An auditory cortex model for sound processing

TL;DR

This paper refines an auditory cortex model inspired by visual geometrical modeling, transforming degraded sounds into images for reconstruction using advanced mathematical techniques, demonstrating effective results on speech data.

Contribution

The study introduces an improved auditory cortex model that employs a geometrical approach and Wilson-Cowan equations for sound reconstruction from degraded signals.

Findings

Effective reconstruction of speech signals demonstrated

Algorithm shows good performance on speech library

Utilizes time-frequency domain and Heisenberg group techniques

Abstract

The reconstruction mechanisms built by the human auditory system during sound reconstruction are still a matter of debate. The purpose of this study is to refine the auditory cortex model introduced in [9], and inspired by the geometrical modelling of vision. The algorithm transforms the degraded sound in an 'image' in the time-frequency domain via a short-time Fourier transform. Such an image is then lifted in the Heisenberg group and it is reconstructed via a Wilson-Cowan differo-integral equation. Numerical experiments on a library of speech recordings are provided, showing the good reconstruction properties of the algorithm.