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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Veröffentlicht in:	IEEE/ACM transactions on audio, speech, and language processing Jg. 25; H. 1; S. 35 - 49
Hauptverfasser:	Al-Tmeme, Ahmed, Woo, Wai Lok, Dlay, Satnam Singh, Gao, Bin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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