ArticleFrontiers in neuroscience2019
An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 42 citations in OpenAlex.
- Exercise-induced neuropeptidergic and neurochemical neuroadaptation in stress regulation and emotional disorders.Acta neurologica Belgica · 2026Review
- Spiking neural networks for EEG signal analysis using wavelet transform.Frontiers in neuroscience · 2025Article
- Snn and sound: a comprehensive review of spiking neural networks in sound.Biomedical engineering letters · 2024Review
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- Spiking neural network with working memory can integrate and rectify spatiotemporal features.Frontiers in neuroscience · 2023Article
- Lead federated neuromorphic learning for wireless edge artificial intelligence.Nature communications · 2022Article
- Voltage slope guided learning in spiking neural networks.Frontiers in neuroscience · 2022Article
- ALSA: Associative Learning Based Supervised Learning Algorithm for SNN.Frontiers in neuroscience · 2022Article
- Spatio-Temporal Sequential Memory Model With Mini-Column Neural Network.Frontiers in neuroscience · 2021Article
- Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
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 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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