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

  • Jinqiu Sang*
  • , Hongmei Hu
  • , Ian M. Winter
  • , Matthew C.M. Wright
  • , Stefan Bleeck
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication11th International Symposium on Communications and Information Technologies, ISCIT 2011
Pages512-517
Number of pages6
DOIs
StatePublished - 2011
Externally publishedYes
Event11th International Symposium on Communications and Information Technologies, ISCIT 2011 - Hangzhou, China
Duration: 12 Oct 201114 Oct 2011

Publication series

Name11th International Symposium on Communications and Information Technologies, ISCIT 2011

Conference

Conference11th International Symposium on Communications and Information Technologies, ISCIT 2011
Country/TerritoryChina
CityHangzhou
Period12/10/1114/10/11

Keywords

  • bio-inspired
  • fractional derivation
  • neural coding
  • sparse coding

Fingerprint

Dive into the research topics of 'The 'neural space': A physiologically inspired noise reduction strategy based on fractional derivatives'. Together they form a unique fingerprint.

Cite this