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== about == | == 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. | [[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 | ||
| + | * multi-dimensionnal M to N mapping based on examples | ||
| + | * Multi-dimensioannal autocorrelation | ||
| + | * Matrix/Vector Statistics (min, max, mean, std, histogram, mahalanobis distance) | ||
== papers == | == papers == | ||
Revision as of 18:42, 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
- 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..