Raphaël Leiba

Postdoctoral researcher in Acoustics

Acoustics researcher in LVA - INSA Lyon (France), working on the application of CLEAN-T approach to sound source localisations on aircrafts.

Between 2019 and 2022 I was working on SoundBounce at Lios with Trinity College Dublin in the Fluids, Acoustics & Vibration group

Prior to this Dublin experience, I was a Postdoctoral Teaching Fellows in the Faculty of Sciences & Engineering of Sorbonne University (Paris, France)
I was working in both ∂'Alembert Institute and STMS-IRCAM. It mainly focused on acoustic imaging, machine learning and psychoacoustics.

Research topics

Acoustic imaging &
sound source separation

Using beamforming and Clean-T algorithms coupled with large array of microphones (between 128 and 256) - Megamicros and MAMBO projects

Sound Insulating Materials

Development of a soundproofing composite material: SoundBounce

Composite material property measurements

Audio signal classification

Using supervised and unsupervised Machine Learning algorithms.
Examples : Road traffic monitoring, construction site monitoring, etc.


Noise annoyance estimation (perceptual experience) and modelling.
Psychoacoustic indices modelling implementations in Python.


The impact of surface roughness on an additively manufactured acoustic material: An experimental and numerical investigation

Journal of Sound and Vibration, Nov., 2022.

Agnieszka Ciochon, John Kennedy, Raphaël Leiba, Lara Flanagan, Mark Culleton


In BeBec. Berlin (2022)

Leiba R, Leclere Q, Julliard E

Development of acoustic “meta-liners” providing sub-wavelength absorption

International Journal of Aeroacoustics, Sept., 2020.

Lara Flanagan, David Heaphy, John Kennedy, Raphaël Leiba, Henry Rice

Acoustical Classification of the Urban Road Traffic with Large Arrays of Microphones

Acta Acustica united with Acustica, Nov., 2019.

Leiba R, Ollivier F, Marchiano R, Misdariis N, Marchal J and Challande P

Conception d'un outil de diagnostic de la gêne sonore en milieu urbain

Thesis at: Université Pierre & Marie Curie - Paris 6

Leiba R -- December, 2017

Large array of microphones for the automatic recognition of acoustic sources in urban environment

In InterNoise. Hong-Kong (2017)

Leiba R, Ollivier F, Marchal J, Misdariis N and Marchiano R

"Urban acoustic imaging : from measurement to the soundscape perception evaluation"


Leiba R, Ollivier F, Marchiano R, Misdariis N and Marchal J

Utilisation d'antennes à grand nombre de microphones pour la reconnaissance automatique de sources sonores en environnement urbain

Acoustique & Technique., Nov., 2017.

Leiba R, Ollivier F, Marchal J, Misdariis N and Marchiano R

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Curriculum Vitae

  • Laboratoire Vibrations Acoustique - INSA Lyon
Current Work:
  • Application of CLEAN-T approach for noise source localisation on aircrafts;
  • Design of a new microphone array for Airbus future flyover test campaigns, suitable both for DAMAS-MS and CLEAN-T;
  • Determine the limitations of CLEAN-T algorithm.
  • Soundbounce development, Lios
  • Trinity College Dublin
Current Work:
  • Understanding the underlying physical mechanisms responsible for SoundBounce performances;
  • Improving SoundBounce performances;
  • Increasing the Technological Readiness Level (TRL) of SoundBounce;
  • ESA project: Feasibility study on using SoundBounce in the Fairing Acoustic Protections of future launchers.
  • ∂'Alembert institute, UMR 7190, SU-CNRS
  • STMS Ircam-CNRS-SU
  • Improvements in classifying road traffic vehicles (Machine Learning);
  • Psychoacoustic tests for noise annoyance modelling;
  • Psychoacoustic indices model development (open-source project in progress).
  • ∂'Alembert institute, UMR 7190, SU-CNRS
  • STMS Ircam-CNRS-SU

Noise annoyance diagnostic tool conception in urban areas


Noise, especially road traffic noise, is cited by many studies as a source of major societal concern. So far, public responses are based only on energy quantification of sound exposure, often by measuring or estimating LA or Lden, and sound-level reduction related decision are taken. Nevertheless, psychoacoustic studies have shown that the sound level explains only a small part of the perceived noise annoyance. It is interesting to have more information about the source of noise and not to reduce the information to its sound level. In this thesis a tool is proposed for estimating the noise annoyance induced by each road vehicle using its audio signal and noise annoyance models. To do so, the audio signal of the vehicle is isolated by using inverse methods, large microphones arrays and image processing to obtain its trajectory. The knowledge of the trajectory and of the signal allows the vehicle to be classified by a machine learning method according to Morel et al. taxonomy. Once its category obtained, the specific annoyance of the vehicle is estimated thanks to a noise annoyance model using psychoacoustic and energetic indices. This allows the estimation of specific noise annoyance for each vehicle within the road traffic. The application of this method is made during a measurement day on a large Parisian artery.

[ pdf ]

Full CV

(Jun 2021)

MSc projects