Segment-level Metric Learning for Few-shot Bioacoustic Event Detection
This work addresses the problem of detecting novel sounds with limited examples for bioacoustic monitoring, representing an incremental improvement over existing metric learning methods.
The paper tackles few-shot bioacoustic event detection by proposing a segment-level framework that uses both positive and negative events during training, achieving an F-measure of 62.73 on the DCASE 2022 validation set, outperforming the baseline by 34.02 points.
Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different sound classes, also known as positive events. In this study, we propose a segment-level few-shot learning framework that utilizes both the positive and negative events during model optimization. Training with negative events, which are larger in volume than positive events, can increase the generalization ability of the model. In addition, we use transductive inference on the validation set during training for better adaptation to novel classes. We conduct ablation studies on our proposed method with different setups on input features, training data, and hyper-parameters. Our final system achieves an F-measure of 62.73 on the DCASE 2022 challenge task 5 (DCASE2022-T5) validation set, outperforming the performance of the baseline prototypical network 34.02 by a large margin. Using the proposed method, our submitted system ranks 2nd in DCASE2022-T5. The code of this paper is fully open-sourced at https://github.com/haoheliu/DCASE_2022_Task_5.