Compact feature subset-based multi-label music categorization for mobile devices

Abstract

Music categorization based on acoustic features extracted from music clips and user-defined tags forms the basis of recent music recommendation applications, because relevant tags can be automatically assigned based on the feature values and their relation to tags. In practice, especially for handheld lightweight mobile devices, there is a certain limitation on the computational capacity, owing to consumers’ usage behavior or battery consumption. This also limits the maximum number of acoustic features to be extracted, and results in the necessity of identifying a compact feature subset that is used for the music categorization process. In this study, we propose an approach to compact feature subset-based multi-label music categorization for mobile music recommendation services. Experimental results using various multi-labeled music datasets reveal that the proposed approach yields better performance when compared to conventional approach.