通过层级标签传播修复音频数据集标注错误，提升模型性能。
Hierarchical Label Propagation: A Model-Size-Dependent Performance Booster for AudioSet Tagging
- 基于语义层级向上传播标签，修正标注不一致问题。
- 每段音频正标签数从1.98升至2.39，影响109个类别。
- 小模型提升更明显，适合作为音频分类预训练增强策略。
AudioSet是音频标记任务中使用最广泛、规模最大的数据集，包含约200万段音频样本，人工标注了527个事件类别，这些类别按语义层次结构组织。然而，标注存在不一致，尤其在语义层级中应为正类的类别常被误标为负类。为此，我们提出层级标签传播（HLP），将标签沿层次结构向上传播，使每段音频的平均正标签数从1.98增至2.39，影响527个类别中的109个。实验表明，HLP在多种模型架构（如PANN的CNN6和ConvNeXT、PaSST）上均带来性能提升，且小模型受益更显著。在另一个常用数据集FSD50K上，使用HLP训练的模型始终优于未使用HLP的模型。源代码将公开于GitHub。
原文摘要 · Abstract (English)
AudioSet is one of the most used and largest datasets in audio tagging, containing about 2 million audio samples that are manually labeled with 527 event categories organized into an ontology. However, the annotations contain inconsistencies, particularly where categories that should be labeled as positive according to the ontology are frequently mislabeled as negative. To address this issue, we apply Hierarchical Label Propagation (HLP), which propagates labels up the ontology hierarchy, resulting in a mean increase in positive labels per audio clip from 1.98 to 2.39 and affecting 109 out of the 527 classes. Our results demonstrate that HLP provides performance benefits across various model architectures, including convolutional neural networks (PANN's CNN6 and ConvNeXT) and transformers (PaSST), with smaller models showing more improvements. Finally, on FSD50K, another widely used dataset, models trained on AudioSet with HLP consistently outperformed those trained without HLP. Our source code will be made available on GitHub.
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