# Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

arXiv: https://arxiv.org/abs/1910.09993 · HTML: https://mlsanity.com/paper/1910.09993/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [Pruning of Deep Spiking Neural Networks through Gradient Rewiring](https://mlsanity.com/paper/2105.04916/) (2021)
- [Exploring Lottery Ticket Hypothesis in Spiking Neural Networks](https://mlsanity.com/paper/2207.01382/) (2022)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [Adaptive Sparse Structure Development with Pruning and Regeneration for Spiking Neural Networks](https://mlsanity.com/paper/2211.12219/) (2022)
- [In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms](https://mlsanity.com/paper/2309.02393/) (2023)
- [Fundamental Survey on Neuromorphic Based Audio Classification](https://mlsanity.com/paper/2502.15056/) (2025)
- [Analysis on Effects of Fault Elements in Memristive Neuromorphic Systems](https://mlsanity.com/paper/2312.04840/) (2023)
- [Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networks](https://mlsanity.com/paper/2408.02961/) (2024)
- [Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism](https://mlsanity.com/paper/2503.23767/) (2025)
- [Scale-covariant spiking wavelets](https://mlsanity.com/paper/2602.02020/) (2026)

## Abstract (arXiv)

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which enables Spiking Neural Networks (SNNs) to perform inference at very low energy consumption. Spiking networks are characterized by their ability to process information efficiently, in a sparse cascade of binary events in time called spikes. However, a big performance gap separates artificial from spiking networks, mostly due to a lack of powerful SNN training algorithms. To overcome this problem we exploit an SNN model that can be recast into an RNN-like model and trained with known deep learning techniques. We describe an SNN training procedure that achieves low spiking activity and pruning algorithms to remove 85% of the network connections with no performance loss. The model achieves state-of-the-art performance with a fraction of power consumption comparing to other methods.