A software sampler programmed in SuperCollider playing a tSNE-Scatterplot.
Visit the project on my website to get general information and a video-demo of my sampler.
This projects builds upon a series of projects, where I created a sample library of vocal interjections and extracted their F0-data. This data would later serve different projects in art and artistic research, this being one of them.
My application was strongly inspired by -and dependent on ml4a's AudioTSNEViewer.
A more elaborate documentation will follow shortly!
Meanwhile (and if you can read German), please refer to doc/Vocal-Sampler_Projektdokumentation.PDF and/or study the code to understand what's going on here
(and why there's little use in a detailed how-to).
I also considered providing my very own sample library in order to provide easy reproducibility, but I really can't do that yet, as it's just so very private.
You basically cannot directly recreate this project without creating your own sample library and putting it into data/Wave/*.
Audio-files found in this directory have to follow my very secret file-naming conventions.
Once that's done, use ml4a's python-script to create your own version of data/audiotsne.json.
After that you can run tsne2dict.scd on that file to complete it by the nesseccary data (Emotion & Expression),
thus creating your own version of data/vocalmap.json.
With a method of your choice, analyze these audio-files for F0-data and put that into data/CSV/*, mirroring the folder structure and naming conventions of data/Wave/*.
Now you should be able to run vocal_sampler.scd.
- upload an example folder for
data/CSV/*anddata/Wave/* - change paths of these files to relative - not absolute.
- upload the documentation for preceding projects