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<p>Acoustic event detection faces great challenges in recognizing event categories and locating event temporal boundaries due to the scarcity of high-quality labeled data and the limitation of label granularity. To conquer these constraints, a novel data augmentation method, LTSRR, and a two-stage method, PCL-AED, are proposed in this study. LTSRR is developed to generate training samples with rich spectral characteristics, which significantly expands the diversity of the training data. Subsequently, PCL-AED puts up a segment-level progressive event classification strategy for alleviating the impact of uneven sample distribution on audio tagging, and a temporal contrast learning mechanism is designed to enhance the sensitivity of the model to event boundaries for acoustic event detection. Experiments on DCASE2018 and DCASE2021 Task 4 datasets show that the LTSRR and PCL-AED proposed in this paper exhibit competitive performance, providing a new stroke to the semi-supervised acoustic event detection task.</p>

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PCL-AED: progressive acoustic event detection based on contrastive learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月23日

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  • Yuanjian Chen,
  • Jinjie Huang

摘要

Acoustic event detection faces great challenges in recognizing event categories and locating event temporal boundaries due to the scarcity of high-quality labeled data and the limitation of label granularity. To conquer these constraints, a novel data augmentation method, LTSRR, and a two-stage method, PCL-AED, are proposed in this study. LTSRR is developed to generate training samples with rich spectral characteristics, which significantly expands the diversity of the training data. Subsequently, PCL-AED puts up a segment-level progressive event classification strategy for alleviating the impact of uneven sample distribution on audio tagging, and a temporal contrast learning mechanism is designed to enhance the sensitivity of the model to event boundaries for acoustic event detection. Experiments on DCASE2018 and DCASE2021 Task 4 datasets show that the LTSRR and PCL-AED proposed in this paper exhibit competitive performance, providing a new stroke to the semi-supervised acoustic event detection task.