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

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