Abstract:
This paper addresses the design of wavelets adapted to the processed signals and the considered application. Our approach consists of parameterizing a mother wavelet, and...Show MoreMetadata
Abstract:
This paper addresses the design of wavelets adapted to the processed signals and the considered application. Our approach consists of parameterizing a mother wavelet, and defining a quality criterion for the optimization of the parameters, according to the context. The first parameterization, leading to orthogonal wavelets, considers the coefficients of the scaling filter as the parameters. A second parameterization, leading to semiorthogonal wavelets, consists of convolving an existing wavelet (or scaling function) by a given sequence. In this paper, we explore these two methods and apply them to the supervised classification of signals made of waveform trains.
Published in: Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
Date of Conference: 23-23 March 2005
Date Added to IEEE Xplore: 09 May 2005
Print ISBN:0-7803-8874-7