Machine Learning in Cycling '74's Max

Link to Presentation Slides

Repository for Max patches that accompany my Seattle Max Meetup presentation, March 25th, 2023

Summary

In the accompanying talk and demonstration I introduce what Machine Learning (ML) is using simple linear regression as a first example, then expand into other examples of unsupervised and supervised learning. Next I show how ML may be applied to musical tasks using Max, specifically using externals for Max to accomplish the tasks of 1) mapping between a gesture and synthesizer presets in real-time, 2) mapping between an input sound and a corpus of audio in real-time using concatenative synthesis, and 3) using neural networks trained on corpuses of audio to morph the timbre of a sound at the sample level in real-time.

Assumptions

• You're interested in incorporating ML into your art-making practice
• You're not deeply knowledgeable about ML (yet!)
• You're comfortable programming in Max
• You're aware of how to install packages (File -> Show Package Manager)

ML in Max Resources (Externals Packages)

Package	Developer	Features	Source	Release Date	Development Status	Install Notes
MuBu + PiPo	ISMM Team @ IRCAM	many different ML algorithms, focused on audio feature extraction and sound organization, somewhat steep learning curve	Closed Source	2010	In Development	via Max Package Manager
nn~	Acids Team @ IRCAM	deep learning neural network model in Max	Open Source	2022	In Development	via this link if on Mac, build from source on Windows, works with RAVE trained models
FluCoMa	FluCoMa Team	many different ML algorithms, well organized, some example patches assume knowledge of ML	Closed Source	2018	Just Stopped Development	via Max Package Manager
ml.star	Benjamin D. Smith	a number of different ML algorithms, with nice example patches, although some dated, a good entry point	Closed Source	2011	Not in Development	via Max Package Manager
ml-lib	IRL Labs	a direct implenentation of the Gesture Recognition Toolkit by Nick Gillian in Max, very barebones, data type agnostic, data as lists	Open Source	2013	In Development	via Max Package Manager

Included Patches

Learning Patches

• linearregression.maxpat - demonstrates simple linear regression
• supervised.maxpat - demonstrates supervised learning using the support-vector machine algorithm, requires ml-lib externals
• unsupervised.maxpat - demonstrates unsupervised learning using the Fuzzy C-means clustering algorithm, requires ml.* externals

ML in Max Patches

• shapetosoundmapping.maxpat - using ML (supervised learning) to learn mappings between a shape and a parametric space, requires ml-lib externals
• concantenativesynthesis.maxpat - using ML (unsupervised learning) to map between one corpus of audio and another at the grain level, requires MUBU externals
• deeplearning.maxpat - using Deep ML (neural network) to map between an input sound and a trained sound model in real-time, requires nn~ external

TODO

• Incorporate https://github.com/testcase/mlmat
• Incorporate other ML Max tools
• Link to my own ML-related work (Murmurator, EcoBobbles, others)

RNBO Extras

RNBO is a new Max feature that allows you to write RNBO patches (similar to Max patches) that can then be exported to five different targets, three of which I'll demo.:

• C++ Source
• JS Web Export - see this Template for a great starting point.
• Raspberry Pi - see here for a step-by-step guide
• Audio Plug-in - click here for a step-by-step guide
• Max External

Remember: RNBO is NOT Max. Most notably, you can't have symbols (e.g. (parameter $1)) and you also can't incorporate Max externals (as of this writing).