An approach for mulit-label music mood classification

Music can express emotion in succinctly but in an effective way. Peoples select different music at different time concordance with listening time's mood and objectives. Music classification and retrieval by perceived emotion is natural and functionally powerful. Since, human perception of music mood varies individual to individual; multi-label music mood classification has become a challenging problem. Because music mood may well change one or more times in an entire music clip, an exact song may offer more than one music taste to the music listener. Therefore, tracking mood changes in an entire music clip is given precedence in multi-label music mood classification tasks. This paper presents self-colored music mood segmentation and a hierarchical framework based on new mood taxonomy model to automate the task of multi-label music mood classification. The proposed mood taxonomy model combines Thayer's 2 Dimension (2D) model and Schubert's Updated Hevner adjective Model (UHM) to mitigate the probability of error causing by classifying upon maximally 4 class classification from 9. The verse and chorus parts approximately 50 to 110 sec of the whole songs is exerted manually as input music trims in this system. Consecutive self-colored mood is segmented by the image region growing method. The extracted feature sets from these segmented music pieces are ready to inject the Fuzzy Support Vector Machine (FSVM) for classification. One-against-one (O-A-O) multi-class classification method are used, for 9 class classification upon updated Hevner labeling. The hierarchical framework with new mood taxonomy model has the advantage of reducing computational complexity due to the number of classifiers employed for O-A-O approach as only 19 instead of 36 classifiers.