1 citations · 1 across the 6 of their papers we have counts for
6 papers
VaSAB: The variable size adaptive information bottleneck for disentanglement on speech and singing voice
Frederik Bous, Axel Roebel
The information bottleneck auto-encoder is a tool for disentanglement commonly used for voice transformation. The successful disentanglement relies on the right choice of bottlenec…
Analysis and transformations of voice level in singing voice
Frederik Bous, Axel Roebel
We introduce a neural auto-encoder that transforms the musical dynamic in recordings of singing voice via changes in voice level. Since most recordings of singing voice are not ann…
Towards Universal Neural Vocoding with a Multi-band Excited WaveNet
Axel Roebel, Frederik Bous
This paper introduces the Multi-Band Excited WaveNet a neural vocoder for speaking and singing voices. It aims to advance the state of the art towards an universal neural vocoder,…
Voice Reenactment with F0 and timing constraints and adversarial learning of conversions
Frederik Bous, Laurent Benaroya, Nicolas Obin +1
This paper introduces voice reenactement as the task of voice conversion (VC) in which the expressivity of the source speaker is preserved during conversion while the identity of a…
Semi-supervised learning of glottal pulse positions in a neural analysis-synthesis framework
Frederik Bous, Luc Ardaillon, Axel Roebel
This article investigates into recently emerging approaches that use deep neural networks for the estimation of glottal closure instants (GCI). We build upon our previous approach…
Analysing Deep Learning-Spectral Envelope Prediction Methods for Singing Synthesis
Frederik Bous, Axel Roebel
We conduct an investigation on various hyper-parameters regarding neural networks used to generate spectral envelopes for singing synthesis. Two perceptive tests, where the first c…