Open Access

ARTICLE

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

Jiajia Yu^*

Department of Electronic and Computer Engineering, Southeast University Chengxian College, Nanjing, 210088, China

* Corresponding Author: Jiajia Yu. Email:

Intelligent Automation & Soft Computing 2023, 37(1), 777-797. https://doi.org/10.32604/iasc.2023.037354

Received 31 October 2022; Accepted 07 February 2023; Issue published 29 April 2023

Abstract

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 delivered 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 enhancement 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 adaptability. This study provides a reference and basis for research applying neural networks in speech enhancement.

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Keywords

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