TY - GEN
T1 - Probabilistic dance performance alignment by fusion of multimodal features
AU - Dremeau, Angelique
AU - Essid, Slim
PY - 2013/10/18
Y1 - 2013/10/18
N2 - 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.
AB - 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.
KW - Hidden Markov Model
KW - Multimodal alignment
KW - dance gestures
UR - https://www.scopus.com/pages/publications/84890445550
U2 - 10.1109/ICASSP.2013.6638337
DO - 10.1109/ICASSP.2013.6638337
M3 - Conference contribution
AN - SCOPUS:84890445550
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3642
EP - 3646
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -