Differentiated analysis for music traffic in software defined networks: A method of deep learning. (April 2023)

• Record Type: Journal Article
• Authors: Yang, Yuanyuan; Soradi-Zeid, Samaneh
• Is Part Of: Computers & electrical engineering. Volume 107(2023)
• Publication Date: 2023-04
• Subjects: Music traffic -- Apriori method -- Software defined network -- Differentiated analysis -- Deep learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2023.108649
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the rapid development of software defined network, people have more and better choices for huge music network resources. However, they cannot find music traffic and services that meet their needs quickly and accurately. Deep learning technology has achieved various successes in speech signal processing and other fields. To meet the demand for music traffic, we propose an improved Apriori method for deep learning and implement it systematically. We then study differentiated analysis for music traffic systems and analyze the effectiveness of music traffic analysis by comparing the improved Apriori method with other methods. The experimental results indicate that the differentiated analysis for music traffic system using the improved Apriori method has a faster response speed and higher accuracy of differentiated user traffic; additionally, it has better practicability for differentiated analysis for music traffic.