Auditory-inspired sparse representation of audio signals

Abstract

This article deals with the generation of auditory-inspired spectro-temporal fea-tures aimed at audio coding. To do so, we first generate sparse audio representa-tions we call spikegrams, using projections on gammatone/gammachirp kernels that generate neural spikes. Unlike Fourier-based representations, these repre-sentations are powerful at identifying auditory events, such as onsets, offsets, transients and harmonic structures. We show that the introduction of adap-tiveness in the selection of gammachirp kernels enhances the compression 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 qual-ity. We finally propose a method to extract frequent audio objects (patterns) in the aforementioned sparse representations. The extracted frequency-domain patterns (audio objects) help us address spikes (audio events) collectively rather than individually. When audio compression is needed, the different patterns are stored in a small codebook 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. 1