The Spatial Effect of the Pinna for Neuromorphic Speech Denoising

Humans are capable of complex communication in the form of speech, which fundamentally relies on the ability to parse and distinguish sounds in noisy environments. Advances in computing hardware have made artificial neural networks ideal for imitating human speech recognition. Such models have achieved near human-like performance in isolating speech from noisy audio, at the cost of enormous model sizes and power consumption at orders of magnitude greater than the brain. Spiking neural networks have been proposed as an alternative, attaining model efficiency by prioritizing biological fidelity. Inspired by the biological pinna, our model encodes noisy speech input with spatial cues to aid in speech denoising. We show that denoising performance improves when a spiking neural network consumes audio encoded with spatial cues from pinna transforms. Our fixed networks achieve up to +0.15 dB improvement and up to +1.04 dB for our generalized pinna networks against comparable models. We present a neuroscience-inspired, shallow, spiking neural network architecture with just 525K weights that may be used as a starting model to explain neuronal observations.