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

1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
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 $l_1$-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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https://hal.inria.fr/hal-01430754
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Submitted on : Tuesday, January 10, 2017 - 11:18:18 AM
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Long-term archiving on: : Tuesday, April 11, 2017 - 2:11:10 PM

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Xiaofei Li, Laurent Girin, Radu Horaud. Audio Source Separation Based on Convolutive Transfer Function and Frequency-Domain Lasso Optimization. ICASSP 2017 - IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. pp.541-545, ⟨10.1109/ICASSP.2017.7952214⟩. ⟨hal-01430754⟩

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