seminar presentation u10ec107

8/13/2019 Seminar Presentation U10EC107

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SEMINAR ONACOUSTIC ECHO CANCELLATION

Presented By: Guided By:

Amrendra Kumar Mishra Prof. P.K.Shah

B.Tech-IV Associate professorU10EC107 ECED,SVNIT



OUTLINE

• Introduction

• Acoustic Echo Modeling

• Components of AEC• Algorithms for AEC

• MATLAB Simulation

• Uses & Features• Conclusion



WHAT IS ACOUSTIC ECHO ?

Acoustic echo is a time delayed andattenuated version of original speechproduced by a sound source.

Echo is produced due to reflection oforiginal sound from nearby objects ofsound source.

In order to be sensed by human minimum

additional pressure level must be greater

than 20μ pascal at the vicinity of ear ofunderlying person.



Continued..

If a person perceives a original sound andit’s echo with an interval of less than 100ms it go unnoticed due to limited capacityof human hearing.

considering above condition if at roomtemperature speed of sound is 340 m/s andthe reflecting object is d distance apartfrom original sound source ,for detection ofecho by a typical human being

(2*d)/velocity of sound > 100 ms.Placingthe proper values distance calculates out tobe 17 m.



Acoustic echo in voice communication

Consider the given situation. A mobileuser in handset mode.



Mobile user in handsfree mode

Acoustic coupling



How to combat this echo?

As, echoes are attenuated and time delayed version of originalsignal voice of far end talker can be processed and a duplicatecopy of the echo based on estimation of echo path can beproduced and be subtracted from overall speech signal toobtain the desired signal for transmission and at the sametime echoes can be prevented from propagating further incommunication network.



How should adaptive filter be designed ?

In order to design adaptive filter we will have to modelthe echo mathematically to produce an almost exactreplica of the original sound.

Mathematically if x(t) is the original signal then it’s

one of the components of echo can be represented asa1x(t-t1) where a is the attenuation factor and t1 is thedelay encountered by the sound after reflecting froma surface. In case of multiple path available forreflection the composite signal at the input of

microphone can be written.C(t) = x(t) + a1 x(t-t1) + a2 x(t-t2) + a3 x(t-t3) + ……......an x(t-tn)



Acoustic Echo Path Modeling

The acoustic echo path of the near end talkercan be modeled as a linear system having timevarying impulse response h j[n], where j depicts

the coefficient index of h at time n. Given the speech sequence x[n], the resultant

acoustic echo y[n] is:

y[n]=Σ hi[n]x[n-i]Where hi[n] models the impulse response ofthe acoustic path (i.e. from microphone tospeaker at time n).



Room Impulse Response and it’sEstimation

Acoustic modeling of room as a system.



Impulse response of a room.

If a person produces acoustic impulse in front ofmicrophone microphone won’t receive that impulsedirectly it would rather receive the signal coming fromdifferent paths having reflected off different surfaces. So

the signal sensed by microphone would be as that ofshown



Block diagram

Complete block diagram for AEC



Adaptive filter

This is the backbone of entire AEC system. In case ofacoustic echo cancellation we use FIR filter due to it’sstability. Adaptive filter employed in AEC estimates echopath based on a raw signal and with it’s comparison withthe received signal in a closed room environment.

let x be the far end signal vector. H is the room impulseresponse, y be the near end signal that consists ofacoustic echo along with voice of near end talker. H, thetransfer function of adaptive filter which has n number of

coefficients depending upon the order of the filter used.Coefficients of the filters keep on changing based on theecho estimation by the adaptive filter.



Filter structure



How to update coefficients ofadaptive filter ?

There are 6 algorithms used forupdating coefficients of the filter:

Least mean square algorithm

Normalized mean square algorithm

Affine projection algorithm

Fast affine projection algorithm

Recursive least square algorithm Variable step size FAP algorithm



Least mean square algorithm

In 1959 Widrow and Hoff introduced the LMS algorithm.During the years this has been the by far most usedadaptive filtering algorithm for several reasons; it wasthe first, it requires relatively little computation and it

works well, at least for slow changes in the filter.

C(n) = E ( e(n)2 )Where c(n) is called as cost function.

C(n) is minimized to estimate impulse response of room



Implementation of LMS algorithm

Each iteration of LMS algorithm willcarry out following procedure:

1. The output of the FIR filter, y(n) iscalculated using equation:



Continued..

2. The value of the error estimation iscalculated using equation

e(n)=c(n)-y(n) For entire input vector square of e(n) is

calculated and added. Thereafteriteration keeps on happening and when

the mean square error is achieved acertain value adaption stops.



Continued..

3. Lastly, the filter coefficients for nextiteration is calculated using followingformula:

Where μ is called step size and isfixed for LMS algorithm



NLMS algorithm & it’s implementation

NLMS algorithm is just anadvancement of LMS algorithm.

Steps for performing iteration: Output of the filter is calculated

according to following equation:



Continued..

An error signal is calculated as thedifference between the desired signaland the filter output.

Where d(n) and y(n) hold same logical meaning

as in case of LMS algorithm.



Continued..

The step size value for the inputvector is calculated.

Where x(n) is input vector and

superscript T denotes transpose ofmatrix X.



Continued..

Filter tap weights are updatedaccording to the following equation



Comparison between LMS & NLMS

For 1025 order and 7500 iteration below isthe quantitative comparison between bothalgorithms.

Courtesy:S.Haykin, “Adaptive Filter Theory”, PrenticeHall, 1996



Double talk detector

AEC should be able to perform it’s task that whencommunication is in fully duplex nature that implies thatboth the ends are allowed to talk at the same instant,and therefore the name DT- Double Talk. The signalreceived by the microphone will not only comprise echo,

but it will also contain the NET speech signal, if doubletalk exists. The near end signal acts as an uncorrelatednoise as to the adaptive algorithm and so it could allgive rise to the divergence of the adaptive filter. It istherefore necessary to find the existence of double talkin order to seize the process of adaption.



How to decide DT ?

There are two main algorithm:

Geigel algorithm

Variance impulse response algorithm.



Geigel algorithm

when the consented microphoneestimation tear by the maximum ofthe previous far-end samples is

greater than precise threshold, theDT is declared.

ξ > T, Double talk exist

ξ ≤ T ,Double talk doesn’t exist



Variance Impulse Response Algorithm

A newly used method that employs the highest value of theadaptive filter coefficients is VIRE DTD. The adaptive filter’svariance is what designs the VIRE (Variance impulse responsealgorithm) of recent times. Random variations are produced in thetaps of the adaptive filter because the near end speech signal actslike a degrading noise. The adaptive filter’s optimal coefficient areused so as to calculate the fluctuations along with the exponentialforgetting parameter:

By defining certain threshold for the fluctuations ofthe adaptive filter taps, the DT decisionis made as following:

ξ > T Double Talk exist

ξ ≤ T Double Talk doesn’t existDT detection is always frame based. At the end ofeach frame it will be computed.

Therefore the frame length is generally small



Non Linear processor

Due to quantization of analog speechthere exist some residual echo whichis afterwards removed by Non Linear

processor.

Although it is not a part of acousticecho canceller, it is often inbuilt in

AEC.



Simulation in MATLAB

Random room impulse response



Continuted..

Near end talker speech sample



Continued..

Far end echoed signal



Micro phone signal



Output of echo canceller



Industry perspective of AEC

Adaptive digital technologies haspatented the AEC & manufactures

AEC-GEN 2 in software codec.

Texas instrument too has developedan AEC kit TMS320C6711 which canprovide echo return loss upto 50

decibel.



Typical Feature of an AEC

Courtesy:http://www.adaptivedigital.com/pdfspecs/adt_aec-g2.pdf



Applications:

Hands free telephony

Teleconferencing

Howling rejector VOIP

Audio tone Data transfer



Conclusion

In the era of frequent use of hands free acoustic couplingof mc-phone and speaker is quite natural so AECavailability in relevant devices is must.

Along with AEC efficient noise suppression must also becarried out by AEC.

Presently 128 ms tail length echo can be nullified whichis expected to increase a bit further.

Advent of more robust DSP processor and efficientalgorithms are likely to enhance speech quality to agreat extent.

Employing sophisticated techniques echo return loss of90 decibel is quite likely to be achieved in upcoming fewyears.



THANK YOUHOPE YOU ENJOYED

seminar presentation u10ec107

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