Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation

Matthieu Kowalski 1 Emmanuel Vincent 2 Rémi Gribonval 2
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming known mixing filters. State-of-the-art methods operate in the time-frequency domain and rely on narrowband approximation of the convolutive mixing process by complex-valued multiplication in each frequency bin. The source signals are then estimated by minimizing either a mixture fitting cost or a l1 source sparsity cost, under possible constraints on the number of active sources. In this article, we define a wideband l2 mixture fitting cost circumventing the above approximation and investigate the use of a l1,2 mixed-norm cost promoting disjointness of the source timefrequency representations. We design a family of convex functionals combining these costs and derive suitable optimization algorithms. Experiments indicate that the proposed wideband methods result in a signal-to-distortion ratio improvement of 2 to 5 dB compared to the state-of-the-art on reverberant speech mixtures.
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Matthieu Kowalski, Emmanuel Vincent, Rémi Gribonval. Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2010, 18 (7), pp.1818 - 1829. ⟨10.1109/TASL.2010.2050089⟩. ⟨hal-00435897v3⟩

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