This Git contains main code and tasks for the TUMO workshop "Reading and computing music with AI", led by Chahan Vidal-Gorène (Calfa) and Baptiste Queuche (Calfa).
Goal: Analyzing and recognizing scanned music scores to generate audio music files.
Objectives:
- Understand how neural networks work and their application to music recognition;
- Discover the main tasks in computer vision and their application to music;
- Learn how to build an AI project, annotate documents and train/evaluate a model.
Technical objectives: learning conda, YOLO, labelstudio, oemer.
Data: A specific focus will be made on Armenian music scores (mainly from Komitas).
Full instructions: see week 1.
Goal:
- Generating audio music files from music scores
- Music Generation using GAN and style transfer
Objectives:
- Understand how generative networks work;
- Understand object classification;
- Training generative networks;
- Building webapp for demo purposes.
Technical objectives: learning streamlit, tensorflow, prismRNN.
Data: A specific focus will be made on Armenian music.
Full instructions: see week 2.
Download final webapp with models running.