Underdetermined BSS with multichannel complex NMF assuming W-disjoint orthogonality of source

This paper presents a new method for underdetermined Blind Source Separation (BSS), based on a concept called multichannel complex non-negative matrix factorization (NMF). The method assumes (1) that the time-frequency representations of sources have disjoint support (W-disjoint orthogonality of sources), and (2) that each source is modeled as a superposition of components whose amplitudes vary over time coherently across all frequencies (amplitude coherence of frequency components) in order to jointly solve the indeterminacy involved in the frequency domain underdetermined BSS problem. We confirmed experimentally that the present method performed reasonably well in terms of the signal-to-interference ratio when the mixing process was known.

[1]  Hirokazu Kameoka,et al.  Formulations and algorithms for multichannel complex NMF , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[3]  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.

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

[5]  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.

[6]  Kiyohiro Shikano,et al.  Real-Time Implementation of Two-Stage Blind Source Separation Combining SIMO-ICA and Binary Masking , 2005 .

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

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

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

[10]  Hirokazu Kameoka,et al.  Complex NMF: A new sparse representation for acoustic signals , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Hirokazu Kameoka,et al.  A sparse component model of source signals and its application to blind source separation , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Kazuya Takeda,et al.  Evaluation of blind signal separation method using directivity pattern under reverberant conditions , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

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

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