9 citations · 26 across the 6 of their papers we have counts for
8 papers
Sound texture synthesis using convolutional neural networks
Hugo Caracalla, Axel Roebel
The following article introduces a new parametric synthesis algorithm for sound textures inspired by existing methods used for visual textures. Using a 2D Convolutional Neural Netw…
Data Augmentation for Drum Transcription with Convolutional Neural Networks
Celine Jacques, Axel Roebel
A recurrent issue in deep learning is the scarcity of data, in particular precisely annotated data. Few publicly available databases are correctly annotated and generating correct…
Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation
Alice Cohen-Hadria, Axel Roebel, Geoffroy Peeters
State-of-the-art singing voice separation is based on deep learning making use of CNN structures with skip connections (like U-net model, Wave-U-Net model, or MSDENSELSTM). A key t…
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…
An evaluation framework for event detection using a morphological model of acoustic scenes
Mathieu Lagrange, Grégoire Lafay, Mathias Rossignol +2
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its pote…
Sound Analysis and Synthesis Adaptive in Time and Two Frequency Bands
Marco Liuni, Peter Balazs, Axel Röbel
We present an algorithm for sound analysis and resynthesis with local automatic adaptation of time-frequency resolution. There exists several algorithms allowing to adapt the analy…