复杂环境下基于深度学习的声音识别研究
Research on Deep Learning Based Sound Recognition in Complex Environments
付兆婷1
作者信息
- 1. 白银开放大学白银学院,甘肃 白银 730900
- 折叠
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.
Abstract
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.
关键词
复杂环境/卷积神经网络(CNN)/声音识别Key words
complex environment/Convolutional Neural Network (CNN)/voice recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据