A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
An Experimental and Theoretical Comparison of Model SelectionMethods
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Video Rewrite: driving visual speech with audio
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Temporal interaction between an artificial orchestra conductor and human musicians
Computers in Entertainment (CIE) - SPECIAL ISSUE: Media Arts (Part II)
Interacting with a virtual conductor
ICEC'06 Proceedings of the 5th international conference on Entertainment Computing
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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 state observation density is estimated by employing a likelihood-weighted sampling scheme. Our KHMM model is ideal for dynamic sequence synthesis because the global dynamics are learned by the HMM, and subtle details in the dynamic mapping are kept in the kernel-based state density. We demonstrate our virtual conductor by synthesizing extensive animation sequences from input music sequences with different styles and beat patterns.