Spiking-LEAF：面向脉冲神经网络的可学习听觉前端
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
AI总结：
提出专为脉冲神经网络设计的可学习听觉前端Spiking-LEAF，结合可学习滤波器组与新型双区室IHC-LIF神经元，有效捕捉语音多尺度时间动态并提升脉冲编码效率，在分类准确率、噪声鲁棒性和编码效率上均超越现有方法。
摘要：
受大脑启发的脉冲神经网络（SNNs）在时序信号处理方面展现出巨大潜力。然而，由于缺乏有效的听觉前端，其在语音处理中的性能仍然受限。为了解决这一局限性，我们引入了Spiking-LEAF，这是一种专为基于SNN的语音处理精心设计的可学习听觉前端。Spiking-LEAF将一个可学习的滤波器组与一种名为IHC-LIF的新型双区室脉冲神经元模型相结合。IHC-LIF神经元从内毛细胞（IHC）的结构中汲取灵感，并利用分离的树突和胞体区室来有效捕捉语音信号的多尺度时间动态。此外，IHC-LIF神经元结合了侧向反馈机制以及脉冲正则化损失，以增强脉冲编码效率。在关键词识别和说话人识别任务上，所提出的Spiking-LEAF在分类准确率、噪声鲁棒性和编码效率方面均优于最先进的脉冲听觉前端和传统的实值声学特征。
英文摘要：
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitation, we introduce Spiking-LEAF, a learnable auditory front-end meticulously designed for SNN-based speech processing. Spiking-LEAF combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure of inner hair cells (IHC) and they leverage segregated dendritic and somatic compartments to effectively capture multi-scale temporal dynamics of speech signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback mechanism along with spike regularization loss to enhance spike encoding efficiency. On keyword spotting and speaker identification tasks, the proposed Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional real-valued acoustic features in terms of classification accuracy, noise robustness, and encoding efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
