34 citations · 91 across the 10 of their papers we have counts for
11 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…
Creativity in the era of artificial intelligence
Philippe Esling, Ninon Devis
Creativity is a deeply debated topic, as this concept is arguably quintessential to our humanity. Across different epochs, it has been infused with an extensive variety of meanings…
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…
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…