Mood Classification of Music Using AdaBoost
基于AdaBoost的音乐情绪分类

With fast development and boosting of stream media applications,automatic classification of audio signals becomes one of the hotspots on research and engineering.Since mood classification of music is involved with integrated representation and classification of social and natural properties of music,mechanism selection and architecture optimization should be implemented on the basis of different traditional music representations and classification methods.This paper discusses formation of weak classifiers in AdaBoost algorithm based on K-L transformation and GMM training and realizes mood classification of music with multi-layer classifier architecture. The experiments classify 163 songs into four mood classes:calm,sad,exciting and pleasant with 97.5% accuracy on training data and 93.9% accuracy on test data,which proves feasibility and potential value of this method.