Classification of music by mood is a growing area of research with interesting applications, including navigation of large music collections. Mood classifiers are usually based on acoustic features extracted from the music, but often they are used without knowing which ones are most effective. This paper describes how 63 acoustic features were evaluated using 2,389 music tracks to determine their individual usefulness in mood classification, before using feature selection algorithms to find the optimum combination.
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