The XMM library contains different probabilistic models for motion recognition and Mapping between movement and media. The XMM library was developed for movement interaction in creative applications and implements an interactive machine learning workflow with fast training and continuous, real-time inference. XMM is a portable, cross-platform C++ library that implements Gaussian Mixture Models and Hierarchical Hidden Markov Models for recognition and regression.
This library is one of the output of the Jules Françoise‘s PhD. It is an open-source C++ library (with a possible proprietary close license for commercial applications).
MaxMSP implementations are freely available in the MuBu & Friend package, as explained here.
More information here http://julesfrancoise.com/xmm/
- J. Françoise, N. Schnell, R. Borghesi, and F. Bevilacqua, Probabilistic Models for Designing Motion and Sound Relationships. In Proceedings of the 2014 International Conference on New Interfaces for Musical Expression, NIME’14, London, UK, 2014.
- J. Françoise, N. Schnell, and F. Bevilacqua, A Multimodal Probabilistic Model for Gesture-based Control of Sound Synthesis. In Proceedings of the 21st ACM international conference on Multimedia (MM’13), Barcelona, Spain, 2013.