Measure of Quality of Source Separation for Sub- and Super-Gaussian Audio Mixtures

Conventional Blind Source Separation (BSS) algorithms separate the sources assuming the number of sources equals to that of observations. BSS algorithms have been developed based on an assumption that all sources have non-Gaussian distributions. Most of the instances, these algorithms separate speech signals with super-Gaussian distributions. However, in real world examples there exist speech signals which are sub-Gaussian. In this paper, a novel method is proposed to measure the separation qualities of both super-Gaussian and sub-Gaussian distributions. This study measures the impact of the Probability Distribution Function (PDF) of the signals on the outcomes of both sub and super-Gaussian distributions. This paper also reports the study of impact of mixing environment on the source separation. Simulation improves the results of the separated sources by 7 dB to 8 dB, and also confirms that the separated sources always have super-Gaussian characteristics irrespective of PDF of the signa ls or mixtures.

[1]  Amor Chowdhury,et al.  GSM Speech Coder Indirect Identification Algorithm , 2010, Informatica.

[2]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[3]  Shlomo Dubnov,et al.  Speech Source Separation in Convolutive Environments Using Space-Time-Frequency Analysis , 2006, EURASIP J. Adv. Signal Process..

[4]  Te-Won Lee,et al.  A Maximum Likelihood Approach to Single-channel Source Separation , 2003, J. Mach. Learn. Res..

[5]  Heinz Mathis,et al.  Joint diagonalization of correlation matrices by using gradient methods with application to blind signal separation , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[6]  M. Joho,et al.  Joint diagonalization of correlation matrices by using Newton methods with application to blind signal separation , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[7]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[8]  Tapani Ristaniemi,et al.  CDMA delay estimation using fast ICA algorithm , 2000, 11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No.00TH8525).

[9]  Asoke K. Nandi,et al.  Blind separation of independent sources for virtually any source probability density function , 1999, IEEE Trans. Signal Process..

[10]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[11]  Visa Koivunen,et al.  Identifiability, separability, and uniqueness of linear ICA models , 2004, IEEE Signal Processing Letters.

[12]  Rainer Goebel,et al.  Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers , 2007, NeuroImage.

[13]  Terrence J. Sejnowski,et al.  Unsupervised Classification with Non-Gaussian Mixture Models Using ICA , 1998, NIPS.

[14]  Laurenz Wiskott,et al.  CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization , 2004, IEEE Transactions on Signal Processing.

[15]  Eric Moreau,et al.  A generalization of joint-diagonalization criteria for source separation , 2001, IEEE Trans. Signal Process..

[16]  Yannick Deville,et al.  Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation , 2009, ICA.

[17]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[18]  Clemens Brunner,et al.  Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data , 2009, Comput. Intell. Neurosci..

[19]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .

[20]  Matthew T. Sutherland,et al.  Validation of SOBI components from high-density EEG , 2005, NeuroImage.

[21]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[22]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[23]  Antanas Lipeika,et al.  Optimization of Formant Feature Based Speech Recognition , 2010, Informatica.

[24]  James V. Stone.  Independent component analysis: an introduction , 2002, Trends in Cognitive Sciences.

[25]  Kevin Wilson,et al.  Speech Source Separation by Combining Localization Cues with Mixture Models of Speech Spectra , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[26]  É. Moulines,et al.  Second Order Blind Separation of Temporally Correlated Sources , 1993 .

[27]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[28]  Rémi Gribonval,et al.  Audio source separation with a single sensor , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[29]  D. Chakrabarti,et al.  A fast fixed - point algorithm for independent component analysis , 1997 .

[30]  Andrzej Cichocki,et al.  Second Order Nonstationary Source Separation , 2002, J. VLSI Signal Process..

[31]  Andreas Ziehe,et al.  A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation , 2004, J. Mach. Learn. Res..

[32]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[33]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[34]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[35]  Jonathan Foote,et al.  An overview of audio information retrieval , 1999, Multimedia Systems.

[36]  Satoru Morita,et al.  Sound Source Separation of Trio using Stereo Musig Sound Signal Based on Independent Component Analysis , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[37]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[38]  Juan José Murillo-Fuentes,et al.  Optimal Pairwise Fourth-Order Independent Component Analysis , 2006, IEEE Transactions on Signal Processing.

[39]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[40]  Zoran H. Peric,et al.  Forward Adaptation of Novel Semilogarithmic Quantizer and Lossless Coder for Speech Signals Compression , 2010, Informatica.