Unsupervised Machine Learning via FluCoMa In Max
Membership plan: Going Deeper | Topics: Sound Design
Course overview
- The Fluid Corpus Manipulation project (FluCoMa) provides novel machine learning tools for digital composition.
- Unsupervised Machine Learning refers to finding patterns in data.
- FluCoMa objects analyze audio to find similar/different sounds, plot complex analyses in 2D space, and organize sound slices.
- This provides a vast array of creative possibilities for composition, sound design, and performance.
- Ted Moore from FluCoMa will guide you through the creative possibilities of Unsupervised Learning with FluCoMa Max Package.
- Basic experience of FluCoMa is advised before joining the workshop.
For example, it is strongly recommended that you have taken the free on-demand workshop Using Machine Learning Creatively via FluCoMa In Max.
What you'll learn
- Find patterns in unorganized or unknown data
- Plot data in 2 dimensional space (or other dimensions) by adjusting object parameters
- Cluster sound slices to identify sounds that are similar and different from others
- Gridify data points for interfacing with controllers
Who is this course for?
- Sound designers looking to implement unsupervised machine learning tools via FluCoMa in Max
- Musicians looking to explore the creative possibilities offered with unsupervised machine learning tools via FluCoMa in Max
Course content
- 1. Introduction
- 2. Supervised vs Unsupervised Learning in Machine Learning
- 3. Principle Component Analysis for Dimensionality Reduction in Sound Analysis
- 4. Understanding the Uniform Manifold Approximation and Projection Algorithm
- 5. The Importance of Scaling in Data Analysis
- 6. Exploring KMeans Clustering in Audio Analysis
- 7. Understanding the Jonker-Volgenant Algorithm (aka Grid)
- 8. Exploring One-Dimensional Sound Organization with UMAP and MFCC Analysis
- 9. Questions
- Patches and Slides
Requirements
- A computer and internet connection
- Access to a copy of Max 8 (i.e. trial or full license)
- Install of the free FluCoMa Max package
Course schedule
Meet your instructor
Ted Moore (he / him) is a composer, improviser, and intermedia artist. He holds a PhD in Music Composition from the University of Chicago and recently served as a Research Fellow in Creative Coding at the University of Huddersfield, investigating the creative affordances of machine learning and data science algorithms as part of the FluCoMa project.​ His work focuses on fusing the sonic, visual, physical, and acoustic aspects of performance and sound, often through the integration of technology.
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