Auditory-based subband blind source separation using sample-by-sample and Infomax algorithms

Salem, Abderraouf Ben
Selouani, Sid Ahmed
Hamam, Habib
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We present a new subband decomposition method for the separation of convolutive mixtures 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 algorithm. 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 important improvement of the output signal-to-noise ratio when the sensor inputs are severely degraded by additive noise.
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in The 2010 International Conference of Signal and Image Engineering (2010), available online at:
Convolutive mixture , Convolutive sources , Ear model , Full band , Gradient ascent , Human ear , Infomax algorithm , Infomax algorithms , Output signal-to-noise ratios , Sensor inputs , Signal to interference ratio , Source signals , Sub-bands , Subband decomposition , Signal to noise ratio , Blind source separation
Salem, A. B., Selouani, S.-A., & Hamam, H. (2010). Auditory-based subband blind source separation using sample-by-sample and Infomax algorithms. In WCE 2010 - World Congress on Engineering 2010 (Vol. 1, pp. 651–655).