责任导师 : Amal EL FALLAH SEGHROUCHNI

助理责任导师 : BRIOT Jean-Pierre

Deep Learning for Symbolic Music Modeling

Symbolic music modeling (SMM) represents the tasks performed by Deep Learning models on the symbolic music modality, among which are music generation or music information retrieval. SMM is often handled with sequential models that process data as sequences of discrete elements called tokens. This thesis studies how symbolic music can be tokenized, and what are the impacts of the different ways to do it impact models performances and efficiency. Current challenges include the lack of software to perform this step, poor model efficiency and inexpressive tokens. We address these challenges by:

1. developing a complete, flexible and easy to use software library allowing to tokenize symbolic music;
2. analyzing the impact of various tokenization strategies on model performances;
3. increasing the performance and efficiency of models by leveraging large music vocabularies with the use of byte pair encoding;
4. building one of the first large-scale model for symbolic music generation.

2021-2024 刊物

• 2024
  • N. Fradet : “Deep Learning for Symbolic Music Modeling”, 博士论文, 答辩 2024-3-14, 责任导师 El fallah seghrouchni, Amal, 助理责任导师 : Briot, Jean-Pierre (2024)
• 2023
  • N. Fradet, N. Gutowski, F. Chhel, J.‑P. Briot : “Byte Pair Encoding for Symbolic Music”, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore, Singapore, pp. 2001-2020, (Association for Computational Linguistics) (2023)
  • N. Fradet, N. Gutowski, F. Chhel, J.‑P. Briot : “Impact of time and note duration tokenizations on deep learning symbolic music modeling”, Proceedings of the 24^th Conference of the International Society for Music Information Retrieval (ISMIR) 2023, Milano, Italy, pp. 89-97, (ISMIR), (ISBN: 978-1-7327299-3-3) (2023)
• 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 22^nd International Society for Music Information Retrieval Conference, Online, United States (2021)