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[[MnM]] is a set of Max/MSP externals based on [[FTM | FTMlib]] providing a unified framework for various techniques of classification, recognition and mapping for motion capture data, sound and music. | [[MnM]] is a set of Max/MSP externals based on [[FTM | FTMlib]] providing a unified framework for various techniques of classification, recognition and mapping for motion capture data, sound and music. | ||
Revision as of 16:21, 9 December 2006
Mapping is not Music
MnM is a set of Max/MSP externals based on FTMlib providing a unified framework for various techniques of classification, recognition and mapping for motion capture data, sound and music.
Features currently implemented in MnM include:
- Hidden Markov Models
- Principal Components Analysis
- Singular Value Decomposition, LU and QR decompositions
- Non-negative Matrix Factorization and sparse decomposition
- multi-dimensionnal M to N mapping based on examples
- Multi-dimensioannal autocorrelation
- Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance)
The MnM package is released within the FTM distributions.
Publications
- NIME 2006 paper on MnM (PDF)