A structured nonnegative matrix factorization for source separation Reportar como inadecuado

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1 LTCI - Laboratoire Traitement et Communication de l-Information 2 L2S - Laboratoire des signaux et systèmes 3 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data Inria Saclay - Ile de France 4 labROSA

Abstract : In this paper, we propose a new unconstrained nonnegative matrix factorization method designed to utilize the multilayer structure of audio signals to improve the quality of the source separation. The tonal layer is sparse in frequency and temporally stable, while the transient layer is composed of short term broadband sounds. Our method has a part well suited for tonal extraction which decomposes the signals in sparse orthogonal components, while the transient part is represented by a regular nonnegative matrix factorization decomposition. Experiments on synthetic and real music data in a source separation context show that such decomposition is suitable for audio signal. Compared with three state-of-the-art har-monic-percussive decomposition algorithms, the proposed method shows competitive performances. Index Terms— nonnegative matrix factorization, projec-tive nonnegative matrix factorization, audio source separation , harmonic-percussive decomposition.

Autor: Clément Laroche - Matthieu Kowalski - Helene Papadopoulos - Gaël Richard -

Fuente: https://hal.archives-ouvertes.fr/


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