Controlling a Synth using a Neural Network in SuperCollider
This video demonstrates how to use a neural network to control a synthesizer that has 10 control parameters using just the 2 control parameters of an XY pad with the FluCoMa Toolkit. 0:17 demo 0:24 theory 3:45 begin coding 5:21 FluidDataSet 7:09 FluidBufToKr 8:38 adding data points to FluidDataSet 12:54 saving FluidDataSets to disk 16:41 training the neural network (FluidMLPRegressor) 21:04 saving the state of FluidMLPRegressor to disk 22:27 making predictions with FluidMLPRegressor 26:00 updating the MultiSliderView with the predicted values 28:31 next steps 32:27 triggering predictions on the server using FluidMLPRegressor's .kr method To learn more about FluidMLPRegressor visit: https://learn.flucoma.org/reference/m... https://learn.flucoma.org/learn/mlp-p... https://learn.flucoma.org/learn/mlp-t... Starting Code: https://learn.flucoma.org/examples/re... Complete Code (without MLPRegressor .kr): https://learn.flucoma.org/examples/re... Complete Code (with MLPRegressor .kr): https://learn.flucoma.org/examples/re... The Fluid Corpus Manipulation Toolbox (FluCoMa) is a software extension that enables programmatic sound bank mining with machine listening and machine learning within Max, SuperCollider, and Pure Data. Website: https://www.flucoma.org/ Download: https://www.flucoma.org/download/ Discourse: https://discourse.flucoma.org/ Max: https://cycling74.com/ SuperCollider: https://supercollider.github.io/ Pure Data: https://puredata.info/
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Controlling a Synth using a Neural Network in Max
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