ml.lib: Robust, Cross-platform, Open-source Machine Learning for Max and Pure Data
- Jamie Bullock, Birmingham Conservatoire
- Ali Momeni, Carnegie Mellon University
- 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.
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