用脉冲神经网络设计低功耗音频事件触发器，显著降低计算开销。
A Neuromorphic Trigger for Efficient Audio Event Detection
- 用脉冲神经网络构建轻量级触发器，仅处理关键音频片段。
- 在ASD任务中达到0.97的F1分数，在SED任务中减少42.6倍运算量。
- 适合实时、低功耗设备上的音频事件检测应用。
连续音频流的高效处理仍是实时和资源受限系统的关键挑战。本文提出一种基于脉冲神经网络（SNN）的类脑触发器，用于音频事件检测，可选择性地将输入传递给下游模型。该触发器作为灵活且低成本的前端，识别重要音频段，使更复杂的模型仅处理这些片段以完成分类等任务。触发器采用轻量级全连接SNN结构，并结合闭-开滤波器进行后处理。在两个代表性任务上进行评估：异常声音检测（ASD）和声音事件检测（SED）。在类无关的URBAN-SED数据集上，一秒钟片段级别的F1得分为0.97，表明其在识别相关音频区域方面具有高可靠性。在DCASE 2017 Challenge Task 2数据集上，与Dang分类器结合使用时，计算量降低42.6倍，事件误差率下限从0.41降至0.25。结果表明，类脑触发器可作为实时、节能的前端过滤器，大幅降低计算成本。
原文摘要 · Abstract (English)
Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed neuromorphic trigger acts as a flexible low-cost front-end, identifying salient audio segments and enabling these to be processed by a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN using a close-open filter for postprocessing, and is evaluated on two representative tasks: Anomalous Sound Detection (ASD) and Sound Event Detection (SED). For ASD, the trigger achieves a one-second segment-based F1 score of 0.97 on a class-agnostic form of the URBAN-SED dataset, demonstrating high reliability in identifying relevant audio regions. For SED, the trigger is combined with the Dang classifier on the DCASE 2017 Challenge Task 2 dataset, showing a potential $42.6\times$ reduction in FLOPs while reducing the lower bound of the event-based error rate from 0.41 to 0.25. These results highlight the potential of neuromorphic triggers as real-time, energy-efficient front-end filters, enabling substantial reductions in computational cost.
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