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An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks.

Zihan Pan1, Yansong Chua2, Jibin Wu1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.

Frontiers in Neuroscience
|February 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a Biologically plausible Auditory Encoding (BAE) scheme for spiking neural networks (SNNs) that mimics human auditory perception. The BAE scheme improves audio processing and enables new speech recognition benchmarks.

Keywords:
auditory masking effectsauditory perceptionneural encodingspike databasespiking neural network

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) require effective auditory front-ends for processing temporal stimuli like speech.
  • Current auditory front-ends often overlook crucial psychoacoustic and physiological findings from human auditory perception.
  • Efficient encoding of audio into spike patterns is vital for subsequent processing in SNNs.

Purpose of the Study:

  • To propose a novel neural encoding and decoding scheme optimized for audio processing in SNNs.
  • To develop a biologically plausible auditory encoding (BAE) scheme that emulates human auditory system functions.
  • To evaluate the BAE scheme's perceptual quality and performance in sound classification and speech recognition tasks.

Main Methods:

  • Developed the Biologically plausible Auditory Encoding (BAE) scheme, incorporating cochlear filtering, inner hair cell function, auditory masking, and auditory nerve spike encoding.
  • Evaluated BAE's perceptual quality using Perceptual Evaluation of Speech Quality (PESQ).
  • Assessed BAE's performance in sound classification and speech recognition experiments, and released two spike-based speech datasets (Spike-TIDIGITS, Spike-TIMIT).

Main Results:

  • The BAE scheme effectively emulates key perceptual components of the human auditory system.
  • BAE demonstrated promising performance in perceptual quality assessments and downstream tasks like sound classification and speech recognition.
  • The release of Spike-TIDIGITS and Spike-TIMIT datasets provides valuable resources for SNN research.

Conclusions:

  • The proposed BAE scheme offers a more biologically realistic and effective approach to auditory processing in SNNs.
  • BAE has the potential to significantly advance the capabilities of SNNs in handling complex auditory information.
  • The new datasets will facilitate benchmarking and further development of SNNs for auditory tasks.