1 code implementation • 28 Oct 2022 • Louis Bahrman, Marina Krémé, Paul Magron, Antoine Deleforge
Signal inpainting is the task of restoring degraded or missing samples in a signal.
2 code implementations • 28 Jun 2022 • Ondřej Mokrý, Paul Magron, Thomas Oberlin, Cédric Févotte
First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain.
no code implementations • 20 Apr 2022 • Paul Magron, Cédric Févotte
We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model's expressive power.
no code implementations • 29 Mar 2022 • Mostafa Sadeghi, Paul Magron
Structuring the latent space in probabilistic deep generative models, e. g., variational autoencoders (VAEs), is important to yield more expressive models and interpretable representations, and to avoid overfitting.
1 code implementation • 24 Feb 2021 • Paul Magron, Cédric Févotte
In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart.
no code implementations • 20 Oct 2020 • Paul Magron, Cédric Févotte
These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recommend novel songs without listening history.
no code implementations • 1 Oct 2020 • Pierre-Hugo Vial, Paul Magron, Thomas Oberlin, Cédric Févotte
Therefore, we formulate PR as a new minimization problem involving Bregman divergences.
Sound
1 code implementation • Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 2019 • Konstantinos Drossos, Shayan Gharib, Paul Magron, Tuomas Virtanen
On the contrary, with our method there is a decrease of 4% at F1 score and an increase of 7% at ER for the TUT-SED Synthetic 2016 dataset.
1 code implementation • 24 Apr 2019 • Konstantinos Drossos, Paul Magron, Tuomas Virtanen
A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions.