通过精准建模事件起止点，提升声音事件检测精度与效率
Sound Event Detection with Boundary-Aware Optimization and Inference
- 显式建模事件起始与终止时刻，优化时序定位
- 在AudioSet强标注集上达成新最佳性能，无需后处理调参
- 适合需要高精度时序定位的音频分析任务
时序检测问题广泛存在于时间序列估计、行为识别和声音事件检测（SED）等领域。本文提出一种新型时序事件建模方法，通过显式建模事件起始与终止，并引入边界感知的优化与推理策略，显著提升时序事件检测能力。所提方法包含新的时序建模模块——循环事件检测（RED）和事件提议网络（EPN），结合定制损失函数，实现更高效、更精确的时序事件检测。我们在AudioSet的时序强标注子集上评估该方法，实验结果表明，该方法不仅优于传统帧级SED模型搭配先进后处理的效果，还消除了对后处理超参数调优的需求，并在所有AudioSet Strong类别上达到新的最佳性能。
原文摘要 · Abstract (English)
Temporal detection problems appear in many fields including time-series estimation, activity recognition and sound event detection (SED). In this work, we propose a new approach to temporal event modeling by explicitly modeling event onsets and offsets, and by introducing boundary-aware optimization and inference strategies that substantially enhance temporal event detection. The presented methodology incorporates new temporal modeling layers - Recurrent Event Detection (RED) and Event Proposal Network (EPN) - which, together with tailored loss functions, enable more effective and precise temporal event detection. We evaluate the proposed method in the SED domain using a subset of the temporally-strongly annotated portion of AudioSet. Experimental results show that our approach not only outperforms traditional frame-wise SED models with state-of-the-art post-processing, but also removes the need for post-processing hyperparameter tuning, and scales to achieve new state-of-the-art performance across all AudioSet Strong classes.
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