Key points are not available for this paper at this time.
We describe the AIST Dance Video Database (AIST Dance DB), a shared database containing original street dance videos with copyright-cleared dance music. Although dancing is highly related to dance music and dance information can be considered an important aspect of music information, research on dance information processing has not yet received much attention in the Music Information Retrieval (MIR) community. We therefore developed the AIST Dance DB as the first large-scale shared database focusing on street dances to facilitate research on a variety of tasks related to dancing to music. It consists of 13,939 dance videos covering 10 major dance genres as well as 60 pieces of dance music composed for those genres. The videos were recorded by having 40 professional dancers (25 male and 15 female) dance to those pieces. We carefully designed this database so that it can cover both solo dancing and group dancing as well as both basic choreography moves and advanced moves originally choreographed by each dancer. Moreover, we used multiple cameras surrounding a dancer to simultaneously shoot from various directions. The AIST Dance DB will foster new MIR tasks such as dance-motion genre classification, dancer identification, and dance-technique estimation. We propose a dance-motion genre-classification task and developed four baseline methods of identifying dance genres of videos in this database. We evaluated these methods by extracting dancer body motions and training their classifiers on the basis of long short-term memory (LSTM) recurrent neural network models and support-vector machine (SVM) models.
Tsuchida et al. (Mon,) studied this question.