Publications associées à Structured Sparsity Models for Reverberant Speech Separation

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Equivalent black carbon concentration measured with an aethalometer AE33 during the Arctic Ocean 2018 expedition

Julia Schmale, Andrea Baccarini, Benjamin Jérémy Laurent Heutte

Equivalent black carbon (eBC) mass concentration was measured with a commercial aethalometer (model AE33, Magee Scientific, Berkeley, USA). Measurements were performed onboard of the Swedish icebreaker (I/B) Oden from August to September 2018 as part of th ...

EPFL Infoscience2024

Sparse Autoencoders for Speech Modeling and Recognition

Selen Hande Kabil

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

EPFL2023

Aerosol optical absorption coefficients at seven wavelengths in 10 min resolution measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Benjamin Jérémy Laurent Heutte, Ivo Fabio Beck, Lubna Dada

This dataset contains aerosol optical absorption coefficients at seven different wavelengths (babs(λ)), averaged to 10 min time resolution, measured during the year-long MOSAiC expedition from October 2019 to September 2020. The measurements were performed ...

Zenodo2023

Towards learning-based denoising of light fields

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In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

The inductive bias of deep learning: Connecting weights and functions

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Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

EPFL2023

Massively parallel nodal discontinous Galerkin finite element method simulator for room acoustics

Jan Sickmann Hesthaven

We present a massively parallel and scalable nodal discontinuous Galerkin finite element method (DGFEM) solver for the time-domain linearized acoustic wave equations. The solver is implemented using the libParanumal finite element framework with extensions ...

Sage Publications Ltd2023

Novel Methods For Detection And Analysis Of Atypical Aspects In Speech

Julian David Fritsch

Atypical aspects in speech concern speech that deviates from what is commonly considered normal or healthy. In this thesis, we propose novel methods for detection and analysis of these aspects, e.g. to monitor the temporary state of a speaker, diseases tha ...

EPFL2023

A model of non-Maxwellian electron distribution function for the analysis of ECE data in JET discharges

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Recent experiments performed in JET at high level of plasma heating, in preparation of, and during the DT campaign have shown significant discrepancies between electron temperature measurements by Thomson Scattering (TS) and Electron Cyclotron Emission (EC ...

E D P SCIENCES2023