The application and research of double-layer music emotion classification model based on random forest algorithm in digital music

It is urgent to solve the problem of music emotion classification. The stochastic forest algorithm is easy to operate and performs better than other single-layer classification models. Aiming at the problems of feature extraction and classification in conventional music emotion classification methods, music features are divided into long-term features and short-term features, and a two-layer music emotion classification model integrating a random forest (RF) algorithm is designed. The experimental results showed that the SVM model using the Gaussian radial basis kernel function had the highest classification accuracy of 90.78% in training the SVM model. The overall classification accuracy of the two-layer music emotion classification model was 98.92%, the recall rate was 97.63%, and its indicators in different emotion categories were the highest, with an average F1 value of 0.919. To sum up, the two-layer music emotion classification model based on the RF algorithm proposed in the research has excellent recognition and classification capabilities.