This paper aims at assessing the state as well as the progress made in classifying emotions in the music. Music is known as “language of emotions”, hence its logical to consider it as a medium for determining the emotions as well as categorize the music based on the emotions they bring forth [1]. Different segments of a particular music may express different emotions and since emotions are interpreted by humans there may arise some conflicts to come to a well-defined answer. The ability to deduce the emotions exhibited by music is of great significance. For example, the ability to deduce emotions can help understanding the patients suffering from Alexithymia, online music vendors like Spotify, iTunes etc. can provide customized playlists based on moods. The task of emotion determination comes under the task of Music Information Retrieval henceforth referred to as MIR. The paper explores the methods of emotion retrieval that includes methods that use textual information (lyrics, tags etc.), content-based approaches and systems combining multiple methods [2].

Acoustic features, Music Emotion Recognition, MIREX, Social Tagging, MFCC, Centroid, Flux, Rolloff, Chroma, Gaussian Mixture Model, Support Vector Machine, VA Model, PAD Values

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