An auxiliary-function approach to online independent vector analysis for real-time blind source separation

This paper proposes online independent vector analysis (IVA) based on an auxiliary-function approach for real-time blind speech separation. A batch auxiliary-function approach is naturally extended with autoregressive approximation of an auxiliary variable. Experimental evaluations show that the proposed online algorithm works in real time and attains relatively high signal-to-interference ratios without environment-sensitive tuning parameters such as step size under both spatially stationary and dynamic conditions compared to usual real-time IVAs using natural gradient updates or block-wise updates. Our implementation of the proposed algorithm works in real-time for four-channel observations on PCs and worked stably over 7 hours in realistic noisy environments.

[1]  Ono Nobutaka,et al.  Online independent vector analysis with incremental updates of weighted covariance , 2013 .

[2]  Nobutaka Ono,et al.  Stable and fast update rules for independent vector analysis based on auxiliary function technique , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[3]  Emmanuel Vincent,et al.  A General Framework for Online Audio Source Separation , 2012, LVA/ICA.

[4]  George K. Kokkinakis,et al.  Blind speech separation of moving speakers in real reverberant environments , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[5]  Masahito Togami,et al.  Online speech source separation based on maximum likelihood of local Gaussian modeling , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  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)..

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

[8]  Nobutaka Ono,et al.  Auxiliary-Function-Based Independent Component Analysis for Super-Gaussian Sources , 2010, LVA/ICA.

[9]  S. Haykin.  Nonlinear Methods of Spectral Analysis , 1980 .

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

[11]  Ingvar Claesson,et al.  Complex-Valued Independent Component Analysis for Online Blind Speech Extraction , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Rémi Gribonval,et al.  Performance measurement in blind audio source separation , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Taesu Kim,et al.  Real-Time Independent Vector Analysis for Convolutive Blind Source Separation , 2010, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  Te-Won Lee,et al.  Independent Vector Analysis: An Extension of ICA to Multivariate Components , 2006, ICA.

[15]  Hiroshi Sawada,et al.  Blind Source Separation for MOving Speech Signals Using Blockwise ICA and Residual Crosstalk Subtraction , 2004 .

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

[17]  Nobutaka Ono,et al.  Auxiliary-function-based independent vector analysis with power of vector-norm type weighting functions , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.