An adaptive stereo basis method for convolutive blind audio source separation

Authors:
Maria G. Jafari;Emmanuel Vincent;Samer A. Abdallah;Mark D. Plumbley;Mike E. Davies
Affiliations:
Centre for Digital Music, Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK;METISS Project, IRISA-INRIA, Campus de Beaulieu, 35042 Rennes cedex, France;Centre for Digital Music, Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK;Centre for Digital Music, Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK;IDCOM and Joint Research Institute for Signal and Image Processing, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, UK
Venue:
Neurocomputing
Year:
2008

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Abstract

We consider the problem of convolutive blind source separation of stereo mixtures, where a pair of microphones records mixtures of sound sources that are convolved with the impulse response between each source and sensor. We propose an adaptive stereo basis (ASB) source separation method for such convolutive mixtures, using an adaptive transform basis which is learned from the stereo mixture pair. The stereo basis vector pairs of the transform are grouped according to the estimated relative delay between the left and right channels for each basis, and the sources are then extracted by projecting the transformed signal onto the subspace corresponding to each group of basis vector pairs. The performance of the proposed algorithm is compared with FD-ICA and DUET under different reverberation and noise conditions, using both objective distortion measures and formal listening tests. The results indicate that the proposed stereo coding method is competitive with both these algorithms at short and intermediate reverberation times, and offers significantly improved performance at low noise and short reverberation times.