RESEARCH OF MUSIC CLASSIFICATION BASED ON MOOD RECOGNITION

Music emotion is a vital component in the field of multimedia database recovery and computational musicology. The online musical datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, there is a require for robust automatic music emotion classifier system for organizing variety music pieces into different classes according to the specific viable information. Basic components are to be considered for music emotion classification audio feature origin and classifier design. In user propose diverse audio features to precisely characterize the music substance. The feature sets belong to groups dynamic, rhythmic, spectral, and harmonic. Four statistical parameters are considered as representatives, including the fourth-order central moments of each feature as well as covariance part. Number of unimportant parameters is forced by minimum unemployment maximum relevance(MRMR)algorithm and principal component analysis(PCA). Support Vector Machine(SVM) is used as a classifiers to classify the music mood recognition.