吉林大学学报(理学版) ›› 2025, Vol. 63 ›› Issue (4): 1143-1149.

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结合通道与空间注意力机制的声音事件检测方法

冯宇轩, 刘玲文, 付海涛, 朱丽   

  1. 吉林农业大学 信息技术学院, 长春 130118
  • 收稿日期:2023-12-29 出版日期:2025-07-26 发布日期:2025-07-26
  • 通讯作者: 朱丽 E-mail:zhuli@jlau.edu.cn

Sound Event Detection Method Combining Channel and Spatial Attention Mechanism

FENG Yuxuan, LIU Lingwen, FU Haitao, ZHU Li   

  1. College of Information Technology, Jilin Agricultural University, Changchun 130118,  China
  • Received:2023-12-29 Online:2025-07-26 Published:2025-07-26

摘要: 针对样本稀缺条件下声学特征提取不充分的问题, 提出一种基于通道和空间压缩的小样本声音事件检测方法. 该方法通过构建双压缩注意力机制, 在通道维度进行特征筛选, 在空间维度实现特征聚焦, 有效提升了原型网络在小样本场景下的特征判别能力. 实验结果表明, 该方法在数据集DCASE(detection and classification of acoustic scenes and events)上的F1达66.84%, 相比原型网络方法提升4.11个百分点, 为野生动物监测和生态环境评估等实际应用提供了更可靠的技术支持.

关键词: 声音事件检测, 原型网络, 通道注意力, 空间注意力

Abstract: Aiming at the problems of insufficient acoustic  feature extraction under sample scarcity conditions, we proposed a small sample sound event detection method based on channel  and spatial compression.  The method constructed a dual compression attention mechanism to screen features  in channel dimension and achieved  feature focusing in the spatial dimension, effectively improving the feature discrimination ability of the prototype network in small sample scenarios. The experimental results show that F1-score  of the method on the dataset DCASE (detection and classification of acoustic scenes and events) reaches 66.84%, an improvement of 4.11 percentage points compared to the prototypical network, providing more reliable technical  support for practical applications such as wildlife monitoring and ecological environment assessment.

Key words: sound event detection, prototype network, channel attention, spatial attention

中图分类号: 

  • TP391