DocumentCode :
3629822
Title :
On the effectiveness of the ICA-based signal representation in non-Gaussian noise
Author :
Xin Zou;Peter Jancovic;Munevver Kokuer
Author_Institution :
Electronic, Electrical & Computer Engineering, University of Birmingham, UK
fYear :
2008
Firstpage :
1
Lastpage :
4
Abstract :
This paper presents a mathematical analysis demonstrating the effectiveness of the signal representation based on Independent Component Analysis (ICA) in the case of non-Gaussian noise corruption. The analysis is based on calculating a mismatch between the distribution of the observed signal represented by a linear model and a reference distribution. The theoretical results lead to a novel ICA-based signal representation technique in which the ICA transformation matrix is estimated based on noise-corrupted signal but not based on clean signal as normal. Our theoretical findings are experimentally demonstrated by employing the proposed feature representation in a GMM-based speaker recognition system. Experimental results show that employment of the proposed ICA-based features can provide significant recognition accuracy improvements over using both the traditional ICA-based features and MFCC features.
Keywords :
"Signal representations","Independent component analysis","Signal analysis","Speaker recognition","Gaussian noise","Additive noise","Speech enhancement","Feature extraction","Data mining","Discrete cosine transforms"
Publisher :
ieee
Conference_Titel :
Signal Processing, 2008. ICSP 2008. 9th International Conference on
ISSN :
2164-5221
Print_ISBN :
978-1-4244-2178-7
Electronic_ISBN :
2164-523X
Type :
conf
DOI :
10.1109/ICOSP.2008.4697054
Filename :
4697054
Link To Document :
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