Music Emotion Feature Recognition based on Internet of Things and Computer-Aided Technology

Abstract

As an important part of human life, music can convey emotion and regulate the emotions of listeners. Emotion is one of the essential features of music, and the relationship between music and emotion has become the subject of many academic studies. At present, with the rapid development of information technology and artificial intelligence, music emotion recognition has made rapid progress and become one of the important research directions in the field of digital music. Aiming at the problem of poor classification effect of musical emotion caused by the monotony of Support Vector Machine (SVM) projection space, this paper proposes an optimized SVM model for music feature emotion recognition. The new method can not only improve the accuracy of music emotion classification, but also improve the running speed and interpretability of the model. At the end, the practicality and reliability of the new approach are verified by public classification data sets and real music emotion data sets. This paper proposes an optimized SVM model for music feature emotion recognition. The new method can not only improve the accuracy of music emotion classification, but also improve the running speed and interpretability of the model. Finally, the practicality and reliability of the new approach are verified by both the public classification data sets and real music emotion data sets.