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

The 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,...

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Veröffentlicht in:Frontiers in neuroscience Jg. 13; S. 1420
Hauptverfasser: Pan, Zihan, Chua, Yansong, Wu, Jibin, Zhang, Malu, Li, Haizhou, Ambikairajah, Eliathamby
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 22.01.2020
Frontiers Media S.A
Schlagworte:
auditory masking effects
Auditory nerve
auditory perception
Auditory system
Classification
Cochlea
Cognitive ability
Datasets
Deep learning
Firing pattern
Hair cells
Hearing
Information processing
Neural coding
neural encoding
Neural networks
Neuroscience
Speech
Speech recognition
spike database
spiking neural network
Voice recognition
Workloads
neural encoding
auditory masking effects
auditory perception
spiking neural network
spike database
ISSN:1662-453X, 1662-4548, 1662-453X
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Zusammenfassung:The 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 audio 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 sound classification and 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.
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This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience
Edited by: Huajin Tang, Zhejiang University, China
Reviewed by: Sadique Sheik, aiCTX AG, Switzerland; Rui Yan, Sichuan University, China
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2019.01420