
Director, AI And Advanced Technology at Universal Music Group. I am passionate about Tiny Artificial Intelligence enthusiast. Following the first computer music algorithms of pioneer Iannis Xenakis, deep music generative models have emerged and are now considered as strong and innovative tools for artists. I truly believe that the perfect framework isn’t just powerful software instruments, but rather pairing them with hardware to obtain an augmented instrument. This would keep the sensation of touch while usingthe infinite potential of the software inside. With the rise of powerful computers and datasets, deep models are now extremely precise and generate realistic sounds; however, there are consequences due to the size and energy consumption of these models. Conscious of the environmental impact of datacenters on global warming, we need to find a path to combine powerful tools and energy consumption efficiency. To do so, we can rethink the architecture of Deep Learning models to compress them as much as possible without losing precision and efficiency. New compression techniques have emerged and I work on implementing powerful embedded models into tiny computers like Raspberry and small devices like smartphones.
Audio Machine Learning Enthusiast Researcher
IRCAM, France, Paris (Researcher's introduction)
(Host: Institute for Research and Coordination in Acoustics/Music, Graduate School of Informatics)
SONY CSL , France, Paris (Website page)
jeremyuzan7@gmail.com
Resume
Github Page
My Instagram blog on Computer Audio
Audio Machine Learning Enthusiast Researcher
IRCAM, France, Paris (Researcher's introduction)
(Host: Institute for Research and Coordination in Acoustics/Music, Graduate School of Informatics)
SONY CSL , France, Paris (Website page)
jeremyuzan7@gmail.com
Resume
Github Page
My Instagram blog on Computer Audio
Research Interests
- Machine Learning (Generative models, NLP, Computer Vision, Creative AI)
- TinyML (Compressing techniques, Embedded Device, IoT)
- Hearing Aid - Cochlear Implants
- AR/VR and Haptics
- Quantum
Projects

Compressing techniques for audio generative models on embedded device
Compressing techniques for neural networks allows for speeding up inference reducing energy consumption.
With a compressed version of our drum generative model, we consider embedding the pluggin on tiny device like Raspberry,
Jetson Nano, Smartphones .



Variational Autoencoder for MNIST
Implementing a VAE for image reconstruction and generation of new data

Progressive WebAudio
Piano you can play with your computer keyboard and a nice spectrogram... 
Articles
- The Future Of Cochlear Implants with Ultrasounds
Cochlear Implants Research Study
- Some Thinktank/Articles about everything, written by Jeremy Uzan
(ThinkTank)
Music
Contact
Jeremy UzanIRCAM
e-mail: jeremyuzan7@gmail.com
e-mail: jeremy.uzan@atiam.fr
phone FR: +33 6 25 47 52 41
phone USA: +1 917 982 42 18