基于对比损失的多音事件检测帧级特征解耦
Contrastive Loss Based Frame-wise Feature disentanglement for Polyphonic Sound Event Detection
AI总结：
针对重叠声音事件检测中帧级特征纠缠导致区分能力下降的问题，本文提出类别特定解耦特征学习框架，并设计帧级对比损失及半监督扩展，利用未标注数据提升多音事件检测性能。
摘要：
现实环境中重叠声音事件无处不在，但现有的端到端声音事件检测（SED）方法仍难以有效检测它们。一个关键原因是这些方法使用共享且纠缠的帧级特征来表示重叠事件，这降低了特征的区分能力。为了解决该问题，我们提出了一种解耦特征学习框架，以学习类别特定的表示。具体而言，我们采用不同的投影器为每个类别学习帧级特征。为确保这些特征不包含其他类别的信息，我们最大化同一类别内帧级特征之间的公共信息，并提出一种帧级对比损失。此外，考虑到所提方法使用的标注数据有限，我们提出了一种半监督帧级对比损失，可利用大量未标注数据实现特征解耦。实验结果证明了我们方法的有效性。
英文摘要：
Overlapping sound events are ubiquitous in real-world environments, but existing end-to-end sound event detection (SED) methods still struggle to detect them effectively. A critical reason is that these methods represent overlapping events using shared and entangled frame-wise features, which degrades the feature discrimination. To solve the problem, we propose a disentangled feature learning framework to learn a category-specific representation. Specifically, we employ different projectors to learn the frame-wise features for each category. To ensure that these feature does not contain information of other categories, we maximize the common information between frame-wise features within the same category and propose a frame-wise contrastive loss. In addition, considering that the labeled data used by the proposed method is limited, we propose a semi-supervised frame-wise contrastive loss that can leverage large amounts of unlabeled data to achieve feature disentanglement. The experimental results demonstrate the effectiveness of our method.
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。
