A review on speech separation using NMF and its extensions

Speech separation aims to estimate the target signals produced by individual speech sources from a mixture signal. In this paper, we especially review on data-driven separation methods, where algorithms will be enhanced to produce better dictionary learning which considers the geometric of input dat...

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Bibliographic Details
Published in2015 International Conference on Orange Technologies (ICOT) pp. 26 - 29
Main Authors Tuan Pham, Yuan-Shan Lee, Yu-An Chen, Jia-Ching Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Bayes methods
bilevel optimization
Dictionaries
graph regularization
group lasso
non-negative matrix factorization
single channel source separation
Source separation
Sparse matrices
Speech
Training
Transforms
Online AccessGet full text
DOI10.1109/ICOT.2015.7498486

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Summary:Speech separation aims to estimate the target signals produced by individual speech sources from a mixture signal. In this paper, we especially review on data-driven separation methods, where algorithms will be enhanced to produce better dictionary learning which considers the geometric of input data and efficiently performs separation mixture. We review the existing algorithms using non-negative matrix factorization, sparse coding, mixture local dictionary, group lasso, and graph regularization to produce knowledge bases. We also review the extension of NMF by incorporating two state-of-art techniques i.e. bilevel optimization and deep neural network.
DOI:10.1109/ICOT.2015.7498486