信息与控制2025，Vol.54Issue(3)：413-427,15.DOI:10.13976/j.cnki.xk.2025.4052
基于图神经网络的交通场景声音事件检测
Sound Event Detection in Traffic Scene Based on Graph Neural Network
摘要
Abstract
To enhance event detection in traffic scenes using sound signals in complex driving environ-ments,we propose a sound event detection method utilizing a graph neural network for cross-modal information extraction.First,we apply the sound event window method to capturing simultaneous and successive relationships in the sound signal,filtering out potential noise and constructing a graphical structure.We then enhance the graph convolutional neural network to balance relation-ship weights among neighbors and itself,preventing excessive smoothing,and adopt it to under-stand the relationship information in the graph.Additionally,acoustic features and timing informa-tion of sound events are learned using a convolutional recurrent neural network,with event relation-ship information acquired through cross-modal fusion to enhance model detection performance.Compared to the original CRNN(Convolutional Recurrent Neural Network)model,the proposed method achieves better detection performance on TUT Sound Events 2016 and TUT Sound Events 2017 datasets,with a 10.3％and 2.04％increase in F1 score,a 5.89％and 10.06％reduction in error rate,and an 8.1％and 6.07％decrease in global error rates,respectively.Experimental re-sults show that the proposed method can effectively improve the perception ability of intelligent ve-hicles to the surrounding environment during driving.关键词
声音事件检测/智能交通/图神经网络/交叉模态融合/图形构建Key words
sound event detection/intelligent transportation/graph neural network/cross-modal fusion/graph construction分类
信息技术与安全科学引用本文复制引用
姜彦吉,郭丁旭,邱友利,董浩..基于图神经网络的交通场景声音事件检测[J].信息与控制,2025,54(3):413-427,15.基金项目
辽宁省教育厅高等学校基本科研项目(LJKZ0338) （LJKZ0338）
广东省科技创新战略专项市县科技创新支撑项目(STKJ2023071) （STKJ2023071）
葫芦岛市科技计划项目(2023JH(1)4/02b) （2023JH(1）
