Music plays an important role in human history, especially in the digital age. Now the number of music is growing exponentially, while the demand for music organization, classification and retrieval is increasing. The classification and retrieval based on music emotion is different from the traditional classification and retrieval method based on music text. It pays more attention to the emotional expression of creators and the unique characteristics of music in psychology. It is also an indispensable personalized demand for users. Therefore, more and more attention has been paid. This paper analyzes the current research situation of music emotion recognition at home and abroad, and summarizes the existing emotional models, data sets, music features, machine learning algorithms, system frameworks in the study of music emotion recognition. According to these characteristics, we choose to use machine learning to recognize music emotion. According to the experiment, the accuracy of the SVM algorithm is 88, the recognition rate of happy emotion is 90, and the recognition rate of anger is 75.

Yun Liu. Neural Network Technology in Music Emotion Recognition. International Journal of Frontiers in Sociology (2021), Vol. 3, Issue 1: 1-10. https://doi.org/10.25236/IJFS.2021.030101.

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