Channel-Spatial-Based Few-Shot Bird Sound Event Detection
Abstract
In this paper, we propose a model for bird sound event detection that focuses on a small number of training samples within the everyday long-tail distribution. As a result, we investigate bird sound detection using the few-shot learning paradigm. By integrating channel and spatial attention mechanisms, improved feature representations can be learned from few-shot training datasets. We develop a Metric Channel-Spatial Network model by incorporating a Channel Spatial Squeeze-Excitation block into the prototype network, combining it with these attention mechanisms. We evaluate the Metric Channel Spatial Network model on the DCASE 2022 Take5 dataset benchmark, achieving an F-measure of 66.84% and a PSDS of 58.98%. Our experiment demonstrates that the combination of channel and spatial attention mechanisms effectively enhances the performance of bird sound classification and detection.
Cite
@article{arxiv.2306.10499,
title = {Channel-Spatial-Based Few-Shot Bird Sound Event Detection},
author = {Lingwen Liu and Yuxuan Feng and Haitao Fu and Yajie Yang and Xin Pan and Chenlei Jin},
journal= {arXiv preprint arXiv:2306.10499},
year = {2023}
}
Comments
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference