PhD student
Team : SMA
Arrival date : 04/01/2021
    Sorbonne Université - LIP6
    Boîte courrier 169
    Couloir 25-26, Étage 4, Bureau 420
    4 place Jussieu
    75252 PARIS CEDEX 05

Tel: +33 1 44 27 88 40, Nathan.Fradet (at)


Co-supervision : BRIOT Jean-Pierre

Deep Learning for Conditioned Multi-track Symbolic Music Generation

We study in this thesis the application of artificial intelligence, especially deep learning, to music generation. The main interest is to put this discipline at the service of musicians in order to help them in their creative process, by exploiting their ideas to transcribe, extend or modify them according to their desires. A first axis consists in the creation of algorithmic and mathematical models and methods pushing the limits of existing solutions in the literature. The second axis focuses on the adaptation and encoding of musical data into representations responding to the constraints of the associated models and methods while offering as many controls as possible in the creative process that will influence the results according to the user's wishes.

2021 Publications

  • 2021
    • N. Fradet, J.‑P. Briot, F. Chhel, A. El Fallah‑Seghrouchni, N. Gutowski : “MidiTok: A Python Package for MIDI File Tokenization”, Extended Abstracts for the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference, Online, United States (2021)