Near-field frequency domain blind source separation for convolutive mixtures

The paper presents a method for solving the permutation problem of frequency domain blind source separation (BSS) when source signals come from the same or similar directions. Geometric information such as the direction of arrival (DOA) is helpful for solving the permutation problem, and a combination of the DOA based and correlation based methods provides a robust and precise solution. However, when signals come from similar directions, the DOA based approach fails, and we have to use only the correlation based method whose performance is unstable. We show that an interpretation of the ICA solution by a near-field model yields information about spheres on which source signals exist, which can be used as an alternative to the DOA. Experimental results show that the proposed method can robustly separate a mixture of signals arriving from the same direction.

[1]  Shoko Araki,et al.  ARRAY GEOMETRY ARRANGEMENT FOR FREQUENCY DOMAIN BLIND SOURCE SEPARATION , 2003 .

[2]  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).

[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]  K. Matsuoka,et al.  Minimal distortion principle for blind source separation , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[5]  Hiroshi Sawada,et al.  Direction of arrival estimation for multiple source signals using independent component analysis , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

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

[7]  Christopher V. Alvino,et al.  Geometric source separation: merging convolutive source separation with geometric beamforming , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[8]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[9]  Hiroshi Sawada,et al.  Robust real-time blind source separation for moving speakers in a room , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[10]  Dennis R. Morgan,et al.  A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  K. Matsuoka,et al.  Blind Separation for Convolutive Mixture of Many Voices , 2003 .

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

[13]  Y. F. Huang,et al.  A robust method for wideband signal separation , 1993, 1993 IEEE International Symposium on Circuits and Systems.

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