From ftm
Remymuller (talk | contribs) |
|||
| Line 1: | Line 1: | ||
| − | |||
[[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. | [[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 | * Hidden Markov Models | ||
* Principal Components Analysis | * Principal Components Analysis | ||
| Line 11: | Line 10: | ||
* Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | * Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | ||
| − | == | + | == Papers == |
[http://recherche.ircam.fr/equipes/temps-reel/articles/mnm.nime05.pdf download PDF of NIME 2005 paper on MnM] | [http://recherche.ircam.fr/equipes/temps-reel/articles/mnm.nime05.pdf download PDF of NIME 2005 paper on MnM] | ||
---- | ---- | ||
The [[MnM]] object set is released within the [[FTM]] distribution.. | The [[MnM]] object set is released within the [[FTM]] distribution.. | ||
Revision as of 18:20, 1 December 2006
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, 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)
Papers
download PDF of NIME 2005 paper on MnM
The MnM object set is released within the FTM distribution..