Audio-to-Tag mapping: A novel approach for music similarity computation

Similarity measurement between musical pieces is a hard problem. Recent research on contextual information assigned (tags) in social networking services has shown to be highly effective in measuring musical similarity. Nevertheless, such an approach requires adequate amount of tags assigned to each musical datum. In the case of the so called “cold-start” problem, this assumption is not valid for several music data. Herein, we address this problem by proposing the utilisation of a learning mechanism that maps musical data from audio feature space to tag feature space. The developed mapping can be applied to musical data with no or limited contextual information, in order to more accurately evaluate similarity and avoid the sole use of audio-based similarity measures that may affect the similarity measurement quality. Experimental results with real musical data illustrate the substantial gains of the proposed method.