SONORA - The Spatial Dynamics of Room Acoustics
From 01-05-2018 to 31-10-2023
Description
The SONORA project aims to increase the general understanding of how complex sound scenes are impacted by the spatial dynamics of room acoustics. This knowledge is crucial in the design of signal processing algorithms for audio acquisition and reproduction problems in real-life situations, where moving sound sources and observers interact with room acoustics in a complicated manner.
A major part of the project will be devoted to the development of novel room acoustics models and to the unification of existing models. The room acoustics models developed in this project will be data-driven models with a physically motivated structure, and are expected to fill the existing gap between geometric and wave-based models. This will be achieved by formulating existing and novel models in a dictionary-based mathematical framework and introducing a new concept coined as the equivalent boundary model, aimed at relaxing the prior knowledge required on the physical room boundary.
A second part of the project will focus on the development of a protocol for measuring spatiotemporal sound fields. This protocol will be rooted in a novel sound field sampling theory which exploits the spatial sparsity of sound sources by invoking the compressed sensing paradigm.
Thirdly, novel signal processing algorithms capable of handling spatiotemporal sound fields will be designed. By employing recent advances in large-scale optimization and multidimensional scaling, fast and matrix-free algorithms will be obtained that do not require prior knowledge of the sound scene geometry.
The SONORA research results are anticipated to have a notable impact in various audio acquisition and reproduction problems, including acoustic signal enhancement, audio analysis, room inference, virtual acoustics, and spatial audio reproduction. These problems have many applications in speech, audio, and hearing technology, hence a significant benefit for industry and for technology end users is expected in the long run.
Team
- Toon van Waterschoot, Promoter
- Hannes Rosseel, Team member
- Elisa Tengan Pires de Souza, Team member
- Niccolò Antonello, Team member
- Thomas Dietzen, Team member
- Brian Fitzpatrick, Team member
- Martin Jälmby, Team member
Financing
Funding: ERC - European Research Council
Program/Grant Type: ERC Con - ERC Consolidator Grant
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.