Underdetermined Convolutive Source Separation Using GEM-MU With Variational Approximated Optimum Model Order NMF2D

An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As t...

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Vydáno v:IEEE/ACM transactions on audio, speech, and language processing Ročník 25; číslo 1; s. 35 - 49
Hlavní autoři: Al-Tmeme, Ahmed, Woo, Wai Lok, Dlay, Satnam Singh, Gao, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation models
Algorithms
Approximation
Approximation algorithms
Audio source separation
Bayesian analysis
generalized expectation-maximization algorithm
IEEE transactions
Machine learning
Machine learning algorithms
Mathematical models
nonnegative matrix factorization
Optimization
optimum model order selection
Parameter estimation
Source separation
Speech
Time-frequency analysis
Two dimensional models
variational Bayesian
ISSN:2329-9290, 2329-9304
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Shrnutí:An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution (NMF2D) with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As the number of parameters in the NMF2D grows exponentially the number of frequency basis increases linearly, the issues of model-order fitness, initialization, and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2016.2620600