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arXiv · 2206.10175

A Multi-grained based Attention Network for Semi-supervised Sound Event Detection

Ying Hu·Xiujuan Zhu·Yunlong Li·Hao Huang·Liang He
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Abstract

Sound event detection (SED) is an interesting but challenging task due to the scarcity of data and diverse sound events in real life. This paper presents a multi-grained based attention network (MGA-Net) for semi-supervised sound event detection. To obtain the feature representations related to sound events, a residual hybrid convolution (RH-Conv) block is designed to boost the vanilla convolution's ability to extract the time-frequency features. Moreover, a multi-grained attention (MGA) module is designed to learn temporal resolution features from coarse-level to fine-level. With the MGA module,the network could capture the characteristics of target events with short- or long-duration, resulting in more accurately determining the onset and offset of sound events. Furthermore, to effectively boost the performance of the Mean Teacher (MT) method, a spatial shift (SS) module as a data perturbation mechanism is introduced to increase the diversity of data. Experimental results show that the MGA-Net outperforms the published state-of-the-art competitors, achieving 53.27% and 56.96% event-based macro F1 (EB-F1) score, 0.709 and 0.739 polyphonic sound detection score (PSDS) on the validation and public set respectively.

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Ying Hu, Xiujuan Zhu, Yunlong Li, Hao Huang, Liang He. 2022-06-21. A Multi-grained based Attention Network for Semi-supervised Sound Event Detection. https://doi.org/10.21437/interspeech.2022-767

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