17 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Télécom ParisFR4 papers
- Laboratoire Traitement et Communication de l’InformationFR3 papers
- Aix-Marseille UniversitéFR1 paper
- American UniversityUS1 paper
- Ariel UniversityIL1 paper
- Astronomical Institute of the Slovak Academy of SciencesSK1 paper
- Astronomical ObservatoryRS1 paper
- Astronomy and SpaceAU1 paper
- Bard CollegeUS1 paper
- Bar-Ilan UniversityIL1 paper
- Barry UniversityUS1 paper
- California State University SystemUS1 paper
18 papers
Taylor-SWFT: fast discrete Statistical Wave Field Theory using Taylor expansion for late reverberation Work under review
Marius Rodrigues, Louis Lalay, Roland Badeau +2
Dynamic room acoustic simulation aims to render the acoustic effects of an environment in real time while accounting for potentially moving sources and receivers. In this context,…
Importance Sampling Optimization with Laplace Principle
Radu-Alexandru Dragomir, François Portier, Victor Priser
Grid search and random search are widely used techniques for hyperparameter tuning in machine learning, especially when gradient information is unavailable. In these methods, a fin…
Is Phase Really Needed for Weakly-Supervised Dereverberation ?
Marius Rodrigues, Louis Bahrman, Roland Badeau +1
In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating…
IS : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering
Clémentine Berger, Paraskevas Stamatiadis, Roland Badeau +1
We are interested in audio systems capable of performing a differentiated processing of stationary backgrounds and isolated acoustic events within an acoustic scene, whether for ap…
Consensus-Based Optimization Beyond Finite-Time Analysis
Pascal Bianchi, Radu-Alexandru Dragomir, Victor Priser
We analyze a zeroth-order particle algorithm for the global optimization of a non-convex function, focusing on a variant of Consensus-Based Optimization (CBO) with small but fixed…
GW230814: investigation of a loud gravitational-wave signal observed with a single detector
The LIGO Scientific Collaboration, The Virgo Collaboration, The Kagra Collaboration +1783
GW230814, detected by the LIGO Livingston observatory with a signal-to-noise ratio of 42.4, represents the loudest gravitational-wave signal in the GWTC-4.0 catalog. Its source is…
Of All StrIPEs: Investigating Structure-informed Positional Encoding for Efficient Music Generation
Manvi Agarwal, Changhong Wang, Gael Richard
While music remains a challenging domain for generative models like Transformers, a two-pronged approach has recently proved successful: inserting musically-relevant structural inf…
F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation
Manvi Agarwal, Changhong Wang, Gael Richard
While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to l…
A Hybrid Model for Weakly-Supervised Speech Dereverberation
Louis Bahrman, Mathieu Fontaine, Gael Richard
This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms re…
Speech dereverberation constrained on room impulse response characteristics
Louis Bahrman, Mathieu Fontaine, Jonathan Le Roux +1
Single-channel speech dereverberation aims at extracting a dry speech signal from a recording affected by the acoustic reflections in a room. However, most current deep learning-ba…
Long-time asymptotics of noisy SVGD outside the population limit
Victor Priser, Pascal Bianchi, Adil Salim
Stein Variational Gradient Descent (SVGD) is a widely used sampling algorithm that has been successfully applied in several areas of Machine Learning. SVGD operates by iteratively…
Model-Based Deep Learning for Music Information Research
Gael Richard, Vincent Lostanlen, Yi-Hsuan Yang +1
In this article, we investigate the notion of model-based deep learning in the realm of music information research (MIR). Loosely speaking, we refer to the term model-based deep le…
A lightweight dual-stage framework for personalized speech enhancement based on DeepFilterNet2
Thomas Serre, Mathieu Fontaine, Éric Benhaim +2
Isolating the desired speaker's voice amidst multiplespeakers in a noisy acoustic context is a challenging task. Per-sonalized speech enhancement (PSE) endeavours to achievethis by…
A fully differentiable model for unsupervised singing voice separation
Gael Richard, Pierre Chouteau, Bernardo Torres
A novel model was recently proposed by Schulze-Forster et al. in [1] for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existin…
Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport
Bernardo Torres, Geoffroy Peeters, Gaël Richard
In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a cha…
Nyström Kernel Mean Embeddings
Antoine Chatalic, Nicolas Schreuder, Alessandro Rudi +1
Kernel mean embeddings are a powerful tool to represent probability distributions over arbitrary spaces as single points in a Hilbert space. Yet, the cost of computing and storing…
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias +4
Finding the optimal hyperparameters of a model can be cast as a bilevel optimization problem, typically solved using zero-order techniques. In this work we study first-order method…
Fair Regression with Wasserstein Barycenters
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri +2
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of t…