基于神经网络的环境声音分类方法研究

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中文摘要：研究一种基于卷积神经网络(Convolutional Neural Network,CNN)和门控循环单元(Gated Recurrent Unit,GRU)相结合的环境声音分类方法.首先,分析CNN-GRU模型的基本结构;其次,探讨模型进行环境声音分类的数学原理;最后,采用ESC-50 数据集在MATLAB平台上对所提方法进行测试.实验结果表明,CNN-GRU模型的准确率、精确率、召回率及F1 值分别达到了 0.92、0.91、0.89 及 0.90,验证了该模型在处理环境声音分类任务中的有效性和健壮性.

外文标题：Research on Environmental Sound Classification Method Based on Neural Network

外文摘要：A methodology of environmental sound systematization based on the Convolutional Neural Network(CNN)and Gated Recurrent Unit(GRU)is studied.Firstly,the basic structure of the CNN-GRU model is analyzed;secondly,the mathematical principle of the model for environmental sound classification is discussed;finally,the proposed method is tested on the MATLAB platform using the ESC-50 dataset.The experimental results show that the accuracy rate,precision rate,recall rate and F1 value of CNN-GRU model reach 0.92,0.91,0.89 and 0.90,respectively,which verifies the effectiveness and robustness of the model in processing environmental sound classification tasks.

外文关键词：

Convolutional Neural Network(CNN)Gated Recurrent Unit(GRU)sound classification

作者：

徐圣林

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作者单位：

中国药科大学,江苏南京 211198

关键词：

卷积神经网络(CNN) 门控循环单元(GRU) 声音分类

出版年：

2024

DOI：