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Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series Vol. (30) No. (2) 2008
2008
��
(MATLAB)
(EC),,
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Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series Vol. (30) No. (2) 2008
Studying and Handling the Phenomenon of Echo in Communication Systems
Dr. Haytham Al-Radwan
Dr. Sadek Ali** Faifaa Mikaiel***
(Received 6 / 1 / 2008. Accepted 18 / 3 / 2008)
� ABSTRACT �
The phenomenon of echo in wired and wireless communication is considered to be
one of the most important obstacles that affects the quality of the signal and system performance. This research aims to study the phenomenon of echo, its reasons, and its mechanisms. Bearing the outcome in mind, a method is to be suggested to overcome these obstacles. The echo and the echo canceller were simulated by MATLAB using NLMS to suppress the echo of the communication channel. This paper offers effective suggestions to overcome the echo signal and therefore ensure the quality of communication.
Keywords: Satellite communication, Echo, Echo canceller, Hybrid, Acoustic, NLMS.
* Associate Professor, Department of communication Engineering, Faculty of Mechanical and
Electrical Engineering, Tishreen University, Lattakia, Syria.**Associate Professor, Department of Communication Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia , Syria. ***Postgraduate Student, Department of Communication Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia, Syria.
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,(space segment)
(ground segment)
(computers)(mobiles) (PSTN)
(Public Switched Telephone Network)
(MATLAB)
20062007
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-1
(30ms)
(30 ms)
[1]
(500 ms)
-1-1
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(186,000 miles per second)
(23,800mile)
(250 ms)
[1]
-1-2
.
2-wire(hybrid)
4-wire
(Hybrid Echo)
(Acoustic Echo)
[1]
-1-3
(PSTN)
(2-wire) (4-wire)
1(2-wire)
(4-wire)
[2]
1 2 3 6 4 5 7
المقسم المقسم
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1
2
3
4
5
6
7
-1-4
2
[2]
.(Echo Suppressors)
(Echo Canceller)
سماعة المشترك األول
voice
الثانيسماعة
voice
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-1(Echo Suppressors)
.
[1]
-2(Echo Canceller)
80%90%
Double Talk Detector (DTD)
(Non Linear Processor)(NLP
[1] [3]
(AF) (Adaptive Filter)
. (DTD)(Double Talk Detector)
.(NLP)(Non-Liner Processor)
(3)
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3
x(n)(FES)Far-end speaker
v(n)(NES)Near-end speaker
y(n)
d(n)
:e(n)
ĥ(n)
h(n)
:ŷ(n)
3 x(n)
ĥŷ(n)
x(n)
y(n)
(echo path)
d(n)
d(n)
-1:
(4)
+
x(n)
From Near-end speaker
Adaptive Filter
ĥ
Echo path h
Σ Σ
From Far-end speaker
d(n)=y(n)+v(n) v(n)
y(n)
e(n) +
+ -
ŷ(n)
To Far-end speaker
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4
:(4)
Ŷ(n)
Ŷ(n)=x(n)*h0(n)+x(n-1)*h1(n)+……….+x(n-L+1)*hL-1 (1)
(2)
1
0)()(
L
i i inxnh ŷ(n)
(n)= h(n)Tx(n) (3)
x(n)
(h)
h0 = [h0(0), h0(1),…. h0(N-1)] (4)
h0
N
x(n)
x(n)=[x(n),x(n-1),…x(n-N+1)]T (5)
d(n
d(n)=x(n)T h0+v(n), n=0,1,2,…..,L-1 (6)
,n=0,1,2,…..,L-1 (7)
1
0
0 )()()(N
i
nvinxih d(n)=x(n)*h0(n)+v(n)=
x(n)T .x(n)
L
(3)y(n)v(n)
ŷ(n)
e(n)=d(n)- ŷ(n) (8) e(n)= y(n)+v(n)- ŷ(n) (9)
X(n) X(n-1)
X(n-2) X(n-L+1)
h0(n) h1(n) h2(n) hL-1(n)
z-1
z-1
z-1
ŷ(n)
Σ Σ Σ
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e(n)=v(n)y(n)-
ŷ(n)
(Normalized Least Mean Square) (NLMS).[4,3]
(10) J=| e(n)|2=| d(n)- ŷ(n)|2
J
[3]
(11)
h(n) n
δδ<<1
µ0µ2 [3]
-2
(5)
5
)()()()(
)()1( nxnenxnx
nhnhT
From Near-end speaker
Adaptive
Filter ĥ
Echo path
h
Σ Σ
From Far-end speaker x(n)
d(n) v(n)
y(n)
e(n)
+ + -
+
ŷ(n)
To Far-end speaker
DTD
Open during double talke
NLP
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(Geigel Algorithm)
[6,5]
N
Td(n)
|}1) N -x(t | , ... |,x(t)max{| (12)
α
(13)
T T=26dB2T=
3dB
[6,5]
-3
(5)
(6)
(6)NLP
-4(Echo Return Loss)(ERL)
6dB [7]
-5(Echo Return Loss Enhancement )(ERLE)
y(n)e(n)
dB
Td(n)
|}1) N -x(t | , ... |,x(t)max{|
level detector Gain control output
input
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(14)
)(nepe (n)
)(nypy (n)
(7)
All signal passed
start
FES signal x(n)
NES signal v(n)
ECHO signal y(n)
Microphone signal d(n)=V(n)+y(n)
DTD
The signal go to the NLMS filter
loop
α >T
yes
no
Subtract the good signal from error
signal
The signal go to the FES
The signal go to the NLP
)(
)(
10log10ne
ny
p
pERLE
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MATLAB
(command window)
(Far End Speech)(FES)(Near End Speech)(NES)
[8]
(8)
0 5 10 15 20 25 30-4
-3
-2
-1
0
1
2
3
4
Time [sec]
Ampli
tude
Near-End Speech Signal
8v(n)
(9)
0 5 10 15 20 25 30-4
-3
-2
-1
0
1
2
3
4
Time [sec]
Ampli
tude
Far-End Echoed Speech Signal
9y(n)
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(10)
0 0.5 1 1.5 2
x 104
-4
-3
-2
-1
0
1
2
3
4Microphone Signal
Time [sec]
Ampli
tude
10d(n)
>02>
(ERLE)
fs=8KHZv(n)
0.025=
0 5 10 15 20 25 30-4
-2
0
2
4
Time [sec]
Ampl
itude
Output of Acoustic Echo Canceller
u = 0.025
11e(n)
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0 5 10 15 20 25 300
5
10
15
20
25
30
35
40
Time [sec]
ER
LE1
[dB
]Echo Return Loss Enhancement1
= 0.025
12ERLE
0.04=
0 5 10 15 20 25 30-4
-3
-2
-1
0
1
2
3
4
Time [sec]
Ampl
itude
Output of Acoustic Echo Canceller
u = 0.04
13e(n)
Time(sec)
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0 5 10 15 20 25 300
5
10
15
20
25
30
35
40
Time [sec]
ER
LE2
[dB
]
Echo Return Loss Enhancement1
= 0.04
14ERLE
v(n)=0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 105
-100
-50
0
50
100
150
200Erorr signal
= 0.025
15e(n)
NLMS
Time(sec)
amplitude
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0 20 40 60 80 100 120 140 160 180 200-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1filter coefficients
= 0.025
15)
0 0.5 1 1.5 2
x 105
-100
-50
0
50
100
150
200Erorr signal
time(sec)
ampli
tude
= 0.04
16e(n)
Time(sec)
magnitude
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0 50 100 150 200-1
-0.5
0
0.5
1filter coefficients
frequency
mag
nitud
e
= 0.04
17
MATLAB
µ
µ
ERLE
ERLE(8,10)
ERLE6dB
ERLE6dB
PNLMS(Proportionate Normalized Least Mean Square)
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1- INTELSAT Digital Satellite Communication Technology. Revision 2,Aprile 1995.
2- WALLIN ,F. Combining Acoustic Echo Cancellation and Suppression, Linkoping
University, Sweden ,October 2003. pp,71. 3-International Telecommunication Union (ITU) "Digital network echo canceller
"Recommendation ITU-T,G.168. 4- FARHANG,B, Adaptive Filters: Theory and Applications, Chichester, England,
Wiley, 1998
5- BENESTY, D.R. MORGAN and J.H. CHO, “A New Class of Doubletalk Detectors Based on Cross-correlation”, IEEE Trans. Speech Audio Processing, vol. 8, pp. 168-172, March 2000.
6- BENESTY, T. GANSLER, D.R. MORGAN, M.M. SONDHI and S.L. GAY, “Advances
in Network and Acoustic Echo Cancellation”, Springer-Verlag, 2001. 7-International Telecommunication Union (ITU) "Application of the E-model :A planning
guide "Recommendation ITU-T,G.108. 8-“Matlab Real Time Workshop; User’s Guide,” The Mathworks Product Documentation. 5/7/2007,<http://www.mathworks.com/access/helpdesk/help/helpdesk.shtm>
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%the near-end speech signal v(n) savefile = 'test1.mat'; fs=8000; x=wavread('gg'); save(savefile, 'x','fs') plot(x) savefile = 'near-end speech. mat'; fs=8000; %g: wave file v=wavread('g'); p1 = audioplayer(v,fs); playblocking(p); save(savefile, 'v', 'p1','fs') n = 1:length(v); t = n/fs; plot(t,v); axis([0 33.5 -4 4]); xlabel('Time [sec]'); ylabel('Amplitude'); title('Near-End Speech Signal');
%the far-end speech signal x(n) savefile = 'Far end speech. mat'; fs=8000; %gg: wave file v=wavread('gg'); p8 = audioplayer(v,fs); playblocking(p8); save(savefile, 'v', 'p8','fs') load Far end speech x = x(1:length(x)); d= filter(H,1,x); t =(1:length(x))/fs; plot(t,d); axis([0 33.5 -4 4]); xlabel('Time [sec]'); ylabel('Amplitude'); title('Far-End Echoed Speech Signal'); set(gcf, 'Color', [1 1 1]) p2 = audioplayer(d,fs); playblocking(p2);
%Microphone Signal fff=length(v) s=dhat(1:length(v)); ss=0.001*randn(length(v),1); f=length(s) ffff=length(ss) g=s+v; d=ss+g; subplot(3,3,3) r=1:length(d);
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rr=r/fs; plot(r,d); axis([0 2*10^4 -4 4]); xlabel('Time [sec]'); ylabel('Amplitude'); title('Microphone Signal'); set(gcf, 'Color', [1 1 1]);
%NLMS algorithm fs = 8000; function [e,h]=nlms(µ,L,x,d,δ); % Normalized LMS % Call: % [e,h]=nlms(µ,M,x,d, δ ); % % Input arguments: % µ = step size, dim 1x1 % L = filter length, dim 1x1
% x = input signal, dim Nx1 % δ = constant, dim 1x1 % Output arguments: % e = estimation error, dim Nx1 % h = final filter coefficients, dim Mx1 %intial value 0 w=zeros(L,1); %input signal length N=length(u); %make sure that u and d are colon vectors x=x(:); d=d(:); %NLMS for n=L:N uvec=u(n:-1:n-M+1); e(n)=d(n)-w’*xvec; h=h+µ /(δ +xvec’*xvec)*xvec*conj(e(n));
end
%ERLE function [a]=erle(e,y); % Call: % erle=erle(e,y); % Input arguments: % e = residual echo, dim Nx1 % y = hybrid output signal, dim Nx1 % Output arguments: % erle = ERLE(n) in dB, n=1..N, dim Nx1 erle1=filter(Hd2,(e-v(1:length(e))).^2)./(filter(Hd2,dhat(1:length(e)).^2)); erledB1 = -10*log10(erle1); subplot(3,3,5); plot(t,erledB1); axis([0 33.5 0 40]); xlabel('Time [sec]'); ylabel('ERLE1 [dB]'); title('Echo Return Loss Enhancement1');
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