The ‘neural space’: A physiologically inspired noise reduction strategy based on fractional derivatives

Abstract

We present a novel noise reduction strategy that is inspired by the physiology of the auditory brainstem. Following the hypothesis that neurons code sound based on fractional derivatives we develop a model in which sound is transformed into a ‘neural space’. In this space sound is represented by various fractional derivatives of the envelopes in a 22 channel filter bank. We demonstrate that noise reduction schemes can work in the neural space and that the sound can be resynthesized. A supervised sparse coding strategy reduces noise while keeping the sound quality intact. This was confirmed in preliminary subjective listening tests. We conclude that new signal processing schemes, inspired by neuronal processing, offer exciting opportunities to implement novel noise reduction and speech enhancement algorithms.