Learning nonlinear overcomplete representations for efficient coding
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Sparse spectrotemporal coding of sounds
EURASIP Journal on Applied Signal Processing
A scale-rate filter selection method in the spectro-temporal domain for phoneme classification
Computers and Electrical Engineering
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The use of biologically inspired, feature extraction methods has improved the performance of artificial systems that try to emulate some aspect of human communication. Recent techniques, such as independent component analysis and sparse representations, have made it possible to undertake speech signal analysis using features similar to the ones found experimentally at the primary auditory cortex level. In this work, a new type of speech signal representation, based on the spectro-temporal receptive fields, is presented, and a problem of phoneme classification is tackled for the first time using this representation. The results obtained are compared, and found to greatly improve both an early auditory representation and the classical front-end based on Mel frequency cepstral coefficients.