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Sound event localization and detection based on deep learning

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Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spa-tial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multi-overlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimen-sion as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential informa-tion and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the pro-posed SELD algorithm.Field experiments show that the pro-posed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy com-pared with the baseline method.

sound event localization and detection(SELD)deep learningconvolutional recursive neural network(CRNN)chan-nel attention mechanism

ZHAO Dada、DING Kai、QI Xiaogang、CHEN Yu、FENG Hailin

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School of Mathematics and Statistics,Xidian University,Xi'an 710071,China

Science and Technology on Near-Surface Detection Laboratory,Wuxi 214035,China

国家自然科学基金Foundation of Science and Technology on Near-Surface Detection LaboratoryFoundation of Science and Technology on Near-Surface Detection LaboratoryFoundation of Science and Technology on Near-Surface Detection Laboratory陕西省自然科学基金

61877067TCGZ2019A002TCGZ2021C00361424142005112021JZ-19

2024

10.23919/JSEE.2023.000110

系统工程与电子技术(英文版)
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会 中国系统仿真学会

系统工程与电子技术(英文版)

CSTPCD
影响因子：0.64
ISSN：1004-4132
年,卷(期)：2024.35(2)
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