Fuzzy Computing Model of Music Emotion on Account of Machine Learning Algorithm

Abstract

Music as a for people to appreciate the art form, had a great technical challenge for its classification, because music emotion relative to the immobilized, formatted transaction is different, music emotion is fuzzy, the boundary is not clear, a music track tend to have a variety of emotional experience, in order to solve the problem of music classification, fuzzy kernel clustering algorithm will be mapped to high-dimensional music sample Feature space can better identify and extract features, so as to continue clustering more accurately. It is difficult to distinguish the emotional features of a musical track which has both excited feelings and missing feelings. To solve this problem, a fuzzy computing model of musical emotions based on machine learning algorithm is proposed. This paper studies the content and principle of fuzzy computing model of music emotion based on machine learning algorithm, expounds the main content of fuzzy computing model of music emotion, and talks about the effect of fuzzy computing model of music emotion based on machine learning algorithm on fuzzy computing processing field. The data show that the error rate of the music emotion fuzzy calculation model based on machine learning algorithm is only 0.1 than that of other models.