4 accepted papers
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 degrad…
To solve the domain shift problem in speaker verification, one effective domain adaptation approach is to learn domain-invariant embeddings via aligning the source and target distributions in the embedding space. However, this approach could be problematic when the source and target domains are from…
This paper focuses on the problem of temporal dependency modeling in the CNN-based models for audio classification tasks. To capture audio temporal dependencies using CNNs, we take a different approach from the purely architecture-induced method and explicitly encode temporal dependencies into the C…
Respiratory sound classification refers to identifying adventitious sounds from given recordings automatically. Due to the difficulty of collection and the expensive manual annotation, there are only limited samples available, which impacts on learning better models. Meanwhile, a majority of these m…