Blind Audio Source Separation (Bass): An Unsuperwised Approach

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Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Blind Audio Source Separation (Bass): An Unsuperwised Approach 1

2

Naveen Dubey1, Rajesh Mehra2

ME Scholar, Dept. Of Electronics, NITTTR, Chandigarh, India Associate Professor, Dept. Of Electronics, NITTTR, Chandigarh, India 1 naveen_elex@rediffmail.com

ABSTRACT: Audio processing is an area where signal separation is considered as a fascinating works, potentially offering a vivid range of new scope and experience in professional and personal context. The objective of Blind Audio Source Separation is to separate audio signals from multiple independent sources in an unknown mixing environment. This paper addresses the key challenges in BASS and unsupervised approaches to counter these challenges. Comparative performance analysis of Fast-ICA algorithm and Convex Divergence ICA for Blind Source Separation is presented with the help of experimental result.

Result reflects Convex Divergence ICA with α=-1 gives more accurate estimate in comparison of Fast ICA . In this paper algorithms are considered for ideal mixing situation where no noise component taken in to account.

Fig.1 BASS System Diagram

Index Terms: BASS, ICA, Fast-ICA, SIR, Convex Divergence, Entropy, Unsupervised Learning.

And the model for un-mixing using BASS

I. INTRODUCTION Blind separation of at a time active audio sources is very interesting area for researchers and is a popular task in field of audio signal processing motivated by many emerging applications , like distant-talking speech communication, human-machine applications, in intelligence for national security in call interception, handfree and so on[1].

The key objective of BASS is to retrieve ‘p’ audio source from a convolutive mixture of audio signals captured by ‘m’ microphone sensors, can be mathematically represented as.

Where:  ʘ denotes matrix convolution  t is the sample index  S(t)= [S1(t). . . . .Sp(t)]T is the vector of ‘p’ sources.  X(t)= [X1(t). . . . Xm(t)]T is observed signal from ‘m’ microphones.

p Mij 1

xi ( n )  

h

j 0 k 0

ij

(k ) s j n  k , i  1,....., m(1)

Where: Xi(n) : ‘m’ recorded audio (observed) signals Sj(n) : ‘p’ original (audio) signals.

The original signals Sj(n) are unknown in “blind” scenario.

In actual sense, the mixing system is a multi-input multioutput (MIMO) linear filter with source microphone impulse response hij, each of length Mij,[2].

The BASS system can be understood by another mathematical model of matrix convolution [3]. As the model for mixing X(t) = A(t) ʘ S(t) (2)

29

Sˆ (t )  W (t ) ʘ X(t)



(3)

Sˆ (t ) =[ Sˆ1(t ) . . . . Sˆp(t ) ]T is the output of

reconstructed sources.  A(t) is the M X P X L mixing array,  W(t) is the P X M X L un mixing array, A(t) and W(t) can also be considered as M X P and P X M matrices, where each element is an FIR filter of length L, [4].

Previously discussed model is a an ideal representation of BASS model where number of audio sources is equal to number of microphone sensors, termed as complete model or critically determined model. The modelling can be more complex for more practicability of application, as if number of microphone sensors more than number of audio source (m > p), termed as overdetermined or over complete model. If number of sources are greater than number of microphone sensors (p > m) , named as underdertermined or under complete model [5,6]. Inclusion of noise component and delay between microphones, echo makes BASS problem more complex.ICA is a dominant algorithm for blind source separation problem and based on metrics of likelihood function, negentropy, kurtosis and NITTTR, Chandigarh EDIT-2015

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minimum mutual information (MMI). The remaining content of this paper is organized as follows. Section II reviews of ICA algorithm. Section III reviews Fast-ICA and Convex Divergence ICA for BASS. Section IV summarizes the experiment on simulation and real data.

Conclusion drawn on the basis of experimental results in Section V. II. INDEPENDENT COMPONENT ANALYSIS A big challenge in statistics and concerned areas is to pick a suitable representation of multivariate data. Here representation stands for data transformation such that its essential, hidden structure is made more transparent or accessible. Blind Audio source separation considered as a convolutive mixture, as in equation (2) and to separate out source component estimate can be generated by equation (3). W(t) represents unmixing matrix and key objective of ICA algorithms to find out most accurate value of matrix W(t). It is analogous to designing of a neural structure to short out clustering problem and various learning methods can be adapted for updation of W(t). To implement ICA for BASS problem certain set of assumption and preprocessing needed.

A. ASSUMPTIONS AND AMBIGUITIES IN ICA FRAMEWORK There are certain assumptions of the signal characteristics to implement ICA in proper manner as pointed out The sources being considered are statistically independent.

Suppose there are two random variables x1 and x2. The random variable x1 is independent of x2, if the information content of x1 does not provide any information about x2 and vice versa. Here x1 and x2 are random signals generated from two different physical activities which are not related to each other.

X1 and x2 are said to be independent if and only if the expression for joint Probability Density function is:

Px1, x 2 ( x1, x 2)  p1 ( x1) p 2 ( x 2) The independent distribution.

component

(4) has

non-Gaussian

This assumption is very essential because it not possible to separate Gaussian signal using ICA framework. The sum of non- Gaussian signal signals is itself a Gaussian and it is the principle reason behind non separability of Gaussian signals. Kurtosis and entropy are the techniques to ensure non-Gaussianity of signals, described in next subsection.

The mixing matrix is invertible This assumption have very clear mathematical support that if mixing matrix is not invertible, then unmixing matrix we seek to estimate cam not even exist.

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ICA suffers from two inherent ambiguities; these are (i) permutation ambiguity and (ii) magnitude and scaling ambiguity. In ICA the order of the estimated independent components are not specified and due that the permutation ambiguity is inherent in BSS. This ambiguity is to be expected, so we do not impose any restriction on order and all permutations are equally valid. Magnitude and scaling ambiguity comes into the picture because true variance of the independent components cannot be estimated.

Fortunately in most applications this ambiguity is not significant and to avoid this assumption can be made that each sources has unit variance [6].

B. NON- GAUSSIANITY As per central limit theorem the nature of a sum of independent signals with arbitrary distribution tends towards a Gaussian distribution under specific conditions.

So Gaussian signal can be assumed as linear combinations of number of independent signals. The separation of independent signal can be achieved by making the linear signal transform as non-Gaussian as it could be. To ensure non-Gaussianity there are certain commonly used measures. i.

Kurtosis In probability theory kurtosis is a measure of “peakedness”. When data is preconditions to have unit variance, kurtosis of signal (x) can be calculated by fourth moment of data.

kurt( x)  E{x 4 }  3( E{x 4 }) 2

(5)

Here E{.}- Expectation Now if signal assumed having zero mean and ‘x’ has been normalized such that its variance is equal to one E{x2}=1.

kurt( x)  E{x 4 }  3.

(6) Gaussian nature of distribution can measured on the basis of kurtosis by following criteria’s If: Kurt(x) = 0 : x is Gaussian Kurt(x)>0 : x is super-Gaussian/ platy kurtotic Kurt(x)< 0:x is sub-Gaussian /lepto kutotic Kurtosis is a computationally simple process, as it has a linearity property. But kurtosis is sensitive to outlier data and its statistical significance is poor. Kurtosis is not enough robust for ICA. ii.

Entropy According to information theory, entropy termed as average amount of information contained in each message received. The minimum amount of mutual information ensures better separation along with non-Gaussianity.

Uniformity of signal corresponds to maximum entropy and entropy is considered as randomness of a signal. Entropy for a continuous valued signal (x), called the differential entropy, and is defined as 30

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H ( x)   p( x) log p( x)dx

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(7)

Highest value of entropy represents the Gaussian signal and low value of entropy shows the spiky nature of signal.

In ICA estimated non-Gaussianity must be ensured, which is zero for Gaussian signal and non zero for non-Gaussian signal. Hence entropy minimization is a prime concern in ICA estimation. A normalized version of entropy gives a new measure for non-Gaussianity termed as Negentropy J which is defined as,

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Fixed point algorithms are based on the mutual information minimization. This can be written as I (x) 



f x ( x ) log

f x (x) dx  f xi ( xi )

(12)

(8) For Gaussian signal negentropy is zero and nonGaussianity achieved by negentropy maximization.

Minimization of mutual information leads to ICA solution.

For MI minimization negentropy needs to minimized [7].For the estimation of negentropy, the pdf estimation of the random vector variable required and it is hard to obtain by calculation. Hyvarinen [8] proposed a method to calculate negentropy. Let ‘x’ be a whitened random variable. Then the approximation of J(x) is given by

C.

J ( x) ( E{G( x)}  E{G(u)}) 2

J ( x )  H ( Xgauss )  H ( x )

ICA PREPROCESSING

Before implementing ICA algorithms certain preprocessing steps are carried out. i.

Centering It is a commonly performed pre-processing step to centre the observation vector X by subtracting its mean vector m=E{x}. The centered observation vector can be presented as follows

Xc  x  m

(9) The mixing matrix remains same after this pre-processing, so unmixing matrix can be estimated by centered data after then actual estimated can be derived. ii.

Whitening Whitening the observation vector X is a very useful practice. Whitening involves linearly transforming the observation vector such that its components are uncorrelated and have unit variance [4].The whitening vector satisfies the following relationship

E{x w x wT }  I ..

(10) A simple approach to perform the whitening transformation is to apply eigenvalue decomposition (EVD)[]of x.

E{xx T }  VDV T Here:

(11)

E ( xx T } : co variance matrix of x V: eigenvector of

E ( xx T }

D:diagonal matrix of eigenvalues Whitening is very simple and efficient process that significantly reduces the computational complexity of ICA. III. ICA ALGORITHMS A. FAST ICA Fast ICA is a fixed point algorithm that applies statistics for the recovery of independent source components. Fast ICA uses a simple estimate of Negentropy based on negentropy maximization that requires the use of appropriate non-linearities for unsupervised learning rules of neural networks [10].

31

(13)

Where G(.) is a nonquadratic function and g(.) is first derivative G(.), u is a Gaussian variable with unit variance and zero mean. Nonlinear parameter for convergence is g(.) should grow slowly as given[2].

g1 ( x)  x 3 g 2 ( x )  tanh( x )

(14)

Iteration for unmixing matrix given as #Choose an initial weight matrix W+ For i=1:1++: C While W+ changes 

Wi 

1 1 T T  xg (W i x ) T  g ' (W i x )W i M M i 1

W i  W i   W i T W 





k

W



k

k 1

Wi 

Wi Wi



Output : W  W 1    W c Sˆ  Wx



T

B. CONVEX DIVERGENCE Convex divergence is a learning algorithm through minimizing a divergence measure D(x,W) given a unmixing matrix W and a set of M- dimensional input observations x={x1,. . . . . . . ,xn}. Data is pre-processed by centering and whitening . The unmixing matrix can be estimated by the gradient descent method [9].

W ( i  1)  W ( i )  

 D ( x , W ( i )) .  W (i )

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Where ‘i’ denotes iteration number and η denotes the learning rate. Stopping criteria is when the absolute increment of divergence measures meets a predefined cut off. During the learning in each epochs weight normalized by { Wi

 Wi / Wi }

In Convex divergence ICA (C-ICA), The convex divergence contrast function Dc(x,W,α) is developed with a convexity parameter α as

2 1

2

k 1

M



 1   M   p (Wx k ) 2      l 1 

(1   ) 2

 p (W l x k ))  l 1  (1     2  }.......( 16 ) p (W l x k    

1   2  2 (  p (Wx    n

k

IV. EXPERIMENT AND RESULT In order to perform test on Blind Audio Source Separation algorithms three signals were taken. First signal S1 is a male voice recording of durations 0.03 second from Ghost buster movie, one female voice recording of same duration S2 from movie Pet Detective. Third signal S3 is recording of aeroplanes sound of same duration (downloaded from http://www.wav-sounds.com/movie_wav_sounds.htm).

These three signal were mixed by a random 3X3 mixing matrix. First mixture shown in figure.1 was separated by Fast ICA taking g(x)= x3and Convex divergence algorithm by taking α=1 and α=-1.

Fig 5: S3 Source separated by Fast ICA

Results are shown in table.1.

S.No Algorithm S1 1 Fast ICA 19.80

S2 17.70

S3 15.35

CD-ICA 24.45 25.20 α=1 3 CD-ICA 28.20 27.92 α=-1 Table1: SIR of recovered signals in dB.

22.44

2

27.34

SIR value of recovered signals is low in case of Fast ICA and SIR is comparatively high in case of Complex Divergence ICA. CD-ICA with α = -1 gives more SIR improvement than α=1. Comparison chart is shown in figure.5.

Fig .6: Comparison Chart

Fig.2: Mixed signal of S1,S2,S3 by random mixing matrix

Fig 3: S1 Source signal separated by FastICA

V. CONCLUSION Blind Audio Source Separation is being done by FastICA and Convex divergence ICA for determined mixture in which three source signals are recorded by three microphone sensors. The results reflecting that the Convex divergence ICA gives better performance than fast ICA and for -1 convergence factor gives good SIR improvement by 6.35 dB average. In this paper an ideal mixing model was considered due that resulting SIR is low. The performance of algorithms can be improved and more accurate estimation can be done by considering mixing model including noise components X=A*S+Є Here Є is additional noise in mixing.

REFERENCES

Fig 4: S2 Source signal separated by FastICA

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1. Koldovsky Zbynek,Tichavsky Petr “Time- Domain Blind Separation of Audio Sourceson the Basis of a Complete ICA Decomposition of an Observation Space”, IEEE Transaction on Audio, Speech and Language Processing, Vol. 0 No. 0, PP 01-11 ,2010.

2. Chien Jen-Tzung,Hsieh Hesin-Lung “Convex Divergence ICA for Blind Source Separation”, IEEE Transactions on Audio, Speech, And Language Processing ,Vol.20 No.1, PP.302-313, January,2012

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3. Fu Gen-Shen et al. “Complex Indpendent Component Analysis Using Three Type of Diversity: Non-Gaussianity, Nonwhiteness, and Noncircularity” IEEE Transactions on Signal Processing, Vol 63, No.3,PP.794-805 Feb 2015 4. Vincent Emmanuel, Bertin Nancy, G. Remi, Bimbot Frederic “From Blind to Guided Audio Source Separation”, IEEE Signal Processing Magazine,PP. 107-115, May,2014 5. Naik R. Ganesh, Kumar K Dinesh “An Overview of Independent Component Analysis and Its Applications”, Iformatica 35, PP.6381,2011 6. Emmanuel Vincent, Rémi Gribonval, and Cédric Févotte,” Performance Measurement in Blind Audio Source Separation” IEEE Transactions on Audio, Speech, and Language processing, vol. 14, no. 4,PP 1462-1469,July 2006 7. S.L. Lin and P.C Tung “Application of modified ICA to secure communication in chaotic systems” Lecture Notes in Computer Science, vol.4707, pp.431-444, 2007.

8. A. Hyvarine, J. Karhuenen and E. Oja, “Independent Component Analysis”.John Wiley& Sons, New York,2001.

9. S. Amari, “Natural gradient efficiency in learning” Neural Computing, vol.10, pp.251-276, 1998 10. Zhiming Li and Genke Yang, “Blind separationof Mixed Audio Signals Based on Improved Fast ICA”, CISP, pp.1638-1642, 2013 11. C.D. Meyer, Matrix Analysis and Applied linear Algebra, Cambridge, UK,2000.

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