  This paper focuses on few-shot Sound Event Detection (SED), which aims to
automatically recognize and classify sound events with limited samples.
However, prevailing methods methods in few-shot SED predominantly rely on
segment-level predictions, which often providing detailed, fine-grained
predictions, particularly for events of brief duration. Although frame-level
prediction strategies have been proposed to overcome these limitations, these
strategies commonly face difficulties with prediction truncation caused by
background noise. To alleviate this issue, we introduces an innovative
multitask frame-level SED framework. In addition, we introduce TimeFilterAug, a
linear timing mask for data augmentation, to increase the model's robustness
and adaptability to diverse acoustic environments. The proposed method achieves
a F-score of 63.8%, securing the 1st rank in the few-shot bioacoustic event
detection category of the Detection and Classification of Acoustic Scenes and
Events Challenge 2023.
