Probabilistic modeling paradigms for audio source separation

Abstract : 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, we 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. We show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. We compare the merits of either paradigm and report objective performance figures. We conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.
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Chapitre d'ouvrage
W. Wang. Machine Audition: Principles, Algorithms and Systems, IGI Global, pp.162--185, 2010, 〈10.4018/978-1-61520-919-4.ch007〉
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Emmanuel Vincent, Maria Jafari, Samer Abdallah, Mark Plumbley, Mike Davies. Probabilistic modeling paradigms for audio source separation. W. Wang. Machine Audition: Principles, Algorithms and Systems, IGI Global, pp.162--185, 2010, 〈10.4018/978-1-61520-919-4.ch007〉. 〈inria-00544016〉

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