Feasibility of Music Composition Using Deep Learning-Based Quality Classification Models

Abstract

Polyphonic music technique is the foundation of students' understanding of musical works. The mastery of polyphonic music techniques enables students to better understand the meaning of musical works and get in touch with the soul of music. Hence, teaching polyphonic music is a compulsory course for composition theory. In the past, all the concepts taught in the composition theory class included the use of the main key, and the minimal amount of polyphonic music works was covered. Also, even if students encountered polyphonic music, a brief inclusion of the same would be included in teaching, creating difficulties for the students to understand polyphonic music well. Intelligent music composition, however, refers to a formalized process that allows the composer to create music with the help of a computer, ensuring minimal human intervention. With the popularity of the Internet and the rapid development of multimedia technology, the majority of the users now use online music applications. Therefore, the need to automatically organize and manage the huge amount of music data effectively has evolved. Studying intelligent music composition helps to understand and simulate the way of thinking of composers in making compositions. It also helps to assist composers in making music, in addition to entertaining people. Considering the aforementioned, the present paper uses a deep learning-based quality classification model for music composition feasibility. The experimental results show that the algorithm has the advantages of fast detection speed and high quality. It helps composers to compose music, greatly reduces the workload, and also ensures certain promotion value.