2020
DOI: 10.3389/fnins.2019.01420
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An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks

Abstract: 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 t… Show more

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Cited by 30 publications
(18 citation statements)
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“…While the van Rossum distance solves some challenges in the streaming data setting in which labels can only be assigned coarsely in time, a number of important issues remain open. Here, future work, possibly building on aggregate label losses [101], [107] or connectionist temporal classification (CTC) losses [43], provided that these approaches can be made online-capable, may offer possible solutions.…”
Section: ) Illustration Of Different Computational Graphs Used For Comentioning
confidence: 99%
“…While the van Rossum distance solves some challenges in the streaming data setting in which labels can only be assigned coarsely in time, a number of important issues remain open. Here, future work, possibly building on aggregate label losses [101], [107] or connectionist temporal classification (CTC) losses [43], provided that these approaches can be made online-capable, may offer possible solutions.…”
Section: ) Illustration Of Different Computational Graphs Used For Comentioning
confidence: 99%
“…Each word in the TIDIGITS is repeated 450 times, where 2464 (224 × 11) and 2486 (226 × 11) isolated However, in the study of neuromorphic computing, there are other speech datasets such as the N-TIDIGITS [87] that was designed for SNN benchmarking, but it is relatively small compared with the TIDIGITS. Thus, the TIDIGITS dataset is employed as one of the main benchmarks for ASR research [88]- [90]. The RWCP dataset [86] is a non-speech natural sound dataset recorded in an anechoic room composed of sound samples with rich and diverse frequency components.…”
Section: Audio Signals Recognitionmentioning
confidence: 99%
“…However, the scale of the ASR tasks explored in these studies is relatively small comparing to modern ASR benchmarks due to the limited availability of event-based ASR corpora. Pan et al (Pan et al, 2019) recently proposed an efficient and perceptually motivated auditory neural encoding scheme to encode the large-scale ASR corpora collected by microphone sensors into spiking events. With this encoding scheme, approximately 50% spiking events can be reduced with negligible interference to the perceptual quality of inputs audio signals.…”
Section: Future Directionsmentioning
confidence: 99%