Extension of sparse, adaptive signal decompositions to semi-blind audio source separation - Archive ouverte HAL Access content directly
Conference Papers Year : 2009

Extension of sparse, adaptive signal decompositions to semi-blind audio source separation

(1) , (2) , (1)
1
2

Abstract

We apply sparse, fast and exible adaptive lapped orthogonal transforms to underdetermined audio source separation using the time-frequency masking framework. This normally requires the sources to overlap as little as possible in the time-frequency plane. In this work, we apply our adaptive transform schemes to the semi-blind case, in which the mixing system is already known, but the sources are unknown. By assuming that exactly two sources are active at each time-frequency index, we determine both the adaptive transforms and the estimated source coefficients using l1 norm minimisation. We show average performance of 12-13 dB SDR on speech and music mixtures, and show that the adaptive transform scheme offers improvements in the order of several tenths of a dB over transforms with constant block length. Comparison with previously studied upper bounds suggests that the potential for future improvements is significant.
Fichier principal
Vignette du fichier
nesbit_ICA09.pdf (159.18 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

inria-00544153 , version 1 (07-12-2010)

Identifiers

  • HAL Id : inria-00544153 , version 1

Cite

Andrew Nesbit, Emmanuel Vincent, Mark D. Plumbley. Extension of sparse, adaptive signal decompositions to semi-blind audio source separation. 8th Int. Conf. on Independent Component Analysis and Signal Separation (ICA), Mar 2009, Paraty, Brazil. pp.605--612. ⟨inria-00544153⟩
157 View
274 Download

Share

Gmail Facebook Twitter LinkedIn More