MusiClef: Multimodal Music Tagging Task
The Multimodal Music Tagging task 2012 strives to foster novel and creative multimodal approaches to learn relations between music items and semantic text labels. Attaching semantic labels to multimedia items is a very labor-intensive task if performed manually. Hence, methods that automatically assign a set of tags to a given piece of music are highly desired by the industry. Such auto-taggers further pave the way for various intelligent music retrieval applications, such as automated playlist generators or music recommendation systems. They also enable faceted browsing of music collections as well as semantic search.
In this task, participants will be given several sets of multimodal data related to music songs (see below). The aim is then to build an auto-tagger using some or all of the provided data sets. Including additional data sources is possible as well (e.g., music video clips, images of album covers, or song lyrics). Investigating which categories of tags (e.g., genres, styles, emotions, ...) can be learned well and which ones are more challenging is another relevant question that should be addressed.