Abstract
In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.
Highlights
At present, most music and audio database systems generally use digital simulation technology and database stacking method for completion, but the connection between the database and the outside world must use LAN or Internet. erefore, there are some problems, such as low database security, poor user experience, and low retrieval matching degree [1]
This paper introduces the development status and research progress of various types of music and sound databases and puts forward a music and sound database establishment method based on Big Data and collaborative filtering algorithm
It is significantly higher than the other two methods in analysis accuracy and position judgment accuracy
Summary
Most music and audio database systems generally use digital simulation technology and database stacking method for completion, but the connection between the database and the outside world must use LAN or Internet. erefore, there are some problems, such as low database security, poor user experience, and low retrieval matching degree [1]. Combined with Big Data analysis strategy and complex system discrete dynamic modeling technology, collaborative filtering algorithm can deeply analyze and optimize the hierarchical structure of the database and solve the cost and efficiency problems of traditional music audio database. This study uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music and audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database and improve the efficiency and accuracy of retrieving various types of music and audio files in the database
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