Javier Hurlé
Researcher at Sony CSL Music Team
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolJavier Hurlé is an Associate Researcher at Sony CSL in Paris with nine years of experience at the intersection of deep learning and music technology, holding a PhD in Music Information Processing. He has advanced generative audio synthesis—most notably developing DrumGAN, which was integrated as the AI engine in Steinberg’s Backbone 1.5—and builds research-driven tools for AI-assisted music production. His work spans GANs, audio synthesis, MIR, and lightweight on-device models, with a track record of turning academic research into production-ready plugins and demos. Javier combines hands-on studio experience as a freelance audio engineer with rigorous research training from Télécom Paris and Universitat Pompeu Fabra, enabling both technical depth and musical sensibility. Colleagues know him for bridging scientific communication and engineering, regularly publishing, presenting, and maintaining complex training frameworks in Python. He’s motivated by reshaping the musical landscape through creative, controllable generative models that empower producers rather than replace them.
9 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy - PhD, Music Information Processing, Doctor of Philosophy - PhD, Music Information Processing at Télécom ParisMáster, Sound and Music Computing, Máster, Sound and Music Computing at Universitat Pompeu FabraBA Sound and Image Engineering, Telecommunications, BA Sound and Image Engineering, Telecommunications at Universidad Politécnica de Madrid
Spanish, English, French
Github Skills (10)
generative-adversarial-network10
audio-representation8
audio6
machine-learning6
google6
ai6
jekyll6
mir5
convolution4
music1
Programming languages (4)
Github contributions (5)
SonyCSLParis/DrumGAN
Mar 2020 - Feb 2022
Synthesis of Drum Sounds With Perceptual Timbral Conditioning Using Generative Adversarial Networks
Contributions:147 commits, 6 pushes, 1 branch in 1 year 11 months
generative-adversarial-network
SonyCSLParis/Comparing-Representations-for-Audio-Synthesis-using-GANs
Mar 2020 - Mar 2020
This repo contains code for comparing audio representation sin the task of audio synthesis wth Generative Adversarial Networks (GAN)
Contributions:25 commits, 9 pushes, 1 branch in 12 days
audioaudio-representationgenerative-adversarial-network
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