 0.99;                           % autoregressive pole 
a      = 1 - 1e-5;                       % leak factor

[w,x,d,y,e,p]    = init_leakynlms(M);    % Init NLMS				
[xn,inFs,inBits] = wavread(infile);      % read FES
[dn,inFs,dBits]  = wavread(dfile);       % read NES
inSize  = max(length(xn),length(dn));    % Samples to process
res     = dn;                            % Residual array
E       = init_ipwin(inSize);            % Initialize IPWIN

fprintf('equivalent noise variance = %f\n', (1 - a)/ (2*mu));

%% Processing Loop
for (m=1:inSize)
   % update the delay line
   x  = [2^(inBits-1) * xn(m); x(1:M-1)];

   % scale the Mic signal
   d  = 2^(inBits-1) *dn(m);

   % call asptleakynlms to update the filter	
   [w,y,e,p]= asptleakynlms(x,w,d,mu,a,p,b);	
		 
   % save the last residual sample 
   res(m) = 2^-(inBits-1)*e;

   % update the iteration progress window									  
   [E, stop,brk]	= update_ipwin(E,e,d, 'e', w, dn, res);

   % handle the Stop button
   while (stop  ~= 0), stop 	= getStop; end;

   % handle the Break button				
   if (brk), plot_echo(w,dn,res); break; end;			
end;

wavwrite(res,inFs,inBits,rfile);    % Save the residual
plot_echo(w, dfile, rfile);         % Show results 

Results

Figure 10.30: Performance of an Acoustic Echo Canceler implemented using the Leaky NLMS adaptive filter.

Running the above script will produce the graph shown in Fig. 10.30. The top panel in Fig. 10.30 shows the values taken by the filter coefficients by the end of the simulation (end of input files). The middle panel show the waveforms of the near-end speech signal $d(n)$ and the residual signal $e(n)$ for visual comparison between the echo before and after applying the echo canceler. The bottom panel shows the echo energy decrease in dB achieved by the echo canceler versus time, usually known as the Echo Return Loss Enhancement (ERLE). Note that the ERLE is meaningful only in the time periods where there is echo to be canceled.

Audio Files

The following files demonstrate the performance of the Leaky NLMS algorithm in the echo canceler application mentioned above.

wavin.wav	far-end speech (input) signal.
wavin.wav	near-end speech (microphone) signal.
wavout.wav	residual signal (echo canceler output).

See Also

INIT_ LEAKYNLMS, ASPTLEAKYNLMS.

Reference

[11] and [4] for extensive analysis of the NLMS and the steepest-descent search method.