36 citations · 223 across the 30 of their papers we have counts for
5 papers · 1 filter
RAVE: A variational autoencoder for fast and high-quality neural audio synthesis
Antoine Caillon, Philippe Esling
Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks. However, as raw waveform modelling remains an i…
An Exploratory Study on Perceptual Spaces of the Singing Voice
Brendan O'Connor, Simon Dixon, George Fazekas
Sixty participants provided dissimilarity ratings between various singing techniques. Multidimensional scaling, class averaging and clustering techniques were used to analyse timbr…
Signal-domain representation of symbolic music for learning embedding spaces
Mathieu Prang, Philippe Esling
A key aspect of machine learning models lies in their ability to learn efficient intermediate features. However, the input representation plays a crucial role in this process, and…
Energy Consumption of Deep Generative Audio Models
Constance Douwes, Philippe Esling, Jean-Pierre Briot
In most scientific domains, the deep learning community has largely focused on the quality of deep generative models, resulting in highly accurate and successful solutions. However…
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…