ICA-Based speech features in the frequency domain

Authors:
Włodzimierz Kasprzak;Adam F. Okazaki;Adam B. Kowalski
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland;Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland;Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 4
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Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

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Abstract

We apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compare our features with the Mel Frequency Cepstrum Coefficient (MFCC) features, widely used today for phoneme-based speech recognition.