Research Master Internship: New Algorithms for Automated Room Acoustic Diagnosis

Inria; Strasbourg; Hybride; EUR 6 700 - 10 000; Temps partiel

Avantages offerts par ce poste

Partial transport reimbursement

6 months teleworking possibility

Flexible working hours

Professional equipment provided

Résumé du poste

Inria in Strasbourg invites a pre-thesis internship focused on new algorithms for automated room acoustic diagnosis. The work combines acoustics, signal processing and machine learning, with Python programming as a core skill.

The position is based at Cerema in Strasbourg for a duration of about one month. Candidates should hold a Master's level in relevant fields and demonstrate strong Python programming abilities; knowledge of acoustics and PDEs is a plus.

Qualifications

• Master's level in CS, signal processing, acoustics or applied math.
• Excellent Python programming skills; Matlab a plus.
• Knowledge or interest in audio or acoustics is highly desirable; PDEs a plus.

Responsabilités

• Consolidate results from this topic through a master internship.
• Write a scientific article for journal submission based on findings.
• Perform Python programming tasks.
• Carry out bibliographical studies.

Connaissances

Optimization

Signal processing

Machine learning

Deep learning

Python

Acoustics knowledge

PDEs

Master's degree

Formation

Master's degree (CS / signal processing / acoustics / applied math)

Outils

Python

Matlab

Description du poste

Pre-thesis F/M: New Algorithms for Automated Room Acoustic Diagnosis

Level of qualifications required : Graduate degree or equivalent

Fonction : Tempary Research Position

Level of experience : Recently graduated

Context

Location and duration of thecontract

The research work will take place in Strasbourg (France) at Cerema's offices (11 rue Jean Mentelin, 67200 Strasbourg) and are expected to last 1 month.

Assignment

General description of the subject

Noise pollution is the main source of annoyance for the general public and is a major health and social issue. In buildings, the nuisance is often due to the poor acoustic quality of rooms caused by excessive reverberation (canteens, swimming pools, etc.). When it comes to acoustic refurbishment of rooms, proposing a solution requires a good knowledge of the geometric and acoustic characteristics of the existing building. To estimate certain unknown parameters (e.g. absorption of an old ceiling), acoustic engineers rely on in situ measurements of the sound field and on numerical (or analytical) acoustic models for which they calibrate the parameters iteratively in order to recover the measured sound field value. The entire diagnostic process is therefore long, costly and sometimes imprecise, depending on the models used. Given this situation, the development of so-called inverse methods capable of automatically defining the acoustic parameters of interest from the measurement would be a major advance for building acoustics, leading the path to the development of simpler, faster and more reliable tools for acousticians.

This is a difficult non-linear inverse problem. We are planning an innovative, cross-disciplinary approach, with a team of researchers, each with their own expertise in acoustics (Cédric Foy, UMRAE, internship supervisor), signal processing and machine learning (Antoine Deleforge, Inria, internship supervisor; Sylvain Faisan, ICube) and mathematics (Yannick Privat, IRMA). This approach overcomes the existing barrier between the worlds of audio and acoustics. It involves solving the inverse problem using existing measurements and direct acoustic theoretical models and then, depending on the acoustic parameters to be determined, developing new algorithms that combine techniques from signal processing, optimisation and automated deep learning.

In summary, we are attempting to answer the following question:

Is it possible, using punctual measurements of the sound field in a room, combined with partial and approximate knowledge of the room, the sources and the microphones used, to accurately estimate the acoustic and geometric parameters of the room?

[1] C. Foy, A. Deleforge, D. Di Carlo, Mean Absorption Estimation from Room Impulse Responses using Virtually-Supervised Learning, Journal of the Acoustical Society of America (JASA), 2021.

[2] S. Dilungana, S. Faisan, A. Deleforge, C. Foy, Learning based estimation of individual absorption profiles from a single room impulse response with known positions source, sensor and surfaces, Internoise 2021.

[4] S. Dilungana, A. Deleforge, C. Foy, S. Faisan, Geometry-Informed estimation of surface absorption profiles from impulses responses, Eusipco, 30th European Signal Processing Conference, Belgrade, Serbia, 29 Aug. - 2 Sep. 2022.

[7] T. Sprunck, Y. Privat, C. Foy, A. Deleforge, Room Shape Reconstruction Using Acoustic Super-Resolution, 24th International Congress on Acoustics (ICA), Gyeongju, Korea, 24-28 Oct, 2022.

[8] T. Sprunck, Y. Privat, C. Foy, A. Deleforge, Gridless 3D Recovery if Images Sources from Room Impulse Responses, IEEE Signal Processing Letters, 2022.

Main activities

• Consolidating previous results obtrained on this topic through a master internship
• Writing of a scientific article to submit in a journal based on these findings
• Python programming
• Bibliographical studies

Skills

• Optimisation methods, Signal processing, Machine learning, Deep Learning
• Excellent level of Python programming (Matlab would also be a plus)
• Knowledge, experience or particular interest in audio or acoustics are highly desirable. Knowledge of partial differential equations (PDE) is a plus.
• Master's level (computer science, signal processing, acoustics, applied math...)

Benefits package

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Theme/Domain :Optimization, machine learning and statistical methods
  Scientific computing(BAP E)

About Inria

Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.