36 citations · 223 across the 30 of their papers we have counts for
30 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…
Unsupervised Composable Representations for Audio
Giovanni Bindi, Philippe Esling
Current generative models are able to generate high-quality artefacts but have been shown to struggle with compositional reasoning, which can be defined as the ability to generate…
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…
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…
SingSong: Generating musical accompaniments from singing
Chris Donahue, Antoine Caillon, Adam Roberts +8
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create mus…
Creative divergent synthesis with generative models
Axel Chemla--Romeu-Santos, Philippe Esling
Machine learning approaches now achieve impressive generation capabilities in numerous domains such as image, audio or video. However, most training \& evaluation frameworks revolv…