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Learning Kernel-based HMMs for dynamic sequence synthesis
10th Pacific Conference on Computer Graphics and Applications, 2002. Proceedings.
In this paper we present an approach that synthesizes a dynamic sequence from another related sequence, and apply it to a virtual conductor: to synthesize linked figure animation from an input music track. We propose that the mapping between two dynamic sequences can be modeled with a Kernel-based Hidden Markov model, or KHMM. A KHMM is an HMM for which the kernel-based functions are used to model the state observation density of the joint input and output distribution. Specifically, the statedoi:10.1109/pccga.2002.1167842 dblp:conf/pg/WangZLXS02 fatcat:k4p7ar6lwna4tcbeyruuq7os3q