Sound objects: a bio-inspired representation, hierarchical sparse to very large dimensions used in recognition
The emphasis is put on the hierarchical structure, independence and sparseness aspects of auditory signalrepresentations in high-dimensional spaces, so as to define the components of auditory objects. The conceptof an auditory object and its neural representation is introduced. An illustrative application then follows,consisting in the analysis of various auditory signals : speech, music and natural outdoor environments. Anew automatic speech recognition (ASR) system is then proposed and compared to a conventional statisticalsystem. The proposed system clearly shows that an object-based analysis introduces a great flexibility androbustness for the task of speech recognition. The integration of knowledge from neuroscience and acousticsignal processing brings new ways of thinking to the field of classification of acoustic signals.
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