Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme.

ABSTRACT: 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 introduce a 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 an

SUBMITTER: Haghighatshoar S 

PROVIDER: S-EPMC11814312 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature