New Trends in Biologically-Inspired Audio Coding

Abstract

This book chapter deals with the generation of auditory-inspired spectro-temporal features aimed at audio coding. To do so, we first generate sparse audio representations we call spikegrams, using projections on gammatone or gammachirp kernels that generate neural spikes. Unlike Fourier-based representations, these representations are powerful at identify- ing auditory events, such as onsets, offsets, transients and harmonic structures. We show that the introduction of adaptiveness in the selection of gammachirp kernels enhances the com- pression rate compared to the case where the kernels are non-adaptive. We also integrate a masking model that helps reduce bitrate without loss of perceptible audio quality. We then quantize coding values using the genetic algorithm that is more optimal than uniform quan- tization for this framework. We finally propose a method to extract frequent auditory objects (patterns) in the aforementioned sparse representations. The extracted frequency-domain pat- terns (auditory objects) help us address spikes (auditory events) collectively rather than indi- vidually. When audio compression is needed, the different patterns are stored in a small code- book that can be used to efficiently encode audio materials in a lossless way. The approach is applied to different audio signals and results are discussed and compared. This work is a first step towards the design of a high-quality auditory-inspired “object-based" audio coder.