Probabilistic dance performance alignment by fusion of multimodal features
Angélique Drémeau, Slim Essid · 2013
This paper presents a probabilistic framework for the multimodal alignment of dance movements. The approach is based on a Hidden Markov Model (HMM) and considers different feature functions, each corresponding to a particular modality, namely motion features, extracted from depth maps, and audio features, extracted from audio recordings of dancers' steps. We show that this approach allows performing accurate dancer alignment, while constituting a general framework for various multimodal alignment tasks.