34 citations · 79 across the 7 of their papers we have counts for
7 papers
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…
Challenges in creative generative models for music: a divergence maximization perspective
Axel Chemla--Romeu-Santos, Philippe Esling
The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising…
Diet deep generative audio models with structured lottery
Philippe Esling, Ninon Devis, Adrien Bitton +3
Deep learning models have provided extremely successful solutions in most audio application fields. However, the high accuracy of these models comes at the expense of a tremendous…
Cross-modal variational inference for bijective signal-symbol translation
Axel Chemla--Romeu-Santos, Stavros Ntalampiras, Philippe Esling +2
Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain. This complex t…
Universal audio synthesizer control with normalizing flows
Philippe Esling, Naotake Masuda, Adrien Bardet +2
The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern s…
Modulated Variational auto-Encoders for many-to-many musical timbre transfer
Adrien Bitton, Philippe Esling, Axel Chemla-Romeu-Santos
Generative models have been successfully applied to image style transfer and domain translation. However, there is still a wide gap in the quality of results when learning such tas…
Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics
Philippe Esling, Axel Chemla--Romeu-Santos, Adrien Bitton
Timbre spaces have been used in music perception to study the perceptual relationships between instruments based on dissimilarity ratings. However, these spaces do not generalize t…