  Sound source localisation is used in many consumer electronics devices, to
help isolate audio from individual speakers and to reject noise. Localization
is frequently accomplished by "beamforming" algorithms, which combine
microphone audio streams to improve received signal power from particular
incident source directions. Beamforming algorithms generally use knowledge of
the frequency components of the audio source, along with the known microphone
array geometry, to analytically phase-shift microphone streams before combining
them. A dense set of band-pass filters is often used to obtain known-frequency
"narrowband" components from wide-band audio streams. These approaches achieve
high accuracy, but state of the art narrowband beamforming algorithms are
computationally demanding, and are therefore difficult to integrate into
low-power IoT devices. We demonstrate a novel method for sound source
localisation in arbitrary microphone arrays, designed for efficient
implementation in ultra-low-power spiking neural networks (SNNs). We use a
novel short-time Hilbert transform (STHT) to remove the need for demanding
band-pass filtering of audio, and introduce a new accompanying method for audio
encoding with spiking events. Our beamforming and localisation approach
achieves state-of-the-art accuracy for SNN methods, and comparable with
traditional non-SNN super-resolution approaches. We deploy our method to
low-power SNN audio inference hardware, and achieve much lower power
consumption compared with super-resolution methods. We demonstrate that signal
processing approaches can be co-designed with spiking neural network
implementations to achieve high levels of power efficiency. Our new
Hilbert-transform-based method for beamforming promises to also improve the
efficiency of traditional DSP-based signal processing.
