1,724 research outputs found
Probabilistic Modeling Paradigms for Audio Source Separation
This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most 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
Statistical single channel source separation
PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields
in signal processing and has various significant applications. Unlike conventional
SCSS methods which were based on linear instantaneous model, this research sets out
to investigate the separation of single channel in two types of mixture which is
nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear
SCSS in instantaneous mixture, this research proposes a novel solution based on a
two-stage process that consists of a Gaussianization transform which efficiently
compensates for the nonlinear distortion follow by a maximum likelihood estimator to
perform source separation. For linear SCSS in convolutive mixture, this research
proposes new methods based on nonnegative matrix factorization which decomposes a
mixture into two-dimensional convolution factor matrices that represent the spectral
basis and temporal code. The proposed factorization considers the convolutive mixing
in the decomposition by introducing frequency constrained parameters in the model.
The method aims to separate the mixture into its constituent spectral-temporal source
components while alleviating the effect of convolutive mixing. In addition, family of
Itakura-Saito divergence has been developed as a cost function which brings the
beneficial property of scale-invariant. Two new statistical techniques are proposed,
namely, Expectation-Maximisation (EM) based algorithm framework which
maximizes the log-likelihood of a mixed signals, and the maximum a posteriori
approach which maximises the joint probability of a mixed signal using multiplicative
update rules. To further improve this research work, a novel method that incorporates
adaptive sparseness into the solution has been proposed to resolve the ambiguity and
hence, improve the algorithm performance. The theoretical foundation of the proposed
solutions has been rigorously developed and discussed in details. Results have
concretely shown the effectiveness of all the proposed algorithms presented in this
thesis in separating the mixed signals in single channel and have outperformed others
available methods.Universiti Teknikal Malaysia Melaka(UTeM),
Ministry of Higher Education of Malaysi
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