Acoustic echo

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Qanbar Ali Khan(2011342), Waqas Khalid(2011315)

Mohammad Osama(2011),Imran Khan(2011)

Signals & Systems Lab, Faculty of Engineering Sciences,

Ghulam Ishaq Khan Institute,Topi,Pakistan

[email protected]

[email protected]

ABSTRACT:

Acoustic echo cancellation is a common

Occurrence in todays telecommunication systems. Itoccurs when an audio source and sink operate in full

duplex mode .The signal interference caused by acoustic

echo is distracting to both users and causes a reduction

in the quality of the communication. This paper focuses

on the use of adaptive filtering techniques to reduce this

unwanted echo, thus increasing communication quality.

Adaptive filters alter their parameters in order

to minimize a function of the difference between a

desired target output and their output. In the case of

acoustic echo in telecommunications, the optimal output

is an echoed signal that accurately emulates the

unwanted echo signal. This is then used to negate the

echo in the return signal. The better the adaptive filter

emulates this echo, the more successful the cancellation

will be. This paper examines various techniques andalgorithms of adaptive filtering, employing discrete

signal processing in MATLAB. Also noise cancellation

algorithms are implemented using simulink in MATLAB.

INTRODUCTION:

Acoustic echo occurs when an audio signal is

reverberated in a real environment, resulting in the original

intended signal plus attenuated, time delayed images of this

signal. This project will focus on the occurrence of acoustic

echo in telecommunication systems. Such a system consists ofcoupled acoustic input and output devices, both of which are

active concurrently. An example of this is a hands-free

telephony system. In this scenario the system has both an

active loudspeaker and microphone input operating

simultaneously. The system then acts as both a receiver and

transmitter in full duplex mode. When a signal is received by

the system, it is output through the loudspeaker into an

acoustic environment. This signal is reverberated within the

environment and returned to the system via the microphone
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input. These reverberated signals contain time delayed images

of the original signal, which are then returned to the original

sender (Figure 1, ak is the attenuation, tk is time delay). The

occurrence of acoustic echo in speech transmission causes

signal interference and reduced quality of communication.

FIGURE 1:Origins of acoustic echo

The method used to cancel the echo signal is known

as adaptive filtering. Adaptive filters are dynamic filters which

iteratively alter their characteristics in order to achieve an

optimal desired output. An adaptive filter algorithmically

alters its parameters in order to minimize a function of the

difference between the desired output d(n) and its actual

output y(n). This function is known as the cost function of the

adaptive algorithm. Figure 2 shows a block diagram of the

adaptive echo cancellation system implemented throughout

this paper. Here the filter H(n) represents the impulse response

of the acoustic environment, W(n) represents the adaptive

filter used to cancel the echo signal. The adaptive filter aims to

equate its output y(n) to the desired output d(n) (the signal

reverberated within the acoustic environment). At eachiteration the error signal, e(n)=d(n)-y(n), is fed back into the

filter, where the filter characteristics are altered accordingly.


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FIGURE 2: Block diagram of an adaptive echo cancellation system

This project deals with acoustic echo as applies to

audio signals, although the techniques will be applicable to avariety of other disciplines.The goals of this project are as follows: To examine adaptive filtering LMS technique as theyapply to acoustic echo cancellation and audio signals.

To simulate LMS algorithm using Matlab.

THEORY:

Echo is a phenomenon in which a delayed and distorted version of an original soundor electrical signal is reected back to the source. There are two main types of echo,namely network (or line) and acoustic echoes. As we are doing work on acoustic echo,the explanation of acoustic echo is that if a communication is between one or morehands-free telephones, thenacoustic feedback paths are set up between the telephone'sloudspeaker and microphoneat each end. This acoustic coupling is due to the reectionof the loudspeaker's soundwaves from walls, oor, ceiling, windows and other objectsback to the microphone. Thecoupling can also be due to the direct path from theloudspeaker to the microphone,see Figure 1. Adaptive cancellation of such acousticecho has became very importantin hands-free communication systems, e.g. tele-


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conference, video-conference and PCtelephony systems. The eects of an echodepend mostly on the time delay between the initial andreected sound waves (or soundsignals), and the strength of the reected sounds. In thecase of acoustic echo, if the

time delay is not long, then the echo can be perceived assoft reverberation, which addsartistic quality, for example in a concert hall. However, astrong echo that arrives a fewtens of milliseconds or more after the initial direct soundwill be highly undesirable andirritating.

RESULTS OF LMS ALGORITHM:The LMS algorithm was simulated using Matlab. Figure 2 shows the input speech signal which is collected from the

computer system through microphone. Figure 3 shows the desired echo signal derived from the input signal. Figure

4 shows the adaptive filter output which will reduce the echo signal from the input signal. Figure 5 shows the mean

square error signal calculated from the filter output signal. Figure 6 shows the attenuation which is derived from the

division of echo signal to the error signal.

The adaptive filter is a 1025th order FIR filter. The step size was set to 0.02. The MSE shows that as the algorithm

progresses the average value of the cost function decreases.


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FIGURE 2:INPUT SIGNAL


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FIGURE 3:DESIRED SIGNAL


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FIGURE 4:ADAPTIVE FILTER OUTPUT


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FIGURE 5:MEAN SQUARE ERROR


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FIGURE 6:ATTENUATION

RESULTS OF NLMS ALGORITHM:

The NLMS algorithm was simulated using Matlab. Figure 7 shows the input signal. Figure 8 shows the desired

signal. Figure 9 shows the adaptive filter output. Figure 10 shows the mean square error. Figure 11 shows the

attenuation.

The adaptive filter is a 1025th order FIR filter. The step size was set to 0.1.


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FIGURE 7:INPUT SIGNAL


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FIGURE 8:DESIRED SIGNAL


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FIGURE 9:ADAPTIVE FILTER OUTPUT


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Figure 10: Mean Square Error


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Figure 11: Attenuation

NLMS algorithm is having the advantage over the LMS algorithm incase of Mean square error

and Average attenuation and its summary of the performance is presented in Table 1.

Table 1. Summary of adaptive algorithms performance

ALGORITHMS ITERATIONS FILTERORDER

MEANSQUAREERROR

AVERAGEATTENUATIONS

COMPUTATIONS

LMS 7500 1025 0.001 -11.2435 2N+1

NMS 7500 1025 0.0004 -13.6812 3N+1

FUTURE WORK:

The future work to be done is about the same to input the audio file as a input for differentalgorithms namely NLMS (Normalized Least Mean Squares) Algorithm,VSSLMS (Variable

Step Size Least Mean Squares) Algorithm, VSSNLMS (Variable Step Size Normalized Least

Mean Squares) Algorithm and also with RLS Algorithm.Once determined the above mentioned


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parameters using different algorithms a comparison is done with respect to the calculations and

choosing the best for the abovementioned project and implementing the same using processor

or at the gate level implementation. There are many possibilities for further development inthis

discipline, some of these are as follows.The real time echo cancellation system canbeimplemented using the TI TMSC6711 DSK.

CONCLUSION:

In the present work, the MDF adaptive filter isimplemented on Cortex-M4 processor to

eliminate theacoustic echo of the far-end speaker. It requires less memorystorage, small FFT size. In

performance, the MDF adaptivefilter has a smaller block delay and is faster. This is achievedby

updating the weight vectors more often and reducing thetotal execution time in most of the

processor. Furthermore,the total number of blocks needed can be changed dynamically withoutinterrupting the normal operation. The MDF adaptive filter is most suitable for real-time

applications implemented on the hardware.

ACKNOLEDGEMENT:It is just because of Sir Shoaib, today we are able to make such a tough project. Who strive hard

on us to enhance our skills peculiar in MATLAB . He taught us in such a cordial enviroment

that difficult task also became easy. I also want to thanks Sir Umer Rahim for his efforts in thesignal and systems subject.

REFFERENCES:

[1]Homana, I.; Topa, M.D.; Kirei, B.S.; Echo cancelling using adaptive algorithms, Design

and Technology of Electronics Packages, (SIITME) 15th International Symposium., pp. 317-321, Sept.2009.

[2]. Paleologu, C.; Benesty, J.; Grant, S.L.; Osterwise, C.; Variable step-size NLMS algorithms

for echo cancellation 2009 Conference Record of the forty-third Asilomar Conference onSignals, Systems and Computers., pp. 633-637, Nov 2009.

[3]. 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.

[4]. Tandon, A.; Ahmad, M.O.; Swamy, M.N.S.; An efficient, low-complexity, normalized

LMS algorithm for echo cancellation, IEEE workshop on Circuits and Systems, 2004.

NEWCAS 2004, pp. 161-164, June 2004.

[5]. Eneman, K.; Moonen, M.; Iterated partitioned block frequency-domain adaptive filteringfor acoustic echo cancellation, IEEE Transactions on Speech and Audio Processing, vol. 11, pp.

143-158, March 2003.

Acoustic echo

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