Methodology for 3D sound synthesis of directional acoustic sources by higher-order ambisonics

Autor: Thorner, Philippe, Bavu, Eric, Doc, Jean-Baptiste, Langrenne, Christophe

This paper presents the 3D soundfield synthesis of the pressure field radiated by directional acoustic sources using both the multimodal method and higher-order ambisonics (HOA). Ambisonics is a technique for encoding and reproducing measured or mode

Externí odkaz: http://arxiv.org/abs/2409.12506

Configurable EBEN: Extreme Bandwidth Extension Network to enhance body-conducted speech capture

Autor: Hauret, Julien, Joubaud, Thomas, Zimpfer, Véronique, Bavu, Éric

Publikováno v: IEEE/ACM Transactions on Audio, Speech, and Language Processing (2023 - Volume: 31) - pp. 3499 - 3512

This paper presents a configurable version of Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial Network (GAN) designed to improve audio captured with body-conduction microphones. We show that although these microphones significantl

Externí odkaz: http://arxiv.org/abs/2303.10008

BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data

Autor: Pujol, Hadrien, Bavu, Éric, Garcia, Alexandre

Publikováno v: J. Acoust. Soc. Am. 149 (6), June 2021

Sound sources localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has allowed to drastically improve performances for machine hearin

Externí odkaz: http://arxiv.org/abs/2104.13347

Rotationnal and translational waves in a bowed string

Autor: Bavu, Eric, Yew, Manfred, Placais, Pierre-Yves, Smith, John, Wolfe, Joe

We measure and compare the rotational and transverse velocity of a bowed string. When bowed by an experienced player, the torsional motion is phase-locked to the transverse waves, producing highly periodic motion. The spectrum of the torsional motion

Externí odkaz: http://arxiv.org/abs/physics/0505058

Deeplomatics: A deep-learning based multimodal approach for aerial drone detection and localization

Autor: Bavu, Éric, Pujol, Hadrien, Garcia, Alexandre, Langrenne, Christophe, Hengy, Sébastien, Rassy, Oussama, Thome, Nicolas, Karmim, Yannis, Schertzer, Stéphane, Matwyschuk, Alexis

Publikováno v: QUIET DRONES 2022 SECOND INTERNATIONAL SYMPOSIUM ON NOISE FROM UASs/UAVs and eVTOLs SYMPOSIUM PROCEEDINGS
QUIET DRONES Second International e-Symposium on UAV/UAS Noise
QUIET DRONES Second International e-Symposium on UAV/UAS Noise, INCE/Europe; CidB, Jun 2022, Paris, France

International audience; Protection against illicit drone intrusions is a matter of great concern. The relative stealthy nature of UAVs makes their detection difficult. To address this issue, the Deeplomatics project provides a multimodal and modular

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1398::2bbdd8b852264600b7a86b5eee0ac8c7
https://hal.science/hal-03707115/document