Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things  

作　　者：Jiajia Yu 

机构地区：[1]Department of Electronic and Computer Engineering,Southeast University Chengxian College,Nanjing,210088,China

出　　处：《Intelligent Automation & Soft Computing》2023年第7期777-797,共21页智能自动化与软计算（英文）

基　　金：supported by General Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province(2022SJYB0712);Research Development Fund for Young Teachers of Chengxian College of Southeast University(z0037);Special Project of Ideological and Political Education Reform and Research Course(yjgsz2206).

摘　　要：This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.

关 键 词：Convolutional neural networks speech enhancement noise recognition deep learning human-computer interaction Internet of Things 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]