Creating midi input datasets with pure data - part 1

One of the drivers of my KicKRaTT (linktree in profile) project is examining the prediction choices AI makes from generated synthetic midi input data. To predict an original song that can be genre identified over that which would be deemed experimental. ARBOREAL was constructed from the predictions made by GPT & LSTM models trained on datasets created by two algorithms generating midi input data in the key of C. KicKRaTT's next AI composition will be composed by algorithms generating different genre type midi input in the key of A major. There are many ways that you can generate midi input data for AI synthetic dataset constructions. The algorithms that I use to generate the midi input for the AI training datasets are constructed in pure data. This video demonstrates a patch that has pitch adjustment, adjustable conditional statements & will can generate midi for any length of time. Generating synthetic midi for AI datasets could also be done in Max, audiomulch, software synthesizer modules & vsts. There are numerous ways to generate midi or quantize other sources to midi. Using unorthodox sources for generating original midi data opens up many new genres of music available to predicting.

If you going to teach your computer to compose, use original data, & it will predict with characteristic not plagiaristic. Training your LLMs with datasets composed of known works of music ends in recycled music.

This midi generation video is performed using the MS GS Wavetable available on all Windows desktops. At this point in this process, I am only focused on sculpting the algorithms design to generate midi in a specific genre type or to achieve a desired groove.