Automatic mood classification of Indian Popular music
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Music has been an inherent part of human life when it comes to recreation; entertainment and much recently, even as a therapeutic medium. The way music is composed, played and listened to has witnessed an enormous transition from the age of magnetic tape recorders to the recent age of digital music players streaming music from the cloud. What has remained intact is the special relation that music shares with human emotions. 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 present today is 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. The problem, however, lies in the overhead of manual annotation of music with its corresponding mood and the challenge is to identify this aspect automatically and intelligently. 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. We have been successful to achieve a satisfactory precision of 70% to 75% in identifying the mood underlying the Indian popular music by introducing the bagging (ensemble) of random forest approach experimented over a list of 4600 audio clips.
Authors and Affiliations
Vikas Verma
Keywords
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