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Proceedings Paper

Representation and classification of unvoiced sounds using adaptive wavelets
Author(s): Shubha L. Kadambe; Pramila Srinivasan; Brian A. Telfer; Harold H. Szu
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Paper Abstract

In this paper, we describe a method to represent and classify unvoiced sounds using the concept of super wavelets. A super wavelet is a linear combination of wavelets that itself can be treated as a wavelet. Since unvoiced sounds are high frequency and noise like, we use Daubechies' wavelet of order three to generate the super wavelet. The parameters of the wavelet for representation and classification of unvoiced sounds are generated using neural networks. Even though this paper addresses the problems of both signal representation and classification, emphasis is on classification problem, since it is natural to adaptively tune wavelets in conjunction with training the classifier in order to select the wavelet coefficients which contain the most information for discriminating between the classes. We demonstrate the applicability of this method for the representation and classification of unvoiced sounds with representative examples.

Paper Details

Date Published: 27 August 1993
PDF: 12 pages
Proc. SPIE 1961, Visual Information Processing II, (27 August 1993); doi: 10.1117/12.150961
Show Author Affiliations
Shubha L. Kadambe, AT&T Bell Labs. (United States)
Pramila Srinivasan, Rutgers Univ. (United States)
Brian A. Telfer, Naval Surface Warfare Ctr. (United States)
Harold H. Szu, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 1961:
Visual Information Processing II
Friedrich O. Huck; Richard D. Juday, Editor(s)

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