36 citations · 223 across the 30 of their papers we have counts for
4 papers · 1 filter
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…
Streamable Neural Audio Synthesis With Non-Causal Convolutions
Antoine Caillon, Philippe Esling
Deep learning models are mostly used in an offline inference fashion. However, this strongly limits the use of these models inside audio generation setups, as most creative workflo…
HEAR: Holistic Evaluation of Audio Representations
Joseph Turian, Jordie Shier, Humair Raj Khan +20
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to dev…