Towards time-varying music auto-tagging based on CAL500 expansion

Music auto-tagging refers to automatically assigning semantic labels (tags) such as genre, mood and instrument to music so as to facilitate text-based music retrieval. Although significant progress has been made in recent years, relatively little research has focused on semantic labels that are time-varying within a track. Existing approaches and datasets usually assume that different fragments of a track share the same tag labels, disregarding the tags that are time-varying (e.g., mood) or local in time (e.g., instrument solo). In this paper, we present a new dataset dedicated to time-varying music auto-tagging. The dataset, called CAL500exp, is an enriched version of the well-known CAL500 dataset used for conventional track-level tagging. Given the tag set of CAL500, eleven subjects with strong music background were recruited to annotate the time-varying tag labels. A new user interface for annotation is developed to reduce the subject´s annotation effort yet increase the quality of labels. Moreover, we present an empirical evaluation that demonstrates the performance improvement CAL500exp brings about for time-varying music auto-tagging. By providing more accurate and consistent descriptions of music content in a finer granularity, CAL500exp may open new opportunities to understand and to model the temporal context of musical semantics.