 Open Access
 Total Downloads : 20
 Authors : Megha S. Karabashetti, Dr. Shridhar S. K
 Paper ID : IJERTCONV7IS10001
 Volume & Issue : NCRACES – 2019 (Volume 7, Issue 10)
 Published (First Online): 20062019
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Speech Enhancement using Multiband Spectral Subtraction with Cross Spectral Component Reduction
Megha S. Karabashetti
Department of Electronics and Communication Engineering,
Basaveshwar Engineering College Bagalkot
Dr. Shridhar S. K.
Department of Electronics and Communication Engineering,
Basaveshwar Engineering College Bagalkot
Abstract To preserve the message content, it is necessary to improve the quality and intelligibility of the speech signal. Quality of the speech signal can be improved by enhancing the noisy speech signal. This paper presents two algorithms to reduce the additive background noise considerably.First method is modified multiband spectral subtraction to reduce additive noise, which is nonstationary with respect to the speech signal. In this method spectral subtraction is performed based on the SNR values in different frames of the noisy speech.Secondmethod is implemented to reduce the cross spectral components, where the noise signal is correlated in some extentwith noisy signal.These methods are implemented to overcome the limitations of basic spectral subtraction method. Both the methods are combined to enhance the noisy speech signal.
Keywordsspectral subtraction, cross spectral components, SNR value, multiband spectral subtraction.

INTRODUCTION
Speech has been evolved as the primary way of communication. In any communication system speech signal is usually accompanied by the background noise, which not only affects the task of listening but also degrades the performance of digital signal processor. Hence it is necessary to reduce the background noise for the effective communication.
Speech enhancement is one among the processes to improve the quality, intelligibility, perceptibility of the speech signal, by the reduction of background noise from noisy speech signal. Some of the applications of speech enhancement are, in Mobile communication, Tele communication, Hearing aids, Recording systems, Tele conferencing. This paper provides algorithms for the reduction of additive noise, such as Babble noise, White noise, Flicker noise, etc. Both the algorithms are improvised versions of Basic spectral subtraction method.
In paper [1] Boll presentsdifferent techniques to reduce additive noise one such method is basic spectral subtraction method, which involves the subtraction of power spectral magnitude of the estimated noise from that of the noisy speech signal to obtain the speech signal. There are two assumptions made during the process. First one is, speech signal assumed
to be stationary on short time basis. Second assumption is that the noise signal is uncorrelated to the speech signal. Noise signal is estimated from the silent periods, where there is the absence of the speech signal. In practical these assumptions are not true in all cases; noise is not uniformly distributed throughout the noisy signal. Thus this method of speech enhancement introduces the musical noise.
To overpower the basic spectral subtraction method and to avoid the musical noise that can be caused due to this method, proposed work introduces two speech enhancement algorithms. The First method is modified multiband spectral subtraction technique. This algorithm is implemented to process the noisy speech signal degraded by the additive noise, where speech signal is non stationary [13]. Spectral subtraction is performed on the basis of the SNR of the current frame. Complete details of the method are given in the section1.
Second method involves the computation of the correlation between the speech signal and noise signal, to process the noisy speech signal corrupted by noise correlated to the speech signal. The computation details are explained in the section2.

Modified multiband spectral
Let s(n)be the noisy speech signal to be enhanced, corrupted by the additive noise d(n). And c(n) be the clean speech signal. Hence S(n) is given by,
s(n) = c(n) + d(n)(1)
let s(n) be converted into transfer domain and is written as
S(f) = C(f) + D(f)(2)
Power spectrum of the corrupted speech signal is given as
S (f)2 = C(f)2+ (f)2 + C (f). (f)*+ C (f)*. (f)(3)
According to the assumption made in basic spectral subtraction method noise signal is uncorrelated to the
corrupted signal, C (f). (f)*+ C (f)*. (f)terms in the equation (3) are neglected. Thus clean speech C(f) can be obtained by the equation as follows,
C(f)2=S (f)2  (f)2 (4)
Where is the over spectral subtraction factor estimated by the equation (6), is the spectral floor factor whose value is
0.002 given in the paper [3]. is correlation factor which gives the estimate of the correlation between the noisy speech signal and estimated noise signal. Equation to compute is
But in this method it is assumed that noise is uniformly distributed throughout the corrupted speech signal, which is
(8)
not possible in practical aspects. Hence if we follow the same sd s d
method it subtracts the same amount of, estimated noise from the noisy speech signal. To avoid this another method of speech enhancement is required, where noise to be subtracted depends on the SNR in the corresponding portion of the signal S(n). Modified multiband spectral subtraction is performed to compute the over subtraction factor which depends on the
where,
sd
1 S (k ) D(k )
SNR value. Thus clean speech can be computed by
introducing over subtraction into equation (4) factor can be
sd N / 2 k
given by,
1 Y (k )
(9)
S N / 2 k
1
C(f)2=S (f)2  (f)2 (5)
In paper [13] the author has given the relationship between and SNR. The relation is given as,
D(k
d N / 2 k
Where s, dare the mean of noisy speech signal and noise signal respectively where 0 < k < N/2 , N being the size of
FFT. And 2, 2 are the variances of the corrupted speech
s d
5 SNR< 5
= 4 3/20 (SNR) 5< SNR< 20 (6)
1 SNR > 20

Crosscorrelation technique
In equation (3) C (f). (f)*+ C (f)*. (f) are considered as the cross correlation terms, which are neglected in spectral subtraction technique. But in real time applications there is certain amount of correlation between speech signal and noise. Hence it is necessary to find these correlation terms, rcdand rdc
Respectively, but we dont have access to the clean speech hence we can find the correlation between corrupted speech signal and noise signal. ie., ryd. where rsdis gives
Rsd = rcd + rdd
rsdgives required correlation between clean speech signal and noise signal. Paper [6] gives equation for correlation parameter introduced into equation (5) as follows,
C(f)2=
S (f)2  (f)2 S (f)* (f) if S (f)2> (f)2  (f)2 else (7)
signal and estimated noise signal.


PROPOSED WORK
Initially noisy speech signal is divided into frames of20ms (160 samples per frame). Hamming Window is used for this purpose (with 160 window size). Windowing method may introduce spectral leakages at the edges of the window, which will cause loss of information, hence to avoid the same50% overlapping is done before processing of the signal. Windowed noisy speech signal can be written as
Sw(n) = s(n) * w(n)
From equation (1)
Sw(n) = [c(n) + d(n) ] * w(n)
= cw(n) + dw(n)
FFT of the noisy speech signal is computed followed by the comutation of the power spectrum magnitude as in the equations (2) and (3). In modified multiband spectral subtraction each frame magnitude spectrum of the noisy speech signal is divided into bands with 40 samples each. Spectral subtraction is performed separately for these bands based on there SNR values using the equations (5) and (6) by computing the value of over subtraction factor.
Now finally by using equations (8) and (9) , correlation factor is calculated, and by using equation (7). Magnitude spectrum of the clean speech is obtained.
Magnitude spectrum estimated clean speech signal and unchanged phase spectrum of the original speech signal are combined to form complex spectrum. Inverse Frequency Fourier transform is performed to convert complex spectrum into time domain signal. As 50% overlapping is used in the framing process, 50% overlap adding is done to get the enhanced speech signal.
The Fig1 block diagram shows different steps involved in the implementation of proposed method.
Fig.1 Block diagram for the proposed method.

RESULTS
In this paper subjective listening test and spectrogram analysis is used for the assessment of speech quality. By using these analysis methods performance of the proposed method is compared with the existing speech enhancement techniques.
In subjective listening test processed speech is compared with the unprocessed speech signal, with the help of listeners. listeners are allowed to rate the speech quality based on a predefined scale.
Spectrogram is the timefrequency representation of any speech signal, where frequency of the signal vary as the time varies. color of the spectrogram represents the energy of the speech at that frequency. Dark color depicts that the speech signal is of high energy.
Fig.2signal 1 0dB SNR noisy speech with Babble noise
Fig.3signal enhanced by multiband spectral subtraction
Fig.4signal enhanced by the proposed method
Fig.5signal 2 – 15 dB SNR noisy speech signal with Babble noise
Fig.6signal 2 enhanced by the proposed method
Fig.2 and Fig.5 shows spectrogram analysis of 0dB and 15dB noisy speech signal corrupted by Babble noise respectively.
And the Fig.2 and Fig.6 shows the spectrogram analysis of the enhanced speech signals. It is observed that the speech quality has been increased by using proposed method. Mean opinion of subjective listening test of modified multiband spectral subtraction for signal with 0dB and 15dB SNR is 2.7 and 2.6 (moderate) respectively. Mean opinion of the proposed method for signal with 0dB and 15dB SNR is 3.7 and 3.6 respectively (greater than that of previous method).
CONCLUSION
Problems and limitations of the basis spectral subtraction method is considered in this paper. In this paper we have performed multiband spectral subtraction by computing the value of over subtraction factor. Further cross spectral components were computed by crosscorrelation technique. By the result analysis, it is concluded that the quality of the speech signal has increased by the proposed method than that in the spectral subtraction method.
ACKNOWLEDGEMENT
The authors would like to thank Dr. Shridhar K. sir for there helpful and fruitful guidance during the course of the work. The authors also thank authorities of Basaveshwar Engineering college for providing all required facilities during the course of this work.
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