An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks

TL;DR

This paper introduces a biologically inspired auditory encoding and decoding scheme for spiking neural networks, improving speech processing by incorporating psychoacoustic principles and providing new datasets for benchmarking.

Contribution

The paper presents BAE, a novel auditory encoding scheme that emulates human auditory processing, and releases two spike-based speech datasets for research benchmarking.

Findings

BAE achieves high perceptual quality as measured by PESQ.

Speech recognition experiments show improved performance with BAE.

Two new spike-based speech datasets are introduced for benchmarking.

Abstract

Auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing. However, most of the auditory front-ends in current studies have not made use of recent findings in psychoacoustics and physiology concerning human listening. In this paper, we propose a neural encoding and decoding scheme that is optimized for speech processing. The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve. We evaluate the perceptual quality of the BAE scheme using PESQ; the performance of the BAE based on speech recognition experiments. Finally, we also built and published two spike-version of speech datasets: the Spike-TIDIGITS and the Spike-TIMIT, for researchers to use and benchmarking of future SNN research.