  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.
