Music creation with deep learning techniques

Jean-Pierre Briot Université Sorbonne, Paris

COGNITIVE INFORMATICS SEMINAR UQÀM ISC DIC CRIA

Thursday, 10:30 am September 15, 2022

Zoom: https://uqam.zoom.us/j/88481835073

Abstract: A growing application area for the current wave of deep learning (the return of artificial neural networks on steroids) is the generation of creative content, notably the case of music (and also images and text). The motivation is in using machine learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This talk will survey some recent achievements in deep-learning-based music generation, using recent and dedicated generative architectures such as VAE, GAN and Transformer, analyzing principles, successes as well as challenges, including the limits of automated generation versus providing assistance to human musicians.

Jean-Pierre Briot is a senior researcher (research director) in computer science at LIP6, joint computer science research lab of CNRS (Centre National de la Recherche Scientifique) and Sorbonne Université in Paris, France. He is also permanent visiting professor at PUC-Rio in Rio de Janeiro, Brazil. His general research interests are the design of intelligent adaptive and cooperative software, at the crossroads of artificial intelligence, distributed systems and software engineering, with various applications in the internet of things, decision support systems and computer music. His current interest is the use of AI techniques (notably deep learning-based) within music creation processes. He is the principal author of a recent reference book on deep learning techniques for music generation

Briot, J. P., Hadjeres, G., & Pachet, F. D. (2020). Deep learning techniques for music generation (Vol. 1). Heidelberg: Springer. https://link.springer.com/book/10.1007/978-3-319-70163-9https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-3-319-70163-9&data=05%7C01%7Charnad%40ecs.soton.ac.uk%7C4d1eb4139bd14fe34dfe08da85c0365b%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637969357838311509%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lnDHUsvtL99uSu2lCrLx2rLy241BtfMbWgQKGR3h8Wk%3D&reserved=0

For more details (including access to publications): http://webia.lip6.fr/~briot/cv/https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwebia.lip6.fr%2F~briot%2Fcv%2F&data=05%7C01%7Charnad%40ecs.soton.ac.uk%7C4d1eb4139bd14fe34dfe08da85c0365b%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637969357838311509%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=jglPJ3%2FIlMDlWLXxGSKFCZdN%2FHFnsIev9EMszGFgIUo%3D&reserved=0

Briot, J. P. (2021). From artificial neural networks to deep learning for music generation: history, concepts and trends. Neural Computing and Applications, 33(1), 39-65. https://hal.sorbonne-universite.fr/hal-02539189v3/file/nn4music-hal-v3.pdf

Briot, J. P. (2019). Apprentissage profond et génération de musique, Hors série Intelligence artificielle, Tangente - L'aventure mathématique, (68):30-37, September 2019. https://webia.lip6.fr/~briot/cv/apgm-2019https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwebia.lip6.fr%2F~briot%2Fcv%2Fapgm-2019&data=05%7C01%7Charnad%40ecs.soton.ac.uk%7C32bd97f487734efa0ff308da85c12738%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C637969361888860776%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=jcT5s%2Bwchg0IcjSW8yewQioHoN0DKHVfEJKss%2FgnLKw%3D&reserved=0