From ftm
Remymuller (talk | contribs) |
(→Mapping is not Music) |
||
| (19 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
| − | == | + | == Mapping is not Music == |
| − | [[MnM]] is a set of Max/MSP externals based on [[ | + | [[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 | + | * Hidden Markov Models, see for example [[Gesture Follower]] |
* Principal Components Analysis | * Principal Components Analysis | ||
* Singular Value Decomposition, LU and QR decompositions | * Singular Value Decomposition, LU and QR decompositions | ||
| Line 11: | Line 11: | ||
* Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | * Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | ||
| − | + | The MnM package is released within the [[Download | FTM distributions]]. | |
| − | [ | ||
| − | + | == Publications == | |
| − | + | {{:MnM Publications}} | |
Latest revision as of 23:36, 10 February 2008
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, see for example Gesture Follower
- 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)