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...

Celý popis

Uloženo v:

Podrobná bibliografie
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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!