Automatic Classification Analysis of Tibetan Folk Music Based on Adaboost Algorithm

Automatic classification of music is a necessary prerequisite for rapid and accurate retrieval of music resources, and its potential application needs are enormous; However, due to the ambiguity of music classification and the diversity of music signals, the study of automatic music classification is still in its infancy; This paper proposes the application of machine learning Adaboost algorithm in the automatic classification of Tibetan folk music, extracts the characteristics of music signals, and uses Adaboost algorithm and decision tree to perform predictive data analysis on Tibetan folk music signals; The results show that the Adaboost algorithm has higher accuracy and better robustness in the classification of Tibetan folk music signals, the accuracy of the Adaboost algorithm is improved by 12.5% compared to the decision tree.