Query Processing And Optimization Method of Ethnic Music Data Based on Deep Belief Network

With the development of the Internet and the large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music that users like to hear from the massive digital music data on the Internet has become a key issue in the field of ethnic music information retrieval. Key issues and main goals addressed. There is a complex relationship between ethnic music data, which is suitable for modeling and query processing with data structure. However, the existing query processing algorithms only focus on structural features, and are not optimized for folk music metadata and music content data, and the query efficiency is not high. Currently, the amount of data in various media is exploding, and digital music is everywhere and increasing day by day. At the same time, various music applications have also greatly contributed to the advancement of information technology, such as music sharing, which made the ancient peer-to-peer technology mainstream at the end of the 20th century. The method used in this paper is a deep belief network. Compared with other classification algorithms, its error convergence speed is faster, and the final recognition error for the data set is relatively smaller.