# Surrey System for DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection with Segment-level Metric Learning

arXiv: https://arxiv.org/abs/2207.10547 · HTML: https://mlsanity.com/paper/2207.10547/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection](https://mlsanity.com/paper/2309.08971/) (2023)
- [Few-shot bioacoustic event detection at the DCASE 2023 challenge](https://mlsanity.com/paper/2306.09223/) (2023)
- [WhaleVAD-BPN: Improving Baleen Whale Call Detection with Boundary Proposal Networks and Post-processing Optimisation](https://mlsanity.com/paper/2510.21280/) (2025)

## Abstract (arXiv)

Few-shot audio event detection is a task that detects the occurrence time of a novel sound class given a few examples. In this work, we propose a system based on segment-level metric learning for the DCASE 2022 challenge of few-shot bioacoustic event detection (task 5). We make better utilization of the negative data within each sound class to build the loss function, and use transductive inference to gain better adaptation on the evaluation set. For the input feature, we find the per-channel energy normalization concatenated with delta mel-frequency cepstral coefficients to be the most effective combination. We also introduce new data augmentation and post-processing procedures for this task. Our final system achieves an f-measure of 68.74 on the DCASE task 5 validation set, outperforming the baseline performance of 29.5 by a large margin. Our system is fully open-sourced at https://github.com/haoheliu/DCASE_2022_Task_5.