NDLI: Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis

Content Provider	IEEE Xplore Digital Library
Author	Ofli, F. Erzin, E. Yemez, Y. Tekalp, A.M.
Copyright Year	1999
Abstract	We propose a novel framework for learning many-to-many statistical mappings from musical measures to dance figures towards generating plausible music-driven dance choreographies. We obtain music-to-dance mappings through use of four statistical models: 1) musical measure models, representing a many-to-one relation, each of which associates different melody patterns to a given dance figure via a hidden Markov model (HMM); 2) exchangeable figures model, which captures the diversity in a dance performance through a one-to-many relation, extracted by unsupervised clustering of musical measure segments based on melodic similarity; 3) figure transition model, which captures the intrinsic dependencies of dance figure sequences via an n-gram model; 4) dance figure models, capturing the variations in the way particular dance figures are performed, by modeling the motion trajectory of each dance figure via an HMM. Based on the first three of these statistical mappings, we define a discrete HMM and synthesize alternative dance figure sequences by employing a modified Viterbi algorithm. The motion parameters of the dance figures in the synthesized choreography are then computed using the dance figure models. Finally, the generated motion parameters are animated synchronously with the musical audio using a 3-D character model. Objective and subjective evaluation results demonstrate that the proposed framework is able to produce compelling music-driven choreographies.
Sponsorship	IEEE Signal Processing Society IEEE Circuits and Systems Society IEEE Communications Society IEEE Computer Society
Starting Page	747
Ending Page	759
Page Count	13
File Size	1737960
File Format	PDF
ISSN	15209210
Volume Number	14
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Motion segmentation Animation Feature extraction Analytical models Computational modeling Solid modeling musical measure clustering Automatic dance choreography creation multimodal dance modeling music-driven dance performance synthesis and animation music-to-dance mapping
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Science Applications Media Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis

Correction to Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis [Jun 12 747-759]

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Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis

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