About

I am an audio signal processing researcher. From September 2026 I join the RITMO Centre at the University of Oslo as a Postdoctoral Research Fellow on the GROOVE project, leading the computational analysis of groove-based rhythms. Before that I completed a PhD in audio signal processing at KU Leuven, on distributed processing for wireless acoustic sensor networks.

Doctoral research

My doctoral work at KU Leuven (STADIUS, ESAT), part of the SOUNDS European Training Network, focused on distributed audio signal processing for wireless acoustic sensor networks. I developed topology-independent algorithms for ad-hoc microphone networks, using distributed multichannel Wiener filtering for node-specific signal estimation, aimed at robust speech enhancement where centralized processing is impractical. Much of the work dealt with real system constraints: sampling rate offsets, synchronization, and limited communication between nodes. I also spent a 10-month research stay at Fraunhofer IDMT and the University of Oldenburg across 2022 and 2023.

Applied and industrial work

Before my PhD I led an applied acoustics project with ROCKWOOL International and KU Leuven: finite-element modelling of reverberation rooms and design optimization for the ISO 354 revision on sound absorption measurement. Earlier, at Oktogrid (Copenhagen), I built DSP fault-detection algorithms for electrical transformers using MEMS microphone arrays, work that led to an issued US patent.

Education

M.Sc. in Engineering Acoustics from the Technical University of Denmark (DTU), a Diplôme d'ingénieur from Mines Nancy, and the French Classes Préparatoires in Grenoble.

What's next

GROOVE (2026–2029) moves my work from speech and acoustics toward rhythm and music information retrieval. I lead the computational component: source separation on commercial recordings, multi-dimensional feature extraction, and machine-learning classification of groove patterns across funk, soul, reggae, samba, and hip-hop. It brings my signal processing background into computational musicology, close to a long-held passion for music.