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

Publication Date
  • Rc0321 Neuroscience. Biological Psychiatry. Neuropsychiatry
  • Rf Otorhinolaryngology
  • Biology
  • Computer Science
  • Mathematics
  • Musicology
  • Physics


Microsoft Word - fractional noise reduction4[1].doc The ‘Neural Space’: a Physiologically Inspired Noise Reduction Strategy Based on Fractional Derivatives Jinqiu Sang1, Hongmei Hu1, Ian Winter2, Matthew C. M. Wright1 and Stefan Bleeck1 1Institute of Sound and Vibration Research, University of Southampton, SO17 1BJ, UK 2Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3EG, UK 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. Keywords-neural coding; sparse coding; fractional derivation; bio-inspired I. INTRODUCTION Speech enhancement and noise reduction strategies have been developed on the basis of various mathematical principles. Common strategies that are used today in acoustical signal processing are spectral subtraction, Wiener filtering and subspace algorithms [1-6]. These methods, although based on fundamentally different strategies share commonalities: first, they are based on the signal amplitude and are therefore sensitive to the signal energy, and second, although they can improve the speech quality, they generally do not improve the speech intelligibility. A system to improve speech intelligibility in noisy situation, possibl

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