Neural Networks via FluCoMa In Max
Taught by: Ted Moore
Using Machine Learning for composition and sound design
The Fluid Corpus Manipulation project (FluCoMa) provides novel machine learning tools for use in the digital composition process. The MLPRegressor is a neural network that can be used to perform regression.
What is Regression?
In Machine Learning, regression can be thought of as a mapping from one space to another where each space can be any number of dimensions. By providing input and output data as DataSets, the neural network is trained using supervised learning to predict output data points based on input data points. This gives a vast array of creative possibilities for composition, sound design and performance.
What to expect in this workshop?
In this workshop, Ted Moore from the FluCoMa project guides you through an exploration of some of the creative possibilities available via Neural Networks with their Max Package. Basic experience of FluCoMa is advised before joining this workshop course. For example, it is recommended that you have taken the free on-demand workshop Using Machine Learning Creatively via FluCoMa In Max.
Level
What you'll learn
- Train neural networks to perform musical tasks
- Use training and testing data to validate trained models
- Troubleshoot the training process by adjusting neural network parameters
- Combine different types of input and output data
Course content
- Presentation slides (63 pages)
- Flucoma patches
- 1. Introduction _ What is FluCoMa_
- 2. Plan _ Outline
- 3. Classification
- 4. Multilayer-Perceptron
- 5. A Musical Motivation for Classification
- 6. Supervised vs. Unsupervised Learning
- 7. Training a Classifier
- 8. Feed-forward and Back-propagation
- 9. Classification Patch
- 10. The _error_ _Training fluid.mlpclassifier~
- 11. Making Predictions with fluid.mlpclassifier
- 12. Validation with Training & Testing Data
- 13. Saving a Trained Neural Network for Later Use
- 14. Doing Classification with fluid.mlpregressor~
- 15. Artistic Use of Classification
- 17. Automated Dataset Creation and Validation
- 18. Neural Network Parameters (Object Attributes)
- 19. Hiddenlayers
- 20. Activation and Outputactivation
- 21. Learnrate
- 22. Maxiter
- 23. Batchsize
- 24. Validation
- 25. Overfitting
- 26. Momentum
- 27. Q&A on Parameters
- 28. Neural Network Regression with Audio Descriptors
- 29. Musical Example
- 30. Training fluid.mlpregressor~
- 31. Wavetable Autoencoder
- 32. @tapin and @tapout
- 33. Final Q&A
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Add to cartRequirements
- 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
Who is this course for
About the workshop leader
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.
