- Paper Digest: Recent Papers on AI for Music
- https://github.com/affige/genmusic_demo_list
- Types of NNs and tokenization
- https://github.com/affige/DeepMIR
- Ross Greer, Shlomo Dubnov. Deep and Shallow: Machine Learning in Music and Audio
- Handbook of Artificial Intelligence for Music
LLMs are GPT-4 and such. They know natural languages. In future, they should also know everything about music theory, be able to make Roman numeral analysis, analysis of forms. They should convert between ABC notation, kern, MusicXML. And they should be able to compose coherent long pieces in a prompted style.
Articles that excite me:
- https://github.com/zharry29/drums-with-llm
- https://arxiv.org/search/cs?searchtype=author&query=Donahue%2C+C
- https://www.semanticscholar.org/paper/LakhNES%3A-Improving-Multi-instrumental-Music-with-Donahue-Mao/24a70db0bbb5f486126477e32a6a44ab917a4b11#citing-papers
- MidiBERT, MusicBERT
- https://github.com/tripathiarpan20/midiformers
- https://github.com/asigalov61/Los-Angeles-Music-Composer
- https://github.com/gudgud96/magenta-in-pytorch and his ISMIR reviews in his blog on DDSP
- https://epub.jku.at/obvulihs/download/pdf/8503579?originalFilename=true
- https://liuhaumin.github.io/LeadsheetArrangement/
- https://github.com/archinetai/audio-ai-timeline
- A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions SHULEI JI, JING LUO, and XINYU YANG
- 🎥 Music + AI Reading Group
- https://arxiv.org/html/1706.08675
- https://rafaelramirez8.wixsite.com/mml2020
- https://github.com/jongwook