FEW-Shot Continual Learning with Weight Alignment and Positive Enhancement for Bioacoustic Event Detection

In this paper, we propose a new continual learning framework for few-shot bioacoustic event detection (BED). First, we modify the recently proposed dynamic few-shot learning (DFSL) and generalize it to the BED task. Then, we introduce a weight alignment loss to enhance the weight generator of modified DFSL for detecting novel events. Furthermore, to augment the few positive samples of each target bioacoustic event, a positive enhancement approach is proposed to select high-confidence pseudo positives using the cumulative distribution of initial detection posterior probabilities. All experiments are performed on DCASE 2022 Task5 Challenge dataset, results show that the proposed methods significantly outperform the prototypical network (PN) baseline, it brings the overall F-measure of validation set from 52.2% to 55.9%. Moreover, the proposed framework shows great complementarity with the conventional PN, the F-measure is improved to 60.6% after applying a simple score fusion.