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    Probabilistic Modeling Paradigms for Audio Source Separation 
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    Probabilistic Modeling Paradigms for Audio Source Separation

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    Accepted version (2.098Mb)
    Editors
    Wang, W
    Pagination
    162 - 185 (23)
    Publisher
    IGI Global
    Publisher URL
    http://www.igi-global.com/book/machine-audition-principles-algorithms-systems/40288
    ISBN-10
    1615209204
    ISBN-13
    9781615209194
    DOI
    10.4018/978-1-61520-919-4.ch007
    Journal
    Machine Audition: Principles, Algorithms and Systems
    Metadata
    Show full item record
    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, 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.
    Authors
    Vincent, E; Jafari, MG; Abdallah, SA; Plumbley, MD; Davies, ME
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/5265
    Collections
    • Electronic Engineering and Computer Science [2816]
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