2 citations · 2 across the 4 of their papers we have counts for
4 papers
Keep what you need : extracting efficient subnetworks from large audio representation models
David Genova, Philippe Esling, Tom Hurlin
Recently, research on audio foundation models has witnessed notable advances, as illustrated by the ever improving results on complex downstream tasks. Subsequently, those pretrain…
Combining audio control and style transfer using latent diffusion
Nils Demerlé, Philippe Esling, Guillaume Doras +1
Deep generative models are now able to synthesize high-quality audio signals, shifting the critical aspect in their development from audio quality to control capabilities. Although…
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
Giovanni Bindi, Nils Demerlé, Rodrigo Diaz +18
Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of…
Continuous descriptor-based control for deep audio synthesis
Ninon Devis, Nils Demerlé, Sarah Nabi +2
Despite significant advances in deep models for music generation, the use of these techniques remains restricted to expert users. Before being democratized among musicians, generat…