Non-parametric linear time-invariant system identification by discrete wavelet transforms
Digital Signal Processing
Bioinspired sparse spectro-temporal representation of speech for robust classification
Computer Speech and Language
A clustering based feature selection method in spectro-temporal domain for speech recognition
Engineering Applications of Artificial Intelligence
A difference engine for image reconstruction
EGGH'93 Proceedings of the Eighth Eurographics conference on Graphics Hardware
A scale-rate filter selection method in the spectro-temporal domain for phoneme classification
Computers and Electrical Engineering
International Journal of Speech Technology
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An analytically tractable framework is presented to describe mechanical and neural processing in the early stages of the auditory system. Algorithms are developed to assess the integrity of the acoustic spectrum at all processing stages. The algorithms employ wavelet representations, multiresolution processing, and the method of convex projections to construct a close replica of the input stimulus. Reconstructions using natural speech sounds demonstrate minimal loss of information along the auditory pathway. Close inspection of the final auditory patterns reveals spectral enhancements and noise suppression that have close perceptual correlates. The functional significance of the various auditory processing stages is discussed in light of the model, together with their potential applications in automatic speech recognition and low bit-rate data compression