# sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks

arXiv: https://arxiv.org/abs/2403.05772 · HTML: https://mlsanity.com/paper/2403.05772/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2024.

## Built on by (most cited first, 4 of 4)

- [Enhancing Audio-Visual Spiking Neural Networks through Semantic-Alignment and Cross-Modal Residual Learning](https://mlsanity.com/paper/2502.12488/) (2025)
- [ED-sKWS: Early-Decision Spiking Neural Networks for Rapid,and Energy-Efficient Keyword Spotting](https://mlsanity.com/paper/2406.12726/) (2024)
- [Fundamental Survey on Neuromorphic Based Audio Classification](https://mlsanity.com/paper/2502.15056/) (2025)
- [Scale-covariant spiking wavelets](https://mlsanity.com/paper/2602.02020/) (2026)

## Abstract (arXiv)

Speech applications are expected to be low-power and robust under noisy conditions. An effective Voice Activity Detection (VAD) front-end lowers the computational need. Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient. However, SNN-based VADs have yet to achieve noise robustness and often require large models for high performance. This paper introduces a novel SNN-based VAD model, referred to as sVAD, which features an auditory encoder with an SNN-based attention mechanism. Particularly, it provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms. The classifier utilizes Spiking Recurrent Neural Networks (sRNN) to exploit temporal speech information. Experimental results demonstrate that our sVAD achieves remarkable noise robustness and meanwhile maintains low power consumption and a small footprint, making it a promising solution for real-world VAD applications.