Intelligent Management Framework for Internet Multimedia Resources Based on Deep Neural Network and Web Crawlers
Abstract
This work focuses on an intelligent management framework for vocal music media resources, with use of deep neural networks and web crawlers. The web driver module is used to simulate the Google browser access mode to collect and preprocess the data of the China vocal music network webpage project, and the delay code is added to solve the problem of Internet Protocol (IP) restrictions on crawling webpages. After parsing, the content of the project description is obtained and temporarily stored in the local file through the panda’s module to realize the automatic collection of project data. Membership functions of four basic types of emotions are designed. By utilizing the analytic hierarchy process, the experience and wisdom of many music experts are gathered. The weights of activity and the evoking force in determining the music emotion membership value are discussed, trying to make the membership function closer to reality. Through the application of the music emotion analysis model, the music emotion analysis technology is combined with the recommendation algorithm, and the music recommendation algorithm combined with the emotion analysis is realized. The experimental test proves that the algorithm has higher accuracy than the traditional music recommendation algorithm.
This paper was recommended by Regional Editor Takuro Sato.
