Sound event detection (SED) is an interesting but challenging task due to the scarcity of data
and diverse sound events in real life. This paper presents a multi-grained based attention …

The efficient capital markets hypothesis (EMH) posits that security prices incorporate all
available information in capital markets. Nevertheless, real stock markets often exhibit …

One of the main issues of polyphonic sound event detection (PSED) is the class imbalance
problem caused by the proportions of active and inactive frames. Since the target sounds …

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 …

Sound event detection (SED) is a core technology in smart home projects that rely on
detected sound events to trigger specific actions. SED systems face two major challenges …

In Weakly-supervised Sound Event Detection (WSED), the ground truth of training data
contains the presence or absence of each sound event only at the clip-level (ie, no frame …

Semi-supervised sound event detection (SSED) task requires recognizing the categories of
events and marking each event's onset and offset times in a mixed audio recording using a …

The training of sound event detection (SED) models remains a challenge of insufficient
supervision due to limited frame-wise labeled data. Mainstream research on this problem …

In the domain of sound event detection (SED), Convolutional Recurrent Neural Network
(CRNN) has become the most successful architecture, which adopts Recurrent Neural …

The Sound Event Detection task aims to determine the temporal locations of acoustic events
in audio clips. In recent years, the relevance of this field is rising due to the introduction of …