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(features)
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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 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 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..