Auditory-based Subband Blind Source Separation using Sample-by-Sample and Infomax Algorithms

We present a new subband decomposi- tion method for the separation of convolutive mix- tures of speech. This method uses a sample-by-sample algorithm to perform the subband decomposition by mimicking the processing performed by the human ear. The unknown source signals are separated by maximizing the entropy of a transformed set of signal mixtures through the use of a gradient ascent algo- rithm. Experimental results show the efficiency of the proposed approach in terms of signal-to-interference ratio. Compared with the fullband method that uses the Infomax algorithm, our method shows an impor- tant improvement of the output signal-to-noise ratio when the sensor inputs are severely degraded by ad- ditive noise.

[1]  Shoko Araki,et al.  Subband-Based Blind Separation for Convolutive Mixtures of Speech , 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[2]  Shoko Araki,et al.  The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech , 2003, IEEE Trans. Speech Audio Process..

[3]  Jiangtao Xi,et al.  Blind source separation of nonstationary convolutively mixed signals in the subband domain , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Sang-Hoon Oh,et al.  A filter bank approach to independent component analysis for convolved mixtures , 2006, Neurocomputing.

[5]  Douglas D. O'Shaughnessy,et al.  Auditory-based acoustic distinctive features and spectral cues for automatic speech recognition using a multi-stream paradigm , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Shoko Araki,et al.  Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[7]  Philipos C. Loizou,et al.  Subband-Based Blind Signal Processing for Source Separation in Convolutive Mixtures of Speech , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[8]  Anju Vyas.  Print , 2003 .

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