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

arXiv: https://arxiv.org/abs/1909.01302 · HTML: https://mlsanity.com/paper/1909.01302/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks](https://mlsanity.com/paper/2205.02115/) (2022)
- [Ternary Spike-based Neuromorphic Signal Processing System](https://mlsanity.com/paper/2407.05310/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting](https://mlsanity.com/paper/2406.13179/) (2024)
- [Spiking Music: Audio Compression with Event Based Auto-encoders](https://mlsanity.com/paper/2402.01571/) (2024)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization](https://mlsanity.com/paper/2602.02439/) (2026)
- [HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses](https://mlsanity.com/paper/2508.11644/) (2025)
- [Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition](https://mlsanity.com/paper/2608.30792/) (2026)

## Abstract (arXiv)

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.