Efficient feature extraction and De-noising method for Chinese speech signals using GGM-based ICA

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3773 LNCS, Issue , 2005, Pages 925-932

  Bin, Yang ^a   Wei, Kong ^a  

^a SHANGHAI MARITIME UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; DATA REDUCTION; FEATURE EXTRACTION; GAUSSIAN NOISE (ELECTRONIC); NOISE ABATEMENT; VECTORS;

DE-NOISING METHOD; GENERALIZED GAUSSIAN MODEL (GGM); NON-GAUSSIAN STATISTICAL STRUCTURE; SPEECH SIGNALS;

SPEECH PROCESSING;

EID: 33745330441     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11578079_95     Document Type: Conference Paper

References (7)

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3. Anthony J Bell, Terrence J Sejnowski, Learning the Higher-order structure of a nature sound, Network: Computation in Neural System 7 (1996), 261-266
4. Gil-Jin Jang, Te-won Lee, Learning statistically efficient features for speaker recognition, Neurocomputing, 49 (2002): 329-348
5. Te-Won Lee, Michael S. Lewicki, The Generalized Gaussian Mixture Model Using ICA, in international workshop on Independent Component Analysis (ICA'OO), Helsinki, Finland, June 2000, pp: 239-244
6. A. Hyvärinen, Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Technical Report A51, Helsinki University of Technology, Laboratory of Computer and Information Science, 1998
7. Hyvärinen A., Hoyer P., Oja E., Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation, Advances in Neural Information Processing System 11 (NIPS'98), 1999