MoodPlayer: An Emotion Based Music Player

Abstract

This report describes, songs, as expressional medium, has always been a consistent choice to portray and comprehend the feelings of a human being. A trustworthy system of emotion base classification can go a long way in helping us parse their significance. However, there are no optimum outcomes in the research field in emotion base classification systems. In this paper, we present an optimum cross-platform music player, MoodPlayer, which will suggest the songs according to the current mood of user. MoodPlayer provides smart and accurate mood base song recommendation by integrating the competence of emotion context reasoning within our versatile music suggestion system. Our music player works in two modules: Emotion Module, Song and Recommendation Module. A photo of user’s face will be captured and taken as input in the initial steps of Emotion Module and using some algorithms the mood will be identified. The song and recommendation module proposes music to the end user by mapping their emotions to the mood type of the song and changes the playlist if the mood of the person is changed