Robust hierarchical and sparse representation of natural sounds in high-dimensional space

Abstract

Based on general findings from the field of neuroscience and their algorithmic implementations using signal processing, information theory and machine learning techniques, this paper highlights the advantages of modelling a signal in a sparse and high-dimensional feature space. The emphasis is put on the hierarchical organisation, very high dimensionality and sparseness aspects of auditory information, that allow unsupervised learning of meaningful auditory objects from simple linear projections. When the dictionaries are learned using independent component analysis (ICA), it is shown that specific spectro-temporal modulation patterns are learned to optimally represent speech, noise and tonal components. In a noisy isolated-word speech recognition task, sparse and high-dimensional features have shown greater robustness to noise compared to a standard system based on a dense low-dimensional feature space. This brings new ways of thinking in the field of recognition and classification of acoustic signals. © 2013 Springer-Verlag Berlin Heidelberg.