Problem
Research questions and friction points this paper is trying to address.
Cochlear Implants
Speech Denoising
Low-Power
Spiking Neural Networks
Energy Consumption
Low-Power End-to-End Cochlear Implant Speech Denoising with Spiking Neural Networks
🤖 AI Summary
为了解决助听器用户在噪音环境中理解语音的问题，本文提出了一种基于脉冲神经网络的低能耗语音降噪方法，实现了与现有深度学习模型相当的性能。
📝 Abstract
Cochlear implants (CI) restore hearing for individuals with severe to profound hearing loss. However, CI users often struggle to understand speech in noisy environments. Deep neural networks (DNN) have shown promise in enhancing speech for CI users, yet their high energy demands make them non-ideal for low-power CI processors. Spiking neural networks (SNN), on the other hand, offer comparable performance with significantly lower energy consumption. Hence, we propose a novel SNN inspired by the Deep ACE architecture that simultaneously performs speech enhancement and CI coding. Our model achieves competitive vocoded short-time objective intelligibility (VSTOI) and signal-to-noise ratio improvement (SNRi) scores compared to Deep ACE, while achieving more than a sixfold reduction in energy consumption.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Spiking Neural Networks
Low-Power
Speech Denoising
Cochlear Implants
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