Abstract: We most often choose to listen to a song or music which best fits our mood at that instant. In spite of this strong correlation, most of the music software’s presents today are still devoid of providing the facility of mood-aware play-list generation. This increase the time music listeners take in manually choosing a list of songs suiting a particular mood or occasion, which can be avoided by annotating songs with the relevant emotion category they convey.
We contribute by making an effort to build a system for automatic identification of mood underlying the audio songs by mining their spectral, temporal audio features. Our focus is specifically on Indian Popular Hindi songs. We have analyzed various data classification algorithms in order to learn, train and test the model representing the moods of these audio songs and developed an open source framework for the same.
Keywords: Random forest, Bagging, Music, Domain knowledge, Mood identification.
Title: A Novel Approach for Automatic Mood Classification of Indian Popular Music
Author: Shalini Malik
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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