Representation and classification of unvoiced sounds using adaptive wavelets
Shubha L. Kadambe, Pramila N. Srinivasan, Brian A. Telfer, Harold H Szu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.