Music Video Dataset

The strong emphasis on classification experiments is motivated by their facilitation of rapid content descriptor development. The data-sets are carefully selected to be specialized tools in this process. Despite their different focusses, all sub-sets are non-overlapping, thus can be combined to the \textit{MVD-MIX} data-set, which is intended for similarity retrieval and recommendation experiments.

Subset Classes Videos Artists
MVD-VIS 8 800 490
     Music genre classification with visual features
MVD-MM 8 800 550
     Multi-modal music genre classification
MVD-MIX 16 1600 1040
     Extended multi-modal music genre classification

The dataset creation was preceded by the selection of genres. For the MVD-VIS dataset eight orthogonal classes with minimum overlap were defined. This aim was accomplished by restricting the search on clearly defined sub-genres. For the MVD-MM dataset eight top-level genres with high inter-genre overlaps were selected. Additional avoidance of overlaps between the genres of these two subsets allow for a combination into the bigger MVD-MIX dataset. Each genre consists of 100 selected videos. Entries for the MVD-VIS and MVD-MM datasets were selected primarily by their audible properties. This decision was based on the introducing definition of Music Video Information Retrieval - a cross-domain approach to MIR problems.

References

  • Alexander Schindler and Andreas Rauber. An audio-visual approach to music genre classification through affective color features. In Proceedings of the 37th European Conference on Information Retrieval (ECIR'15), Vienna, Austria, March 29 - April 02 2015.