Frame-Level Embedding Learning for Few-shot Bioacoustic Event Detection

We propose an effective frame-level embedding learning framework for few-shot bioacoustic event detection (FSBED). First, the duration of different animal calls varies greatly, so we innovatively propose a frame-level embedding learning scheme, which can obtain adaptive event receptive fields with more accurate frame-level units. Next, we develop a transfer learning-based approach to deal with the mismatch between training and testing data. Finally, we use the idea of semi-supervised learning to solve the problem of too little labeled data in few-shot learning. By incorporating these several sets of techniques, our overall system ranked first place in the FSBED task of Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge 2022.