复杂环境下基于深度学习的声音识别研究

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NETL
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中文摘要：针对复杂环境下的声音识别问题,提出一种基于深度学习的声音识别方法.首先,通过自适应滤波降噪和梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficient,MFCC)提取等方法提取声音特征.其次,采用L2正则化的卷积神经网络(Convolutional Neural Network,CNN)识别声音,以提高模型的泛化能力和准确性.最后,使用ESC-50数据集对所提方法进行验证和测试.实验结果表明,该方法的精确率、准确率及召回率均优于对比方法.

外文标题：Research on Deep Learning Based Sound Recognition in Complex Environments

外文摘要：A deep learning based sound recognition method is proposed for the problem of sound recognition in complex environments. Firstly, sound features are extracted through methods such as adaptive filtering noise reduction and Mel-Frequency Cepstral Coefficient (MFCC) extraction. Secondly, L2 regularized Convolutional Neural Network (CNN) are used to recognize sounds, in order to improve the model's generalization ability and accuracy. Finally, validate and test the proposed method using the ESC-50 dataset. The experimental results show that the accuracy, precision, and recall of this method are superior to the comparison methods.

外文关键词：

complex environmentConvolutional Neural Network (CNN)voice recognition

作者：

付兆婷

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作者单位：

白银开放大学白银学院,甘肃白银 730900

关键词：

复杂环境卷积神经网络(CNN) 声音识别

出版年：

2024

DOI：