Title: MUSIC EMOTION RECOGNITION WITH AUDIO AND LYRICS FEATURES

Issue Number: Vol. 6, No. 4
Year of Publication: Oct - 2016
Page Numbers: 260-273
Authors: C. V. Nanayakkara, H. A. Caldera
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P002150

Abstract:


Music Emotion Recognition (MER) is a field of science dedicated to recognizing emotions associated with music pieces. With the new interest in music therapy and music recommendation systems, MER has caught immense interest of scientists. This study is an attempt at discerning how well music related emotions can be predicted with music features; audio and lyrics. Emotion classes associated with songs were initially identified with clustering. Independent classification experiments were executed utilizing lyrics and audio features, to assess the comparative best model for predicting music emotions. The classification algorithms attempted in this research are Naïve Bayes, Random Forest, SVM and C4.5 decision. Random Forest with oversampling on the audio feature set produced comparative best results.