When Audio Denoising Meets Spiking Neural Network

Xiang Hao, Chenxiang Ma, Qu Yang, Kay Chen Tan, Jibin Wu

Abstract: Audio denoising techniques are essential tools for enhancing audio quality. Spiking neural networks (SNNs) offer promising opportunities for audio denoising, as they leverage brain-inspired architectures and computational principles to efficiently process and analyze audio signals, enabling real-time denoising with improved accuracy and reduced computational overhead. This paper introduces Spiking-FullSubNet, a real-time audio denoising model based on SNN. Our proposed model incorporates a novel gated spiking neuron model (GSN) to effectively capture multi-scale temporal information, which is crucial for achieving high-fidelity audio denoising. Furthermore, we propose the integration of GSNs within an optimized FullSubNet neural architecture, enabling efficient processing of full-band and sub-band frequencies while significantly reducing computational overhead. Alongside the architectural advancements, we incorporate a metric discriminator-based loss function that selectively enhances the desired performance metrics without compromising others. Empirical evaluations show the superior performance of Spiking-FullSubNet, ranking it as the winner of Track 1 (Algorithmic) of the Intel Neuromorphic Deep Noise Suppression Challenge.

External IDs:dblp:conf/ieeecai/HaoMYTW24