3 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…
In the domain of sound event detection (SED), Convolutional Recurrent Neural Network (CRNN) has become the most successful architecture, which adopts Recurrent Neural Network (RNN) to model temporal dependencies from the output of Convolutional Neural Network (CNN). However, CRNN does not fully use…
Self-attention mechanism has been widely employed in semi-supervised sound event detection (SS-SED). In self-attention, since dependencies between pairwise features at all moments are captured, the irrelevant features of different classes of sounds and background sounds at other moments are inevitab…