A novel approach for design of a speech enhancement system using auto-trained NLMS adaptive filter

Int. J. of Information and Communication Technology, Vol. x, No. x, xxxx 1

A Novel Approach for Design of a SpeechEnhancement System using Auto-TrainedNLMS Adaptive Filter

Sivaranjan Goswami

Department of Electronics and Communication EngineeringDon Bosco College of Engineering and TechnologyGuwahati, Indiaemail: [email protected]

Bijeet Bardoloi


Pinky Deka


Darathi Dutta


Dipjyoti Sarma

Assistant ProfessorDepartment of Electronics and Communication EngineeringDon Bosco College of Engineering and TechnologyGuwahati, Indiaemail: [email protected]

Copyright c© 2009 Inderscience Enterprises Ltd.

Int. J. Information and Communication Technology, Vol. 6, Nos. 3/4, 2014, pp: 326-338

2 S. Goswami, B. Bardoloi, P.Deka, D. Dutta and D. Sarma

Abstract: Speech signal often gets degraded by various types of noiseat different stages of speech recording, processing and communicationsystems. One major source of noise is the background noise, whichis nothing but the other acoustic sound waves from various sourcesthat enters the system along with the original signal through themicrophone. Adaptive filters are found to be very reliable and effectivein speech enhancement. However, one major problem is training. Fortraining, we must have a priori knowledge of the noise-free speech.But practically, it is not possible to have a priori knowledge of thespeech that has been recorded in a particular background. Many newtechniques for speech enhancement has been evolved which involvea wide range of training and collection of large libraries for speechenhancement. However, this types of technique usually require largememory and processing time. Thus these techniques are not reliable forreal time application. The work here proposes an adaptive filter basedalgorithm for enhancement of the noisy speech. The only requirementis a clean short voice record of the speaker which should not necessarilycontain the same words spoken in the noisy speech. The work proposedhere increases the output SNR by around 1.5 times the input SNR andalso enhances the listening comfort of the speech. Thus it is a veryrobust technique for speech enhancement for both real time speechand also for recorded speech which does not require any library orstatistical processing.

Keywords: Adaptive Filter, Speech Enhancement, Training, LMS,RLS, NLMS

1 Introduction

At present, there are a number of electronic devices used for recording andtransmitting speech signals. The main problem with these devices is that thebackground noise as well as other disturbances also add up with the speechsignal being recorded or transmitted and corrupt it. With the advancement oftechnology, a clean and clear speech signal is required for better hearing andbetter performance of various smart systems using voice command. There havebeen many experiments to remove this unwanted part of the signal. It is notalways possible to manually design a filter since exact measurement of all frequencycomponents of both speech and noise is a difficult task. Adaptive filter therefore isa good alternative.

Adaptive filters are widely in use for speech enhancement. Adaptive filtersusually require training in the same background-voice combination for actuallyperforming in a particular environment. Some techniques involve spectral analysis(1) or stochastic analysis which demand large memory and processing time. Againanother approach to speech enhancement using dictionary (2) also requires a widerange of training and maintaining of a large library for speech enhancement. Anadaptive filter based algorithm has been proposed in this work which does not


A Novel Approach for Design of a Speech Enhancement System using Auto-Trained NLMS Adaptive Filter3

require any priory knowledge of the background noise. The only requirement is aclean short voice record of the speaker which should not necessarily contain thesame words spoken in the noisy speech. The work described here is based on theextraction of the noise reference from the input speech and training the adaptivefilter automatically. The input signal is then filtered with the filter parametersobtained.

In the following section a brief introduction and classification is discussed. It isfollowed by the application of adaptive filters in speech enhancement in section 3.Section 4 contains a brief review of the most commonly used adaptive algorithms inthe field of speech processing. The problem definition and the proposed algorithmis discussed in section 6 which is followed by experimental results in sections 7 andfinally the conclusion in 8.

2 Adaptive Filters

An adaptive filter is a computational device that attempts to model therelationship between two signals in real time in an iterative manner (3). Anadaptive filter consists of two main parts – a digital filter (usually FIR) and anupdate algorithm. The working of the two sections are properly discussed in thefollowing section. An adaptive filter is defined by four aspects(3):

1. the signals being processed by the filter

2. the structure that defines how the output signal of the filter is computedfrom its input signal

3. the parameters within this structure that can be iteratively changed to alterthe filter’s input-output relationship

4. the adaptive algorithm that describes how the parameters are adjusted fromone time instant to the next

3 Adaptive Filters for Speech Enhancement

Adaptive filters are widely in use for elimination of noise from speech signal. Thefrequency components of both speech signal and the noise are usually not known.It is therefore an easy and convenient way to use adaptive filters for such purposes.The block diagram of an adaptive filter for noise cancellation is shown in figure 1.Initially, the parameters of the digital filter are initialized with zeros. The inputsignal x(n) is nothing but the clean signal s(n) which is corrupted by some noisen0(n) that needs to be filtered out. y(n) is the output of the filter. It is subtractedfrom s(n) to calculate the error e(n). In every step the filter parameter vector Wis updated. The process is repeated until the system converges, that is, the errore(n) reduces to its minimum value. The function of the adaptive algorithm is toperform this operation. Many adaptive filtering algorithms are available. Out ofthem, three algorithms commonly used for speech enhancement are Least MeanSquare (LMS) adaptive filter, Recursive Least Square (RLS) adaptive filter andNormalized Least Mean Square (NLMS) adaptive filter. A comparative discussionof these filters for speech enhancement will be made in section 4.



Figure 1 Block Diagram of Adaptive Filter

3.1 Training of Adaptive Filter

From figure 1 it can be understood that the original clean speech s(n) is requiredfor calculation of the error e(n) in an adaptive filter. Without e(n) the adaptivealgorithm cannot determine the filter parameters for the next iteration. However,practically s(n) is not available. The only signal that we get from the microphoneis x(n) = s(n) + n0(n). From which we need to estimate s(n). That is, the filteroutput y(n) = s(n) is an estimate of s(n). If we already know s(n) then there is nouse of enhancing the speech signal. Therefore, the adaptive filter must be trainedbefore it is practically used in real applications.

During the training phase, the actual clean speech s(n) is required for updatingthe filter parameters in each iteration. Once the filter output converges, that is,e(n) ≈ 0 or y(n) = s(n) ≈ s(n) then the training is said to be complete. After thetraining is complete, the adaptive algorithm block of figure 1 is removed and weare left with only the digital filter. The digital filter is now supposed to eliminatethe noise added by a particular noise source to any speech signal. This filter nowcan be used for practical application.

It is understood from this discussion that the filter must be trained in aparticular background or in the presence of a particular noise source beforepractically applying the filter. If the filter is trained for a noise source A and thenoise to the speech is due to a different source say B then the filter cannot enhancethe speech signal.



4 Adaptive Filter Algorithms

As shown in figure 1 the most unique and important block of an adaptive filteris the Adaptive Algorithm block. An adaptive filter is classified according to thealgorithm used in this block. A number of such algorithms are available. Everyalgorithm has its own pros and cons. Most of the time the main trade off is betweenconvergence time and computational complexity (4). Another important factor isstability of the filter. The most commonly used adaptive filtering algorithms forspeech processing are

• Least Mean Square (LMS) algorithm

• Recursive Least Square (RLS) algorithm

• Normalized Least Mean Square (NLMS) algorithm

A comparative discussion of all these three algorithms has been done by AllamMousa et. al. in (5). In this section all these three algorithms will be discussedin brief along with their mathematical expressions. Based on the pros and consthe conclusion regarding selection of adaptive filter for our task will also be drawnin this section. It is to be noted that all filters considered here are All Zero FIRFilters.

4.1 Least Mean Square (LMS) Algorithm

This is a very popular adaptive filtering algorithm. It is suitable for use in variousreal time operation because of its simplicity. The update algorithm is given byequation 1.

w(n + 1) = w(n) + µ.e.x(n) (1)

where, µ is the step size. w(n) is the filter co-efficient vector and x(n) is the inputvector. The cost function J(n) is given by equation 2.

J(n) = e2(n) (2)

where, e(n) = y(n) − s(n) is the error signal at time n.Although LMS algorithm is simple and easy to implement in real time,

however, its convergence time is very high, that is, more number of iterations arerequired for convergence.

4.2 Recursive Least Square (RLS) Algorithm

The update algorithm is given by equation 3.

w(n + 1) = w(n) + e(n).k(n) (3)

where w(n) is given by equation 4

k(n) =P (n).x(n)

λ + xT (n).P (n).x(n)(4)



λ is the forgetting factor and P (n) is the inverse correlation matrix. The initialvalue at n = 0

P (0) =

δ−1 0 . . 00 δ−1 . . 0. . . . .. . . . .0 0 . . δ−1

where δ is the regularization factor. P(n) is iteratively updated using theequation 5

P (n + 1) = λ−1.P (n)− λ−1.k(n).xT (n).P (n) (5)

In RLS algorithm the cost function is given by equation 6 as follows:

J(n) =1N

N−1∑

i=0

λ2.e2(n− i) (6)

Where N is the filter length and barx(n) is the input vector.RLS algorithm converges at much fewer number of iterations, however, its

computational complexity is high. Therefore it is difficult to implement in realtime.

4.3 Normalized Least Mean Square (NLMS) Algorithm

It is a modified version of LMS algorithm. The computational complexity isslightly increased. The update equation is given by equation 7

w(n + 1) = w(n) +µ.e.x(n)‖x(n)‖2 (7)

Where ‖x(n)‖2 is the sum of the squares of all the elements of the input vectorx(n) at an instant n.

The NLMS algorithm converges at less number of iterations compared to bothLMS and RLS. It also has the advantage of having minimum value of mean squareerror (MSE). The MSE is maximum of the three for RLS algorithm. ThereforeNLMS algorithm has been selected for all the experiments of this work.

5 Experimental Details

This section provides a detailed discussion of the experimental setup used inthe work. The work is mainly based on NLMS adaptive filter. In subsection 5.1selection of step size and order of the NLMS adaptive filter is discussed, followedby the methods used for performance evaluation in subsection 5.2.



5.1 Selecting Step Size and Filter Order

The step size µ and the order N of an adaptive filter are very crucial. Since NLMSalgorithm is derived from LMS algorithm, so here we will primarily discuss LMSalgorithm. Adaptive filters like LMS, RLS, NLMS etc. are usually all zero FIRfilters. Thus the filtering is performed using the relation given in equation 8 bellow:

y(n) =L∑

k=0

w(k)x(n− k) (8)

Where, L is the order of the signal, x(n) is the input signal and y(n) is theoutput of the filter. Thus it is obvious that as the order of the filter increases,the number of iteration increases and so increases the computation time. The timerequired for convergence also increases with the filter order. However, the filterorder cannot be too small as well.The misadjustment increases as the filter lengthincreases. In the experiment we have observed that as we increase the filter sizefrom a small value to a large value, the MSE first decreases and then suddenlyincreases.

Another important parameter is step size. For stable output the step sizeshould approximately lie within the range given by equation 9:

0 < µ <1

λmax(9)

Where λmax is the maximum wavelength of the input signal x(n) and theapproximate sufficient condition for step size is given by equation 10 (6).

0 < µ <1

L.σ2x

(10)

In speech signal, the minimum frequency is 300 Hz. The wavelengthcorresponding to 300 Hz frequency is approximately 1.1 m. Thus, according toequation 9 the step size should be less than 0.909.

5.2 Performance Evaluation

The performance has been evaluated based on Mean Square Error (MSE) andSignal to Noise Ratio (SNR). The MSE of a signal should be small whereas theSNR should be high.

5.2.1 Measurement of MSE

For calculation of MSE, a clean speech s(n) is first mixed with noise to producenoisy signal [x(n) = s(n)+noise] and then the algorithm is applied to obtain noisefree signal y(n) which is an estimate of s(n) from x(n). MSE is calculated usingthe relation given by equation 11.

MSE(n) =

N∑0

[y(n)− s(n)]2

N(11)

Where N is the total number of samples in s(n).



5.2.2 Measurement of SNR

In our experimental set up, we have the desired signal s(n) which is free from anykind of background noise. The speech and silent samples of this signal can be easilyidentified by observing the short time average magnitude and short time averagezero-crossing rate. Thus the speech and silent parts can be easily classified. s(n) islinearly mixed with a noise signal to find x(n) and y(n) is generated by filteringx(n). Hence there is a one-to-one correspondence among the samples of s(n), x(n)and y(n). Thus if set Ss contains the speech samples of s(n) and Ns contain thesilent samples of s(n) then the sets Sx and Sy will contain the noise-mixed speechcontents of x(n) and y(n) respectively. Similarly sets Nx and Ny will contain thesamples of x(n) and y(n) having only noise. Therefore,

SNR of x(n) =P0(Sx)− P0(Nx)

P0(Nx)(12)

Similarly,

SNR of y(n) =P0(Sy)− P0(Ny)

P0(Ny)(13)

Where the notation P0(A) denotes total power of the samples in a set A.

6 Proposed Algorithm

In this section the proposed algorithm is discussed in details. First the problemhas been defined and then the proposed solution to the problem is discussed indetails.

6.1 Problem Definition

As discussed is section 3.1 training is a very important step of speech enhancementusing adaptive filter. It can be easily understood that an adaptive filter must betrained in the same background, having exactly the same noise sources. Otherwiseit will be of no use. However, practically, it is difficult and rather impracticalto train the adaptive filter for every possible background or noise source. Theselection of the appropriate parameter for a particular background is also achallenge in this case. So the objective of the work is to automatically train theadaptive filter for any noise source.

6.2 Proposed Solution

Usually we do not start talking immediately after turning on a recorder ormicrophone. Let us assume that the first 0.5 second of a speech signal does notcontain any information. But it will contain the noise of the source or backgroundnoise that is corrupting the entire speech signal. If x(n) be the signal receivedfrom the microphone, then the samples x(0) to x(Fs/2− 1) will contain onlybackground noise, where Fs is the sampling rate. Now let us consider that we have



Figure 2 Algorithm for creating noise signal n0(n)

a clean speech s0(n) of length T0 second (T0 > 0.5) which is sampled at the samesampling rate Fs. Now we can generate a noisy version of the clean signal s0(n)using equation 14 as shown bellow:

x0(n) = s0(n) + n0(n), ∀{n : 0 ≤ n ≤ Fs × T0} (14)

Here, n0 is a noise reference generated from the 0.5 second noise using equation15 as shown bellow:

n0(n) = x(mod(n, Fs/2)), ∀{n : 0 ≤ n ≤ Fs × T0} (15)

Where,

mod(a, b) = b× k − a, where k is an integer (16)

The process for generation of noise n0(n) from the initial 0.5 second of x(n) isillustrated in figure 2.

Now we have all the necessary signals for training an adaptive filter. x0(n)is the noisy speech, s0(n) is the clean speech. It is to be noted that the noisen0(n) of this noisy signal is from the same source that has corrupted the originalinput signal x(n). Therefore, if we filter the input signal x(n) using these filtercoefficients, the noise will be reduced.

Basically, the approach is to train the adaptive filter automatically for exactlythe same background for which it is intended to work. No priory knowledge of thespeech or the noise is required. The only additional requirement is the clean speechsignal s0(n). The steps involved in the algorithm can be summarized as shownbellow:



1. Create the noise reference n0(n) using equation 15 as shown in figure 2.

2. Mix the noise and the known speech s0(n) and the noise reference n0(n) toobtain x0(n) as shown in equation 14.

3. Initialize all the parameters of the all zero FIR filter w(n) with zeros.

4. Apply NLMS algorithm to update the filter parameters using equation 7until the filter converges.

5. Filter the original input speech signal x(n) with W (n).

6. Measure Performance.

7 Experimental Results and Discussions

The proposed algorithm has been tested for speech corrupted with real backgroundnoise by mixing speech signal and noise signal. Various experiments are performedto study the variation of MSE and SNR with varying step size and order of thefilter. The details of the experimental results are discussed in this section.

7.1 Experimental Results

Figure 3 shows the variation of the MSE of the NLMS output with varying filterorder and step size. It is observed that the MSE first decreases with increasingorder, reaches a minimum value and then increases. However, the MSE is less thanthat of the input noisy signal only when step size is less. The details are as shownin table 1. The MSE of the input noisy signal is mse(x) = 0.0054. From the tableit can be concluded that MSE is minimum for step size (µ)=0.0010 and order=50.

Table 1 Variation of MSE with increasing filter order at different step sizes (µ).

Order µ=0.0010 µ=0.0015 µ=0.0022 µ=0.003235 0.0054 0.0065 0.0082 0.010440 0.0039 0.0046 0.0061 0.008045 0.0037 0.0041 0.0048 0.006250 0.0036 0.0040 0.0047 0.006055 0.0041 0.0041 0.0044 0.004955 0.0041 0.0042 0.0044 0.004960 0.0053 0.0056 0.0060 0.0066

Figure 4 shows the variation of SNR with order and step size of the NLMSadaptive filter. It is observed that the SNR of the filter output becomes muchhigher than the input SNR for all orders and step-sizes. The SNR of the inputnoisy speech is 6.493 dB. It is also observed that for the point at minimum MSEdiscussed above, the SNR is found to be 16.3192 dB which exceeds the input SNRby around 1.5 times.

The result obtained when a speech recorded in an environment with realbackground noise is shown in figure 5. From the waveform and by hearing, it canbe understood that the speech has been enhanced to a satisfactory level.



10 20 30 40 50 60 70 80 90 100

MSE with varying Order

Order

Input MSEµ=0.0010µ=0.0015µ=0.0022µ=0.0047µ=0.0319

Figure 3 Variation of MSE with filter order at various step size for voice of sameperson in s(n) and s0(n)

10 20 30 40 50 60 70 80 90 100

SNR with varying Order

Order

Input SNR

µ=0.0010

µ=0.0015

µ=0.0022

µ=0.0047

µ=0.0319

Figure 4 Variation of SNR (in dB) with filter order at various step size for voice ofsame person in s(n) and s0(n)



0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−1

−0.5

0

0.5

1

Time (sec)

ma

gn

itu

de

Enhanced Speech

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5−1

−0.5

0

0.5

1Input Noisy Speech

Time (sec)

ma

gn

itu

de

Figure 5 Result obtained when a speech recorded in presence of real-worldbackground is given as input

7.2 Discussion

The experimental result shows that the MSE is reduced and SNR is increased whenthe order of the filter and step size of the adaptive algorithm are properly selected.The only requirement is a clean speech recording of the same speaker for s0(n).When s(n) and s0(n) contain voice of different speaker, then the performance isnot satisfactory. It is because of the fact that during training, the parametersof the filter are so adjusted that the frequency components containing noise arestopped and those containing only speech are passed. Hence, another factor thatmay determine the performance of the system is the noise. The noise and thespeech should not necessarily contain the same frequency components. In this casealso the performance will not be satisfactory. The reference speech should notnecessarily be that same words spoken by the speaker, however, it must contain allpossible sounds, so that all possible frequency components of the speaker’s voiceare included in the pass-band of the filter.

This method is much efficient in real-time as well as off-line applicationcompared to many common approaches to speech enhancement. In (7) a twomicrophone based approach has been proposed to enhance speech signal in realtime. It estimates the noise from the spatial information of the position of themicrophones and performs a spectral subtraction for enhancement. Although theapproach is good, the hardware requirement is more and it cannot enhancerecorded speech since the spatial information is not be available for a recordedspeech.

Dictionary learning based approaches, as in (2) are much effective and givesgood results. However, this type of system has more memory requirement and



processing time hence not affordable using low-cost hardware real-time application.The proposed algorithm is thus a good alternative for both for real time and forenhancing recorded speech at a much lower cost.

In (8) Ali O. Noor et. al. have developed an optimized subband noise cancellerfor speech enhancement. Here the several band pass filter is used to divide thesignal into several subbands and a LMS adaptive filter is used in each subband.The authors claim that the speech is enhanced and performance is better thanfullband adaptive filters. However, in the work the filters are not automaticallytrained. This approach may be combined with our algorithm for automaticallytrain the adaptive filters of each band. Although the performance is better,computational complexity in case of multiple subband approach is more since weneed to use more than one adaptive filters.

8 Conclusion

In this work, an algorithm for automatic training of adaptive filters for speechenhancement has been proposed. Although the algorithm has been implementedfor NLMS adaptive filter, it can be used for any other adaptive filter whichrequire training. It is seen that the proposed algorithm is simple and accuracyis also high. It is observed from the experimental work that for the point atminimum MSE, the SNR is found to be increased by around 1.5 times to the inputSNR. The algorithm is suitable for both online real-time application and offlinerecorded speech application. After the filter parameters are obtained, the onlytask left is filtering, which consists of only multiplication and addition operation.The algorithm has been implemented in a software environment and since thecomputational complexity is less, we are also working to implement it in a low-costmicrocontroller for real time application.

References

[1] Lu-ying Sui, Xiong-wei Zhang, Jian-jun Huang, Bin Zhou “An Improved

Spectral Subtraction Speech Enhancement Algorithm under Non-stationary Noise,

International Conference on Wireless Communications and Signal Processing, 2011

(IEEE), pp: 1-5, year 2011

[2] C. D. Sigg, T. Dikk and J. M. Buhmann ”Speech Enhancement Using Generative

Dictionary Learning”, IEEE Transactions on Audio, Speech, and Language

Processing, Vol. 20, No. 6, pp 1698-1712, 2012

[3] S.C. Douglas ”Digital Signal Processing Handbook”, Ed. Vijay K. Madisetti and

Douglas B. Williams, Boca Raton: CRC Press LLC, 1999, available at http://www.

dsp-book.narod.ru/DSPMW/18.PDF

[4] S. A. Hadei, M. Lotfizad, A Family of Adaptive Filter Algorithms in Noise

Cancellation for Speech Enhancement, International Journal of Computer and

Electrical Engineering, Vol. 2, No. 2, April 2010

[5] Allam Mousa, Marwa Qados, Sherin Bader, Speech Signal Enhancement Using

Adaptive Noise Cancellation Techniques, Canadian Journal on Electrical and

Electronics Engineering Vol. 3, No. 7, September 2012



[6] S. C. Douglas, Markus Rupp, Convergence Issues in the LMS Adaptive Filter, CRCPress LLC, Available at: http://www.dsp-book.narod.ru/DSPMW/19.PDF, 2000

[7] K. Li, Y. Guo, Q. Fu and Y. Yan, A Two Microphone-Based Approach for SpeechEnhancement in Adverse Environments IEEE International Conferene on ConsumerElectronics(ICCE), 2012

[8] A. O. A. Noor, S. A. Samad and A. Hussain, Noise cancellation in Speechusing Optimized Subband Adaptive Filtering, Proceedings of 2009 IEEE StudentConference on Research and Development (SCOReD 2009), 16-18 Nov. 2009, UPMSerdang, Malaysia

[9] N. Chatlani and J. J. Soraghan, EMD-Based Filtering (EMDF) of Low-FrequencyNoise for Speech Enhancement, IEEE Transactions on Audio, Speech and LanguageProcessing, Vol. 20, No. 4, May 2012

[10] S. Jimaa, Convergence Evaluation of a Random Step-Size NLMS AdaptiveAlgorithm in System Identification and Channel Equalization ; Khalifa Universityof Science, Technology and Research (KUSTAR), UAE; Available at: http:

//cdn.intechopen.com/pdfs/17782/InTech-Convergence_evaluation_of_a_

random_step_size_nlms_adaptive_algorithm_in_system_identification_and_

channel_equalization.pdf

[11] Dr. I. M. Rupp, Signal Processing, Advanced Course:ADAPTIVE FILTERS,Institute of Telecommunications, Vienna University of Technology, PreleminaryVersion, Summer Semester, Available at: http://www.nt.tuwien.ac.at/

fileadmin/topics/design_methodology/lectures/adaptive_filters/AF_2011/

AF-LN-PRELIMINARY-web.pdf, 2011

[12] Q. Zeng and W. Abdulla, Speech Enhancement by Multi-Channel CrosstalkResistant Adaptive Noise Cancellation, Faculty of Engineering Papers, TheUniversity of Auckland, Available at: http://homepages.engineering.auckland.

ac.nz/~wabd002/Speech\%20Enhancement\%20by\%20Multi-Channel.pdf, 2006

[13] G. Dheepak, A. Anandraj and G. Deena, Blind Adaptive Equalization for ChannelNoise Cancellation; International Journal of Engineering Science and Technology(IJEST); Vol. 3 No. 1, cc: 341-345, 2011

[14] De. Wang, U. Kjems, M. S. Pedersen and J. B. Boldt; Speech intelligibility inbackground noise with ideal binary time-frequency masking ; J. Acoust. Soc. Am. 125(4), pp: 2336-2448, 2009


A novel approach for design of a speech enhancement system using auto-trained NLMS adaptive filter

Documents

Transcript of A novel approach for design of a speech enhancement system using auto-trained NLMS adaptive filter