Mood Based Music Player
Anuja Arora, Aastha Kaul, Vatsala Mittal · 2019
The ability of music to produce an emotional response in its listeners is one of its most exciting and yet the least understood property. Music not only conveys emotion and meaning but can also stir a listener's mood. This paper will study various algorithms based on classification to provide a clear methodology to i) classify songs into 4 mood categories and ii) detect users mood through his facial expressions and then combine the two to generate user customized music playlist. Songs have been classified by two approaches; by directly training the models namely KNN, Support Vector Machines (SVM), Random Forest and MLP using selected audio features and by predicting a songs arousal and valence values using these audio features. The first approach attains maximum accuracy of 70% using MLP while the latter achieves accuracy of 81.6% using SVM regression. The face mood classifier using HAAR classifier and fisher face algorithm attains precision of 92%.