Audio Source Separation Based on Convolutive Transfer Function and Frequency-Domain Lasso Optimization

Xiaofei Li 1 Laurent Girin 2, 1 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : This paper addresses the problem of under-determined convolutive audio source separation in a semi-oracle configuration where the mixing filters are assumed to be known. We propose a separation procedure based on the convolutive transfer function (CTF), which is a more appropriate model for strongly reverberant signals than the widely-used multi-plicative transfer function approximation. In the short-time Fourier transform domain, source signals are estimated by minimizing the mixture fitting cost using Lasso optimization, with a 1-norm regularization to exploit the spectral sparsity of source signals. Experiments show that the proposed method achieves satisfactory performance on highly reverberant speech mixtures, with a much lower computational cost compared to time-domain dual techniques.
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Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States. 2017
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Xiaofei Li, Laurent Girin, Radu Horaud. Audio Source Separation Based on Convolutive Transfer Function and Frequency-Domain Lasso Optimization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States. 2017. 〈hal-01430754〉

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