Creative Coding
Unsupervised Machine Learning via FluCoMa In Max
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8. Exploring One-Dimensional Sound Organization with UMAP and MFCC Analysis
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.
Learning outcomes
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
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 content
Unsupervised Machine Learning via FluCoMa In Max
9 videos
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Unsupervised Machine Learning via FluCoMa In Max
9 videos
1. Introduction
Checking access...2. Supervised vs Unsupervised Learning in Machine Learning
Checking access...3. Principle Component Analysis for Dimensionality Reduction in Sound Analysis
Checking access...4. Understanding the Uniform Manifold Approximation and Projection Algorithm
Checking access...5. The Importance of Scaling in Data Analysis
Checking access...6. Exploring KMeans Clustering in Audio Analysis
Checking access...7. Understanding the Jonker-Volgenant Algorithm (aka Grid)
Checking access...8. Exploring One-Dimensional Sound Organization with UMAP and MFCC Analysis
Checking access...9. Questions
Checking access...
Instructors
Ted Moore
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.