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An overview of informed audio source separation

By Antoine Liutkus, Jean-Louis Durrieu, Laurent Daudet and Gaël Richard

Abstract

International audienceAudio source separation consists in recovering different unknown signals called sources by filtering their observed mixtures. In music processing, most mixtures are stereophonic songs and the sources are the individual signals played by the instruments, e.g. bass, vocals, guitar, etc. Source separation is often achieved through a classical generalized Wiener filtering, which is controlled by parameters such as the power spectrograms and the spatial locations of the sources. For an efficient filtering, those parameters need to be available and their estimation is the main challenge faced by separation algorithms. In the blind scenario, only the mixtures are available and performance strongly depends on the mixtures considered. In recent years, much research has focused on informed separation, which consists in using additional available information about the sources to improve the separation quality. In this paper, we review some recent trends in this direction

Topics: informed source separation, source separation, audio processing, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing
Publisher: HAL CCSD
Year: 2013
DOI identifier: 10.1109/WIAMIS.2013.6616139
OAI identifier: oai:HAL:hal-00958661v1
Provided by: Hal-Diderot

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