# Low-power SNN-based audio source localisation using a Hilbert Transform spike encoding scheme

arXiv: https://arxiv.org/abs/2402.11748 · HTML: https://mlsanity.com/paper/2402.11748/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 3 of 3)

- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Scale-covariant spiking wavelets](https://mlsanity.com/paper/2602.02020/) (2026)
- [Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition](https://mlsanity.com/paper/2608.30792/) (2026)

## Abstract (arXiv)

Sound source localisation is used in many consumer devices, to isolate audio from individual speakers and reject noise. Localization is frequently accomplished by ``beamforming'', which combines phase-shifted audio streams to increase power from chosen source directions, under a known microphone array geometry. Dense band-pass filters are often needed to obtain narrowband signal components from wideband audio. These approaches achieve high accuracy, but narrowband beamforming is computationally demanding, and not ideal for low-power IoT devices. We demonstrate a novel method for sound source localisation on arbitrary microphone arrays, designed for efficient implementation in ultra-low-power spiking neural networks (SNNs). We use a Hilbert transform to avoid dense band-pass filters, and introduce a new event-based encoding method that captures the phase of the complex analytic signal. Our approach achieves state-of-the-art accuracy for SNN methods, comparable with traditional non-SNN super-resolution beamforming. We deploy our method to low-power SNN inference hardware, with much lower power consumption than super-resolution methods. We demonstrate that signal processing approaches co-designed with spiking neural network implementations can achieve much improved power efficiency. Our new Hilbert-transform-based method for beamforming can also improve the efficiency of traditional DSP-based signal processing.