Bio-inspired sparse representation of speech and audio using psychoacoustic adaptive matching pursuit

Abstract

Current paper devoted to the sparse audio and speech signal modelling via the matching pursuit (MP) algorithm. Redundant dictionary of the time-frequency functions is constructed through the frame-based psychoacoustic optimized wavelet packet (WP) transform. Anthropomorphic adaptation of the time-frequency plan allows minimizing perceptual redundancy of the signal modelling. Psychoacoustic information at MP stage for the best atom selection from the dictionary is used. It improves algorithm performance in terms of human hearing system and computational complexity. Described signal model can be applied in many audio and speech processing tasks such as source separation, watermarking, classification and so on. Presented research focused on the signal encoding. Universal audio/speech coding algorithm that is suitable for the input signals with different sound content is proposed.