Online Non-Negative Convolutive Pattern Learning for Speech Signals

The unsupervised learning of spectro-temporal patterns within speech signals is of interest in a broad range of applications. Where patterns are non-negative and convolutive in nature, relevant learning algorithms include convolutive non-negative matrix factorization (CNMF) and its sparse alternativ...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 61; no. 1; pp. 44 - 56
Main Authors:	Dong Wang, Vipperla, R., Evans, N., Zheng, T. F.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Coding, codes Computing costs convolutive NMF Cost function Detection, estimation, filtering, equalization, prediction Distance learning Exact sciences and technology Image reconstruction Information, signal and communications theory Learning Non-negative matrix factorization On-line systems Online online pattern learning Signal and communications theory Signal processing Signal, noise sparse coding Speech Speech processing speech recognition Statistics Studies Telecommunications and information theory Training Training data E-learning Acoustic signal Non-negative matrix factorization Updating online pattern learning Factorization sparse coding Remote teaching Unsupervised learning Implementation Non negative matrix Vocal signal Coding Volume Batch process Speech recognition On line processing Signal processing Cost function Objective function Learning algorithm Speech processing convolutive NMF
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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