Abstract

Today, people are more interested in hands-

free communication. In such a situation, the

use a regular loudspeaker and a high-gain

microphone, in place of a telephone

receiver, might seem more appropriate. The

presence of a large acoustic coupling

between the loudspeaker and microphone

would produce a loud echo that would make

conversation difficult. Furthermore, the

acoustic system could become instable,

which would produce a loud howling noise

to occur. The solution to these problems is

the elimination of echo with an echo

cancellation algorithm. The echo cancellers

have main disadvantage since it supports

only a half-duplex communication . An

important aspect of echo cancellers is that

full-duplex communication can be

maintained, which allows both speakers to

talk at the same time.

Introduction

Echo is the repetition of a waveform due to

reflection from points where the

characteristics of the medium through which

the wave propagates changes. Echo is

usefully employed in sonar and radar for

detection and exploration purposes. In

telecommunication, echo can degrade the

quality of service, and echo cancellation is

an important part of communication

systems. The development of echo reduction

began in the late 1950s. There are two types

of echo in communication systems: acoustic

echo and telephone line hybrid echo.

Acoustic echo results from a feedback path

set up between the speaker and the

microphone in a mobile phone or hands-free

phone. Telephone line echoes result from an

impedance mismatch at telephone exchange

hybrids where the subscriber's 2-wire line is

connected to a 4-wire line. The perceptual

effects of an echo depend on the time delay

between the incident and reflected waves,

the strength of the reflected waves, and the

number of paths through which the waves

are reflected. Telephone line echoes, and

acoustic feedback echoes are undesirable

and annoying and can be disruptive.

What is echo ?

Echo is a phenomenon where a delayed and

distorted version of an original sound or

electrical signal is reflected back to the

source. If a reflected wave arrives after a

very short time of direct sound, it is

considered as a spectral distortion or

reverberation. However, when the leading

edge of the reflected wave arrives a few tens

of milliseconds after the direct sound, it is

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heard as a distinct echo. Fig 1 illustrate

simple diagram explained echo .

Fig 1 Here B is the reflected wave arrives after a very short time of direct sound

How echo generated

A significant problem in communications is

the generation of echoes. The echoes arise

for a number of reasons, with the primary

reason being an impedance mismatch. The

impedance mismatch occurs when the two-

wire network meets the four-wire network,

this interface is known as the hybrid. This

impedance mismatch causes some of the

signal energy to be returned to the source as

an echo .

The delays between primary and echo

signals are directly related to the

transmission distance. For example, if a

signal was sent to a satellite that redirected

the signal back to another location on earth,

that signal would have a very large time

delay compared to a signal sent to a local

switching station and back. Short delays

(less than 50 ms) will not affect the quality

of the signal as much as longer delays.

Delays of this length are not noticed by the

receiver and therefore are not considered an

annoyance. However, these echoes may

have an effect on data being transmitted

through transmission lines [1]

Echo Types

There are two types of echo in communication systems :

One) Acoustic echo : Acoustic echo

results from a feedback path set up between

the speaker and the microphone in a mobile

phone, hands-free phone, teleconference or

hearing aid system. Acoustic echo is usually

reflected from a multitude of different

surfaces, such as walls, ceilings and floors,

and travels through different paths .

Fig 2 Acoustic echo .

Gain of an acoustic feedback loop depends

on the frequency responses of the electrical

and the acoustic signal paths. The

undesirable effects of the electrical sections

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on the acoustic feedback can be reduced by

designing systems that have a flat frequency

response . [1]

Two) Telephone line Hybrid echo

[Hybrid] : Hybrid echo is the main source

of echo generated from the public-switched

telephone network (PSTN). Echoes on a

telephone line are due to the reflection of

signals at the points of impedance mismatch

on the connecting circuits. Conventionally,

telephones in a given geographical area are

connected to an exchange by a 2-wire

twisted line, called the subscriber's line ,

which serves to receive and transmit signals.

In a conventional system a local call is set

up by establishing a direct connection, at the

telephone exchange, between two

subscribers’ loops. For a local call, there is

usually no noticeable echo either because

there is not a significant impedance

mismatch on the connecting 2-wire local

lines or because the distances are relatively

small and the resulting low-delay echoes are

perceived as a slight amplification and

“livening” effect. For long communication

between two exchanges, it is necessary to

use repeaters to amplify the speech signals;

therefore a separate 2-wire telephone line is

required for each direction of transmission.

Fig 3: Illustration of a telephone call set up by connection of 2-

wire subscriber's via hybrids to 4-wire lines at the exchange

Figure 4: A 2-wire to 4-wire hybrid circuit.

To establish a long-distance call, at each

end, a 2-wire subscriber's line must be

connected to a 4-wire line at the exchange

fig 4 , as illustrated in Figure 5 . The device

that connects the 2-wire subscriber's loop to

the 4-wire line is called a hybrid as shown in

figure 4 . the hybrid is basically a three-port

bridge circuit. If the hybrid bridge were

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perfectly balanced then there would be no

reflection or echo. However, each hybrid

circuit serves a number of subscribers’ lines.

The subscribers' lines do not all have the

same length and impedance characteristics;

therefore it is not possible to achieve perfect

balance for all subscribers at the hybrids.

When the bridge is not perfectly balanced,

some of the signal energy on the receiving

4-wire lines becomes coupled back onto

itself and produces an echo.

Echo is often measured in terms of the echo

return loss (ERL); the higher the echo

return loss the lower will be the echo.

Telephone line echoes are undesirable, and

become annoying when the echo amplitude

is relatively high and the echo delay is long.

For example when a long distance call is

made via a satellite the round-trip echo

delay can be as long as 600 ms, and echoes

can become disruptive. Also, as already

mentioned, there are appreciable delays of

up to 200 ms inherent in digital mobile

phones, which make any echo quite

noticeable. For this reason the employment

of echo cancellers in mobile switching

centers is mandatory. [2]

How to reduce acoustic echo ?

When the telephone connection is between

hands-free telephones or between two

conference rooms, then an acoustic echo

problem emerges that is due to the reflection

of the loudspeaker’s sound waves from the

boundary surfaces and other objects back to

the microphone. There are a number of

methods for removing acoustic feedback.

One method for alleviating the effects of

acoustic feedback and the room

reverberations is to place a frequency shifter

(or a phase shifter) in the electrical path of

the feedback loop. Each time a signal travels

round the feedback loop it is shifted by a

few hertz before being re-transmitted by the

loudspeaker. This method has some effect in

reducing the howling but it is not effective

for removal of the overall echo of the

acoustic feedback. Another approach is to

reduce the feedback loop-gain at those

frequencies where the acoustic feedback

energy is concentrated. This may be

achieved by using adaptive notch filters to

reduce the system gain at frequencies where

acoustic oscillations occur. The drawback of

this method is that in addition to reducing

the feedback the notch filters also result in

distortion of the desired signal frequencies.

The most effective method of acoustic

feedback removal is the use of an adaptive

feedback cancellation system as in fig 5 [3]

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fig 5 the use of an adaptive feedback cancellation system

x(n): far-end signal,, y(n): echo signal ,v(n)

near-end signal, d(n): microphone signal,

u(n): background noise, yhat(n): filter

input, e(n): error signal, d[n]=y[n]+u[n],

e[n] = d[n]- yhat(n)

Hybrid echo cancellation

We will define the speech of speaker A as

the near end signal for hybrid A and the

speech of speaker B as the far-end signal for

hybrid A. For hybrid B, the near-end and

far-end signals are the signals of speaker B

and of speaker A, respectively. The speaker

at the far-end hears not only what the

speaker at the near-end says ,but also an

echo of his own voice .fig 6 , The signal

from the near end signal will be denoted by

v(t). The signal from the far-end speaker is

x(t). The echo of the far-end speaker is y(t).

The echo can be modeled as the result of the

signal x(t) passing through a linear filter h(t)

y(t) = h(t) * x (t) Where *

denotes convolution.

The signal which is sent back to the far-end

speaker is: z(t) = v(t) + y (t)

Another possibility is to identify the impulse

response h(t) of the hybrid or the acoustic

coupling between the loudspeaker and the

microphone, then estimate the echo signal

y(t) and subtract it from z(t). Let the

estimated echo and impulse response be

ˆy(t) and ˆh(t), respectively . The signal sent

back to the far end is:

e(t) = z(t) - ˆy (t) = v(t) + y (t) - ˆy (t)

The estimated echo ˆy(t) is:

ˆy (t)= ˆh (t) * x(t) If ˆ h(t) =h(t), then the

cancellation is complete, i.e. e(t) ≡ v(t) [4]

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Fig 6: hybrid echo cancellation

The echo canceller coefficients wk(m) are

adapted to minimize the energy of the echo

signal on a telephone line, say from speaker

B to speaker A. Assuming that the speech

signals xA(m) and xB(m) are uncorrelated,

the energy on the telephone line from B to A

is minimized when the echo canceller output

x^echoA

(m ) echo xA m is equal to the echo

xAecho (m ) echo xA m on the line. The echo

canceller coefficients may be adapted using

one of the variants of the recursive least

square error (RLS) or the least mean squared

error (LMS) adaptation methods. One of the

most widely used algorithms for adaptation

of the coefficients of an echo canceller is the

normalized least mean square error (NLMS)

method. The time-update equation

describing the adaptation of the filter

coefficient vector is

w(m)= w(m-1)+ µ [e(m) / ( x(m)TA x(m) A )]

x(m) A

where xA(m)=[xA(m), ..., xA(m–P)] and

w(m)=[w0(m), ..., wP–1(m)] are the input

signal vector and the coefficient vector of

the echo canceller, and e(m) is the difference

between the signal on the echo line and the

output of the echo synthesizer. Note that the

normalizing quantity x(m)TA x(m) A is the

energy of the input speech to the adaptive

filter. The scalar µ is the adaptation step

size, and controls the speed of convergence,

the steady-state error and the stability of the

adaptation process.

acoustic echo cancellation is more

complex than line echo cancellation

The problem of acoustic echo cancellation is

more complex than line echo cancellation

for a number of reasons. First, acoustic echo

is usually much longer (up to a second) than

terrestrial telephone line echoes. In fact, the

delay of an acoustic echo is similar to or

more than a line echo routed via a

geostationary satellite system. The large

delay of an acoustic echo path implies that

impractically large filters on the order of a

few thousand coefficients may be required.

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The stable and speedy adaptation of filters

of such length presents a difficult problem.

Secondly, the characteristics of an acoustic

echo path is more non stationary compared

with that of the telephone line echo.

Thirdly, acoustic echoes are due to signals

reflected back from a multitude of different

paths, off the walls, the floor, the ceiling, the

windows etc. Finally, the propagation and

diffusion characteristics of the acoustic

space of a room is a non-linear process, and

is not well approximated by a lumped FIR

(or IIR) linear filter. In comparison, it is

more reasonable to model the characteristics

of a telephone line echo with a linear filter.

In any case, for acoustic echo cancellation,

the filter must have a large impulse response

and should be able to quickly track fast

changes in echo path characteristics. [3]

Matlab Implementation of Echo Cancellation in Telephone Line

The RLS methods provides a faster convergence rate and better overall performance at the cost of higher computational complexity

The information bearing signal is a sine wave of 0.055 cycles/sample. It is considered in this case to be assumed voice signal , When the voice is passing through the telephone line fig-7 the echo is added with the signal fig-8 so that at the far end we would not able to receive the signal of actual voice but we get the signal shown in fig 9. Now our aim is to cancel the echo path. Here the Running the RLS adaptive filter for 1000 iterations. The plot shows the convergence of the adaptive filter response to the response of the FIR filter in fig 10. By computing we get the error e signal shown in fig 11

fig 7 : Assumed voice signal

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fig 8 : Echo path

fig 9 : Voice signal + Echo signal

Fig:10 filter response

Fig: 11 error signal

matlab implementation of Acoustic Echo Cancellation (AEC)

In acoustic echo cancellation, a measured microphone signal d(n) contains two signals: - the near-end speech signal v(n) - the far-end echoed speech signal dhat(n) The goal is to remove the far-end echoed speech signal from the microphone signal so that only the near-end speech signal is transmitted The Room Impulse Response we describe the acoustics of the loudspeaker-to-microphone signal path where the speakerphone is located. We can use a long finite impulse response filter to describe these characteristics

P.S. far end and near end signals are uploaded from matlab

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Summary

Telephone line echo and acoustic feedback echo affect the functioning of telecommunication and teleconferencing systems. In general, line echo cancellation, is a relatively less complex problem than acoustic echo cancellation because acoustic cancellers need to model the more complex environment of the space of a room. We discussed telephone line echoes arising from the mismatch at the 2/4-wire hybrid bridge. And implement RLS filter using matlab simulink to remove it .we considered the acoustic coupling between a loudspeaker and a microphone system. Acoustic feedback echo can result in howling, and can disrupt the performance of teleconference, hands-free telephones, and hearing aid systems. The main problems in implementation of acoustic echo cancellation systems are the requirement for a large filter to model the relatively long echo.

References [1] echo cancellation using adaptive filtering By Thani

Tridhavee And Steve Vuco

[2] Soria, E.; Calpe, J.; Chambers, J.; Martinez, M.; Camps, G.; Guerrero, J.D.M.; “A novel approach to introducing adaptive filters based on the LMS algorithm and its variants”, IEEE Transactions, vol. 47, pp. 127-133, Feb 2008.

[3] Implementation of Acoustic Echo Cancellation For PC

Applications Using MATLAB,Master of Science Thesis In

System on Chip Design, by Lu Lu Stockholm, 05/2007

[4] International Journal of Emerging Technology and Advanced

Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume

2, Issue 10, October 2012) 411 Matlab Implementation of Echo

Cancellation in Telephone Line

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Appendix

M = 4001;fs = 8000;

[B,A] = cheby2(4,20,[0.1 0.7]);

Hd = dfilt.df2t([zeros(1,6) B],A); % Discrete-time, direct-form II transposed filter numerator coefficients B and denominator coefficients A.hFVT = fvtool(Hd); % Analyze the filter using Open Filter Visualization Tool set(hFVT, 'Color', [1 1 1])%he Room Impulse ResponseH = filter(Hd,log(0.99*rand(1,M)+0.01).* ... sign(randn(1,M)).*exp(-0.002*(1:M)));H = H/norm(H)*4; % Room Impulse Responseplot(0:1/fs:0.5,H)xlabel('Time [sec]')ylabel('Amplitude')title('Room Impulse Response')set(gcf, 'Color', [1 1 1])%The Near-End Speech Signalload nearspeechn = 1:length(v);t = n/fs;plot(t,v);axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Near-End Speech Signal');set(gcf, 'Color', [1 1 1])x=wavrecord(16000,8000); p8 = audioplayer(v,fs);playblocking(p8);%he Far-End Speech Signalload farspeechx = x(1:length(x));dhat = filter(H,1,x);plot(t,dhat); axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Far-End Echoed Speech Signal');set(gcf, 'Color', [1 1 1])p8 = audioplayer(dhat,fs);playblocking(p8);

%The Microphone Signald = dhat + v+0.001*randn(length(v),1);plot(t,d) axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Microphone Signal');set(gcf, 'Color', [1 1 1])p8 = audioplayer(d,fs);playblocking(p8);%the Frequency-Domain Adaptive Filter (FDAF)mu = 0.025;W0 = zeros(1,2048);del = 0.01;lam = 0.98;x = x(1:length(W0)*floor(length(x)/length(W0)));d = d(1:length(W0)*floor(length(d)/length(W0)));% Construct the Frequency-Domain Adaptive FilterhFDAF = adaptfilt.fdaf(2048,mu,1,del,lam);[y,e] = filter(hFDAF,x,d);n = 1:length(e);t = n/fs;pos = get(gcf,'Position');set(gcf,'Position',[pos(1), pos(2)-100,pos(3),(pos(4)+85)])subplot(3,1,1);plot(t,v(n),'g') axis([0 33.5 -1 1]);ylabel('Amplitude');title('Near-End Speech Signal');subplot(3,1,2);plot(t,d(n),'b');axis([0 33.5 -1 1]);ylabel('Amplitude');title('Microphone Signal');subplot(3,1,3);plot(t,e(n),'r') axis([0 33.5 -1 1]);xlabel('Time [sec]');ylabel('Amplitude');title('Output of Acoustic Echo Canceller');set(gcf, 'Color', [1 1 1])p8 = audioplayer(e/max(abs(e)),fs);playblocking(p8);

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