{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:46:08Z","timestamp":1776811568439,"version":"3.51.2"},"reference-count":19,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,12,19]]},"abstract":"<jats:p>Speech denoising refers to people finding useful information in noise-affected speech data and processing it. This technology is an important part of speech signal processing technology. With the continuous progress of science and technology, its application in various fields is gradually improving, among which voice noise reduction technology has also been well developed. Especially in recent years, with the rapid development of artificial intelligence science and technology, anti-network speech denoising technology based on deep learning has been derived. Compared with traditional speech denoising techniques, it has better performance in resolving speech data distortion and denoising results. However, in practical applications, this method still has some shortcomings, such as: some unique noises are not removed, and the effective information extraction is distorted under the background of high-intensity noise. In response to these outstanding problems, this research is based on the anti-network speech denoising technology, combined with the Wiener filter speech denoising algorithm, and applies artificial intelligence technology to network security technology management. In this paper, two algorithms, COVL and SSNR, are used to compare the original input speech data signal with noise and the speech data signal preprocessed by Wiener filtering. The additional information is input into the generation network and the discriminant network, and the additional information is used to guide the data generation. The least squares loss function of the binary coding algorithm is used to replace the cross entropy loss function, so that the original discriminant network loss function is not changed, which improves the accuracy of discrimination. This improved technology is superior in terms of speed, productivity and improved accuracy. While building the system, mature network technologies and equipment applications are fully applied, and speech de-noising technology is used for network security situation assessment and prediction simulation tests. The simulation test results show that the network security situation assessment is reasonable and effective, and the prediction is accurate in real time.<\/jats:p>","DOI":"10.3233\/jcm-226368","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T11:33:01Z","timestamp":1661254381000},"page":"2243-2255","source":"Crossref","is-referenced-by-count":0,"title":["Speech denoising algorithm and artificial intelligence application in network security"],"prefix":"10.66113","volume":"22","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"1","key":"10.3233\/JCM-226368_ref1","first-page":"108","article-title":"Design of DSP speech recognition system based on denoising technology","volume":"36","author":"Wei","year":"2017","journal-title":"Sens Microsyst."},{"issue":"1","key":"10.3233\/JCM-226368_ref2","first-page":"41","article-title":"Neural 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adversarial network","volume":"27","author":"Han","year":"2019","journal-title":"Electr Des Eng."},{"issue":"2","key":"10.3233\/JCM-226368_ref7","first-page":"344","article-title":"Research on key technologies of speech signal denoising based on deep convolutional neural network","volume":"50","author":"Yang","year":"2022","journal-title":"Comput Digital Eng."},{"issue":"7","key":"10.3233\/JCM-226368_ref8","first-page":"20","article-title":"Research on multiscale Kalman filter speech enhancement algorithm","author":"Zheng","year":"2021","journal-title":"Inf Technol."},{"issue":"2","key":"10.3233\/JCM-226368_ref9","first-page":"281","article-title":"Chinese speech emotion recognition method based on improved speech processing based on convolutional neural network","volume":"48","author":"Qiao","year":"2022","journal-title":"Comput Eng."},{"issue":"2","key":"10.3233\/JCM-226368_ref10","first-page":"19","article-title":"Speech enhancement technology based on fully convolutional neural network based on lift codec","volume":"11","author":"Sun","year":"2021","journal-title":"Intell Comput Appl."},{"issue":"1","key":"10.3233\/JCM-226368_ref11","first-page":"131","article-title":"Chinese speech recognition technology based on neural network","volume":"45","author":"Dai","year":"2022","journal-title":"J Sichuan Norm Univ (Nat Sci Ed)."},{"issue":"1","key":"10.3233\/JCM-226368_ref12","first-page":"77","article-title":"Influence of attention mechanism on generative adversarial network speech enhancement transfer learning model","volume":"40","author":"Cao","year":"2021","journal-title":"Tech Acoust."},{"issue":"2","key":"10.3233\/JCM-226368_ref13","first-page":"180","article-title":"Generative adversarial network speech reverberation with additive frequency domain decomposition","volume":"54","author":"Quan","year":"2022","journal-title":"Eng Sci Technol."},{"issue":"1","key":"10.3233\/JCM-226368_ref14","first-page":"57","article-title":"A new speech denoising model using MFCC","volume":"36","author":"Jin","year":"2021","journal-title":"J Hubei Univ Technol."},{"issue":"23","key":"10.3233\/JCM-226368_ref15","first-page":"52","article-title":"Adaptive speech denoising based on EEMD and formants","volume":"44","author":"Li","year":"2021","journal-title":"Mod Electr Technol."},{"issue":"5","key":"10.3233\/JCM-226368_ref16","first-page":"485","article-title":"A deep speech denoising method combining residual mechanism and attention mechanism","volume":"32","author":"Li","year":"2021","journal-title":"J Optoelectron Laser."},{"issue":"1","key":"10.3233\/JCM-226368_ref17","first-page":"174","article-title":"Design of optical voiceprint change recognition system based on ChaffMatrix","volume":"43","author":"Bai","year":"2022","journal-title":"Laser J."},{"issue":"11","key":"10.3233\/JCM-226368_ref18","first-page":"2350","article-title":"Hidden attack on speaker recognition based on single \u201caudio pixel\u201d perturbation","volume":"58","author":"Shen","year":"2021","journal-title":"Comput Res Develop."},{"issue":"5","key":"10.3233\/JCM-226368_ref19","first-page":"18","article-title":"Microseismic data denoising method based on generative adversarial network","volume":"40","author":"Zheng","year":"2021","journal-title":"Lab Res Explor."}],"container-title":["Journal of Computational Methods in Sciences and 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