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* Principal Components Analysis | * Principal Components Analysis | ||
* Singular Value Decomposition | * Singular Value Decomposition | ||
| − | * Non-negative Matrix Factorization | + | * Non-negative Matrix Factorization and sparse decomposition |
* multi-dimensionnal M to N mapping based on examples | * multi-dimensionnal M to N mapping based on examples | ||
* Multi-dimensioannal autocorrelation | * Multi-dimensioannal autocorrelation | ||
Revision as of 19:42, 22 November 2006
Mapping is Not Music
about
MnM is a set of Max/MSP externals based on FTM providing a unified framework for various techniques of classification, recognition and mapping for motion capture data, sound and music.
features
- Hidden Markov Models
- Principal Components Analysis
- Singular Value Decomposition
- 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)
papers
download PDF of NIME 2005 paper on MnM
The MnM object set is released within the FTM distribution..