DocumentCode :
2794790
Title :
Online Bayesian learning for dynamic source separation
Author :
Hsieh, Hsin-Lung ; Chien, Jen-Tzung
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fYear :
2010
fDate :
14-19 March 2010
Firstpage :
1950
Lastpage :
1953
Abstract :
Independent component analysis (ICA) is a popular approach for blind source separation when the source signals are stationary with fixed distribution functions. However, the source signals are nonstationary in real-world applications, e.g. the source signals may abruptly appear or disappear or even the number of sources may be changed by time. This study presents a nonstationary ICA for dynamic source separation through an online Bayesian learning procedure. In this procedure, we capture the evolved statistics of independent sources from the online observed signals. The mixing matrix is incrementally compensated at each frame and continuously propagated to the next frame. A variational Bayesian algorithm is established to estimate the nonstationary ICA parameters. The number of latent sources is automatically determined at each frame. In the experiments, the proposed method effectively recovers the source speech signals from different speakers in presence of different mixing scenarios.
Keywords :
belief networks; independent component analysis; source separation; speech processing; unsupervised learning; Independent component analysis; blind source separation; distribution function; dynamic source separation; matrix; online bayesian learning; online observed signal; speech signal; variational Bayesian algorithm; Bayesian methods; Blind source separation; Computational efficiency; Computer science; Distribution functions; Hidden Markov models; Independent component analysis; Signal processing; Signal processing algorithms; Source separation; Bayes procedures; Signal processing; separation; variational methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
ISSN :
1520-6149
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2010.5495299
Filename :
5495299
Link To Document :
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