Published Paper Details:

Music is an amusing sound that leads us to experience peace and happiness. One of the fine arts today is music. Music digitalization provides us to access easily the different kinds of music. We often choose to listen to a song or music which best fits our mood at that moment, by means of features of music the mood of a song can be expressed. Most of the music software present today are lacking for providing the facility of mood-aware play-list generation. Manually choosing songs list suiting a specific mood or occasion by music listeners may be time consuming, that can be avoided by annotating songs with the relative emotion category. Here, work involves analysis of various features in order to learn, train and test the model representing the moods of the audio songs. Some of the basic components are to be considered for music emotion classification audio feature consists of feature set belong to groups dynamic, rhythmic, spectral, and harmonic. Support Vector Machine (SVM) is used as classifiers to classify the music mood recognition.