From ftm
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[[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 currently implemented in MnM include: | |
* Hidden Markov Models | * Hidden Markov Models | ||
* Principal Components Analysis | * Principal Components Analysis | ||
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* Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | * Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | ||
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---- | ---- | ||
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| + | == Publications == | ||
| + | {{:MnM publication}} | ||
Revision as of 16:18, 9 December 2006
Mapping is not 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 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)