Channel-Spatial-Based Few-Shot Bird Sound Event Detection

Lingwen Liu; Yuxuan Feng; Haitao Fu; Yajie Yang; Xin Pan; Chenlei Jin

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arXiv · 2306.10499

Channel-Spatial-Based Few-Shot Bird Sound Event Detection

Lingwen Liu·Yuxuan Feng·Haitao Fu·Yajie Yang·Xin Pan·Chenlei Jin

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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.

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Lingwen Liu, Yuxuan Feng, Haitao Fu, Yajie Yang, Xin Pan, Chenlei Jin. 2023-06-18. Channel-Spatial-Based Few-Shot Bird Sound Event Detection. https://arxiv.org/abs/2306.10499

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