用脉冲神经网络实现低功耗语音降噪，提升人工耳蜗听觉体验。
Low-Power End-to-End Cochlear Implant Speech Denoising with Spiking Neural Networks
- 基于Deep ACE架构的脉冲神经网络，融合语音增强与耳蜗编码
- 在保持语音可懂度和信噪比提升的前提下，能耗降低六倍以上
- 适合部署于功耗敏感的人工耳蜗设备，推动智能助听硬件发展
人工耳蜗（CI）可帮助重度至极重度听力损失者恢复听力，但在嘈杂环境中理解语音仍具挑战。深度神经网络（DNN）虽能有效提升语音质量，但其高能耗不适用于低功耗的耳蜗处理器。脉冲神经网络（SNN）则在性能相近的情况下显著降低能耗。本文提出一种受Deep ACE架构启发的新型SNN，同时完成语音增强与耳蜗编码任务。该模型在语音可懂度（VSTOI）和信噪比改善（SNRi）指标上达到与Deep ACE相当的水平，同时实现超过六倍的能耗降低，显著提升了低功耗场景下的实用性。
原文摘要 · Abstract (English)
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.
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