In this paper, a multi-stage wavelet approach to a problem of signal discrimination is presented. This method uses wavelet expansions of signals. It allows decomposing a highly dimensional classification problem into a few ones of smaller dimensions. A general scheme and implementation to discrimination of ECG signals is given. In order to adapt the general scheme to this particular problem a cross-validation technique is used.
This paper describes the methods of emphasizing spectral cues from the sound waveforms polluted with colored noise. The ambiguous spectral features of noisy speech data are emphasized on cepstral domain. Several methods for this purpose are defined. Some methods are based on spectral subtraction method. Others are based on the emphasis of spectral inclination by the nature of cepstral coefficients. These methods are applied to the pattern recognition of noisy speech. The following describes the examination of the performance of these parameters and the usage of those parameters is discussed.
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