18 citations · 57 across the 8 of their papers we have counts for
8 papers
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…
Neural Granular Sound Synthesis
Adrien Bitton, Philippe Esling, Tatsuya Harada
Granular sound synthesis is a popular audio generation technique based on rearranging sequences of small waveform windows. In order to control the synthesis, all grains in a given…
Ultra-light deep MIR by trimming lottery tickets
Philippe Esling, Theis Bazin, Adrien Bitton +2
Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, t…
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…
Vector-Quantized Timbre Representation
Adrien Bitton, Philippe Esling, Tatsuya Harada
Timbre is a set of perceptual attributes that identifies different types of sound sources. Although its definition is usually elusive, it can be seen from a signal processing viewp…
Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders
Adrien Bitton, Philippe Esling, Antoine Caillon +1
Generative models have thrived in computer vision, enabling unprecedented image processes. Yet the results in audio remain less advanced. Our project targets real-time sound synthe…