Blind Audio Source Separation Using Sparsity Based Criterion for Convolutive Mixture Case

Abdeldjalil Aïssa-El-Bey, Karim Abed-Meraim, Yves Grenier

Abstract: In this paper, we are interested in the separation of audio sources from their instantaneous or convolutive mixtures. We propose a new separation method that exploits the sparsity of the audio signals via an ℓ p -norm based contrast function. A simple and efficient natural gradient technique is used for the optimization of the contrast function in an instantaneous mixture case. We extend this method to the convolutive mixture case, by exploiting the property of the Fourier transform. The resulting algorithm is shown to outperform existing techniques in terms of separation quality and computational cost.