34 citations · 108 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
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…
Spectrogram Inpainting for Interactive Generation of Instrument Sounds
Théis Bazin, Gaëtan Hadjeres, Philippe Esling +1
Modern approaches to sound synthesis using deep neural networks are hard to control, especially when fine-grained conditioning information is not available, hindering their adoptio…
Timbre latent space: exploration and creative aspects
Antoine Caillon, Adrien Bitton, Brice Gatinet +1
Recent studies show the ability of unsupervised models to learn invertible audio representations using Auto-Encoders. They enable high-quality sound synthesis but a limited control…
Using musical relationships between chord labels in automatic chord extraction tasks
Tristan Carsault, Jérôme Nika, Philippe Esling
Recent researches on Automatic Chord Extraction (ACE) have focused on the improvement of models based on machine learning. However, most models still fail to take into account the…
Neural Drum Machine : An Interactive System for Real-time Synthesis of Drum Sounds
Cyran Aouameur, Philippe Esling, Gaëtan Hadjeres
In this work, we introduce a system for real-time generation of drum sounds. This system is composed of two parts: a generative model for drum sounds together with a Max4Live plugi…