  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.
