Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment

This paper presents a blind source separation method for convolutive mixtures of speech/audio sources. The method can even be applied to an underdetermined case where there are fewer microphones than sources. The separation operation is performed in the frequency domain and consists of two stages. In the first stage, frequency-domain mixture samples are clustered into each source by an expectation-maximization (EM) algorithm. Since the clustering is performed in a frequency bin-wise manner, the permutation ambiguities of the bin-wise clustered samples should be aligned. This is solved in the second stage by using the probability on how likely each sample belongs to the assigned class. This two-stage structure makes it possible to attain a good separation even under reverberant conditions. Experimental results for separating four speech signals with three microphones under reverberant conditions show the superiority of the new method over existing methods. We also report separation results for a benchmark data set and live recordings of speech mixtures.

[1]  Hiroshi Sawada,et al.  A Two-Stage Frequency-Domain Blind Source Separation Method for Underdetermined Convolutive Mixtures , 2007, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[2]  Hiroshi Sawada,et al.  Measuring Dependence of Bin-wise Separated Signals for Permutation Alignment in Frequency-domain BSS , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[3]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[4]  Özgür Yilmaz,et al.  Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[5]  Alexey Ozerov,et al.  Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Hiroshi Sawada,et al.  MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization , 2007, EURASIP J. Adv. Signal Process..

[7]  Barak A. Pearlmutter,et al.  Soft-LOST: EM on a Mixture of Oriented Lines , 2004, ICA.

[8]  Andreas Ziehe,et al.  An approach to blind source separation based on temporal structure of speech signals , 2001, Neurocomputing.

[9]  Te-Won Lee,et al.  Blind Source Separation Exploiting Higher-Order Frequency Dependencies , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Barak A. Pearlmutter,et al.  The LOST Algorithm: Finding Lines and Separating Speech Mixtures , 2008, EURASIP J. Adv. Signal Process..

[11]  Arthur H. M. van Roermund,et al.  Unsupervised adaptive filtering, volume I: blind source separation [Book Review] , 2002, IEEE Circuits and Devices Magazine.

[12]  Lars Kai Hansen,et al.  Blind Separation of More Sources than Sensors in Convolutive Mixtures , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[13]  Hiroshi Sawada,et al.  Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

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

[15]  Daniel P. W. Ellis,et al.  Model-Based Expectation-Maximization Source Separation and Localization , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  B. Kollmeier,et al.  Convolutive blind source separation of speech signals based on amplitude modulation decorrelation , 2000 .

[17]  Don H. Johnson,et al.  Array Signal Processing: Concepts and Techniques , 1993 .

[18]  Daniel P. W. Ellis,et al.  An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments , 2006, NIPS.

[19]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  Hiroshi Sawada,et al.  Evaluation of separation and dereverberation performance in frequency domain blind source separation , 2004 .

[21]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[22]  DeLiang Wang,et al.  Speech segregation based on sound localization , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[23]  Lucas C. Parra,et al.  Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

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

[25]  Yutaka Kaneda,et al.  Sound source segregation based on estimating incident angle of each frequency component of input signals acquired by multiple microphones , 2001 .

[26]  Te-Won Lee,et al.  Blind Speech Separation , 2007, Blind Speech Separation.

[27]  Emmanuel Vincent,et al.  The 2008 Signal Separation Evaluation Campaign: A Community-Based Approach to Large-Scale Evaluation , 2009, ICA.

[28]  Shigeki Sagayama,et al.  Sparseness-Based 2CH BSS using the EM Algorithm in Reverberant Environment , 2007, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[29]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[30]  Paris Smaragdis,et al.  Blind separation of convolved mixtures in the frequency domain , 1998, Neurocomputing.

[31]  Birger Kollmeier,et al.  Amplitude Modulation Decorrelation For Convolutive Blind Source Separation , 2000 .

[32]  Hiroshi Sawada,et al.  Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors , 2007, Signal Process..

[33]  Hiroshi Sawada,et al.  A robust and precise method for solving the permutation problem of frequency-domain blind source separation , 2004, IEEE Transactions on Speech and Audio Processing.

[34]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[35]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[36]  Emmanuel Vincent,et al.  First Stereo Audio Source Separation Evaluation Campaign: Data, Algorithms and Results , 2007, ICA.

[37]  Atsuo Hiroe,et al.  Solution of Permutation Problem in Frequency Domain ICA, Using Multivariate Probability Density Functions , 2006, ICA.

[38]  Scott Rickard,et al.  Blind separation of speech mixtures via time-frequency masking , 2004, IEEE Transactions on Signal Processing.