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Probabilistic modeling paradigms for audio source separation

Vincent, E, Jafari, MG, Abdallah, SA, Plumbley, MD and Davies, ME (2010) Probabilistic modeling paradigms for audio source separation In: Machine Audition: Principles, Algorithms and Systems. IGI Global, pp. 162-185. ISBN 9781615209194

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Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.

Item Type: Book Section
Divisions : Surrey research (other units)
Authors :
Vincent, E
Jafari, MG
Abdallah, SA
Davies, ME
Editors :
Wang, W
Date : 1 December 2010
DOI : 10.4018/978-1-61520-919-4.ch007
Copyright Disclaimer : Copyright 2010 IGI Global
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:21
Last Modified : 23 Jan 2020 18:29

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