ml.lib: Robust, Cross-platform, Open-source Machine Learning for Max and Pure Data

Jamie Bullock, and Ali Momeni

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract:

This paper documents the development of ml.lib: a set of open-source tools designed for employing a wide range of machine learning techniques within two popular real-time programming environments, namely Max and Pure Data. ml.lib is a cross-platform, lightweight wrapper around Nick Gillian's Gesture Recognition Toolkit, a C++ library that includes a wide range of data processing and machine learning techniques. ml.lib adapts these techniques for real-time use within popular data-flow IDEs, allowing instrument designers and performers to integrate robust learning, classification and mapping approaches within their existing workflows. ml.lib has been carefully de-signed to allow users to experiment with and incorporate ma-chine learning techniques within an interactive arts context with minimal prior knowledge. A simple, logical and consistent, scalable interface has been provided across over sixteen exter-nals in order to maximize learnability and discoverability. A focus on portability and maintainability has enabled ml.lib to support a range of computing architectures---including ARM---and operating systems such as Mac OS, GNU/Linux and Win-dows, making it the most comprehensive machine learning implementation available for Max and Pure Data.

Citation:

Jamie Bullock, and Ali Momeni. 2015. ml.lib: Robust, Cross-platform, Open-source Machine Learning for Max and Pure Data. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1179038

BibTeX Entry:

  @inproceedings{amomenib2015,
 abstract = {This paper documents the development of ml.lib: a set of open-source tools designed for employing a wide range of machine learning techniques within two popular real-time programming environments, namely Max and Pure Data. ml.lib is a cross-platform, lightweight wrapper around Nick Gillian's Gesture Recognition Toolkit, a C++ library that includes a wide range of data processing and machine learning techniques. ml.lib adapts these techniques for real-time use within popular data-flow IDEs, allowing instrument designers and performers to integrate robust learning, classification and mapping approaches within their existing workflows. ml.lib has been carefully de-signed to allow users to experiment with and incorporate ma-chine learning techniques within an interactive arts context with minimal prior knowledge. A simple, logical and consistent, scalable interface has been provided across over sixteen exter-nals in order to maximize learnability and discoverability. A focus on portability and maintainability has enabled ml.lib to support a range of computing architectures---including ARM---and operating systems such as Mac OS, GNU/Linux and Win-dows, making it the most comprehensive machine learning implementation available for Max and Pure Data.},
 address = {Baton Rouge, Louisiana, USA},
 author = {Jamie Bullock and Ali Momeni},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.5281/zenodo.1179038},
 editor = {Edgar Berdahl and Jesse Allison},
 issn = {2220-4806},
 month = {May},
 pages = {265--270},
 publisher = {Louisiana State University},
 title = {ml.lib: Robust, Cross-platform, Open-source Machine Learning for Max and Pure Data},
 url = {http://www.nime.org/proceedings/2015/nime2015_201.pdf},
 year = {2015}
}