A Simple Method for User-Driven Music Thumbnailing

More and more music is becoming available digitally, increasing the need to navigate through large numbers of audio tracks easily. One approach for improving the browsing experience is music thumbnailing: the procedure of finding a continuous fragment that can represent the whole musical piece. This paper proposes a human-centred approach to creating thumbnails based on listeners’ perception, directly asking listeners to identify the most characteristic fragment. We carried out a user study to assign representativeness scores to multiple fragments from a selection of popular music tracks. To strengthen the results, we performed a replication of the same user study with new participants and a different set of music. Thereafter, we used audio features, a segmentation algorithm, and participants’ overall familiarity with the songs to predict representativeness scores. The results suggest that neither segmentation nor familiarity have a significant impact on users’ thumbnail preferences: even segments with starting points that pay no regard to song structure can be suitable thumbnails. Three high-level audio characteristics, however, do impact the perceived representativeness of a fragment: Raw Intensity, Melodic Conventionality, and Conventionality of Intensity. Based on these findings, we propose a new, easy-to-apply method for music thumbnailing