Application of Remote Error Sensor Techniques for Active Noise Control in Cabins, at the Ears of a Machine Operator

1. Context

Active Noise Control (ANC) relies on generating waves in phase opposition to undesirable noise to attenuate it. This technique is commonly used in closed systems (small volumes compared to the wavelengths to be treated), such as noise-canceling headphones. This requires the user to wear a device to protect them from noise. The recent development of surface actuators has made this technology promising for improving acoustic comfort in other spaces, particularly in vehicle and machine cabins. However, its application in such environments (modal effects) presents significant technical challenges. Noise sources can be multiple, and the interaction between waves can be complex to understand, even when limited to a certain volume around the operator's head.
An innovative approach in this field is the use of virtual microphones, which predict the sound field at specific locations from remote measurements. This aims to create noise attenuation zones without the need to install multiple sensors. For example, Convolutional Neural Networks (CNN) [1,2] can be used to analyze acoustic data and predict the sound field at targeted positions. Other methods [3], such as the additional filter method and the remote microphone method, involve using inverse optimization algorithms. These algorithms adjust acoustic models based on measured data to estimate the sound field at the virtual microphone location, enabling targeted noise attenuation.
The implementation of virtual microphone techniques combined with the use of acoustic antennas offers an interesting opportunity to reduce noise around the heads of machine operators. This would not only improve driving comfort but also ensure better intelligibility of useful acoustic signals, such as safety alerts, radio communications, or sounds associated with machine performance.

2. Thesis Objectives

The main objective of this thesis is to design and implement an active noise control system using virtual microphone techniques and/or acoustic antennas to reduce noise around the head and ears of a machine operator, specifically at the entrance of the auditory canal. This system aims to create a localized quiet zone, thereby improving the operator's acoustic comfort while avoiding the clutter associated with installing physical sensors too close to their head.
Traditionally, virtual sensors are positioned at a relatively short distance from the target area (a few centimeters). The challenge of this thesis is to test solutions that increase this distance, extending it to several tens of centimeters, to reduce clutter around the operator's head and thus offer them greater freedom of movement while maintaining system effectiveness.

3. Proposed Approach and Description of Planned Work

After a literature review phase complementing the one proposed in the appendix, the thesis will be conducted in two main phases: a numerical phase and an experimental phase.
**Numerical Phase:**
- **Modeling and Simulation:** Development of numerical models to simulate the sound field in a machine cabin. Use of acoustic simulation techniques to evaluate the effectiveness of virtual microphones and acoustic antennas.
- **Control Algorithms:** Design and implementation of adaptive active noise control algorithms based on virtual microphone techniques. Exploration of the use of neural networks to improve the accuracy of sound predictions.
**Experimental Phase:**
- **Prototyping:** Construction of prototypes of acoustic antennas and virtual microphone systems. Integration of these prototypes into a machine cabin for laboratory and, if possible, real-world testing.
- **Testing and Validation:** Conducting experimental tests to evaluate the performance of the active noise control system. Analysis of results to validate numerical models and control algorithms.

4. Organizational Framework of the Thesis

Supervision: This thesis work will be carried out within the MEGA doctoral school and CETIM, the technical center for the mechanical industries.
Funding Duration: 3 years.
Location: Fluid Mechanics and Acoustics Laboratory (LMFA UMR 5509). Occasional travel (at least 1 to 2 weeks every 6 months) is planned to CETIM in Senlis or Beauvais.

5. Desired Profile

Candidates should ideally have the following skills:
- Holder of an Engineering or Master's degree in Mechanics, Physics, Electronics, or Signal Processing
- Knowledge of Vibroacoustics, Acoustics, and Signal Processing
- Experience in numerical simulation and programming in Matlab/Simulink/Python (C/C++ would be a plus)
- Autonomy, curiosity, initiative, and ability to draw analogies
- English (ability to write and present in English with prior preparation)

6. Contact

Interested candidates are invited to send their application to the following contacts:
- Pierre Lecomte: pierre.lecomte@ec-lyon.fr, and
- Michel Besombes: michel.besombes@cetim.fr.
Their application should include:
- A detailed CV with a cover letter
- A letter of recommendation or contact details of a referee
- Their academic records from engineering or master's studies
- A writing sample (internship report, scientific articles)

7. Summary Bibliography: "Virtual Microphone" & "Remote Sensing" for ANC in Cabin

1. **2024**, Juhyung Kim: **Remote Microphone Sound-Field Virtual Sensing Method Using Neural Network for Active Noise Control System**, Master thesis, Purdue Univ.

2. **2023**, Christian Antoñanzas, Miguel Ferrer, Maria Diego, Alberto P. Gonzalez: **Remote Microphone Technique for Active Noise Control over Distributed Networks** (DOI: **10.1109/taslp.2023.3264600**)

3. **2021**, Jin Zhang, S. Elliott, J. Cheer: **Robust performance of virtual sensing methods for active noise control**, May 2021 Mechanical Systems and Signal Processing 152(9):107453 DOI:10.1016/j.ymssp.2020.107453

4. **2017**, W. Jung, S.J. Elliott, J. Cheer: **Local Active Sound Control Using the Remote Microphone Technique and Head-Tracking for Tonal and Broadband Noise Sources**, ICSV24, London, 23-27 July 2017.

5. **2016**, Prasanga N. Samarasinghe, Wen Zhang, Thushara D. Abhayapala: **Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars**, IEEE Signal Processing Magazine (Volume: 33, Issue: 6, November 2016), DOI: 10.1109/MSP.2016.2601942.

6. **2012**, Yoshinobu Kajikawa, Woon-Seng Gan, and Sen M. Kuo: **Recent advances on active noise control: open issues and innovative applications**, APSIPA Transactions on Signal and Information Processing / Volume 1 / December 2012 / e3, DOI: 10.1017/ATSIP.

7. **2010**, J-H Thomas, V Grulier, S Paillasseur, J-C Pascal, J-C Le Roux: **Real-time near-field acoustic holography for continuously visualizing nonstationary acoustic fields**, December 2010, The Journal of the Acoustical Society of America 128(6):3554-67, DOI: 10.1121/1.3504656

8. **2009**, Moreau, D.J.; Ghan, J.; Cazzolato, B.S.; Zander, A.C.: **Active noise control in a pure tone diffuse sound field using virtual sensing**. J. Acoust. Soc. Am., 125 (6), 3742–3755. DOI: 10.1121/1.3123404

9. **2007**, Jacek Dmochowski, Rafik A. Goubran: **Decoupled Beamforming and Noise Cancellation**, March 2007 IEEE Transactions on Instrumentation and Measurement 56(1):80 - 88, DOI: 10.1109/TIM.2006.887196

10. **2003**, Munn, J.M.; Cazzolato, B.S.; Kestell, C.D.; Hansen, C.H.: **Virtual error sensing for active noise control in a one-dimensional waveguide: performance prediction versus measurement**. J. Acoust. Soc. Am., 113 (1), 35–38. DOI: 10.1121/1.1523386

11. **2003**, Kuo, S.M.; Gan, W.S.; Kalluri, S.: **Virtual sensor algorithms for active noise control systems**, in Proc. 2003 IEEE Int. Symp. on Intelligent Signal Processing and Communication Systems, December 2003, 714–719.

12. **2002**, Cazzolato, B.: **An adaptive LMS virtual microphone**, in Proc. Active 2002, July 2002, 105–116.

13. **2002**, Haneda, Y.: **Active noise control with a virtual microphone based on common acoustical pole and residue model**, in Proc. ICASSP, vol. 2, 2002, 1877–1880, DOI: 10.1109/ICASSP.2002.5744993

14. **2001**, Kestell, C.D.; Cazzolato, B.S.; Hansen, C.H.: **Active noise control in a free field with virtual sensors**. J. Acoust. Soc. Am., 109(1), 232–243, DOI: 10.1121/1.1326950.

15. **1997**, Bonito, G.; Elliott, S.J.; Boucher, C.C.: **Generation of zones of quiet using a virtual microphone arrangement**. J. Acoust. Soc. Am., 101 (6), 3498–3516, DOI:10.1121/1.418357.

Diplômes : Master 2 ou équivalent

Expérience : Novice (avec formation initiale solide) à expérimenté

Connaissances : Acoustique/Traitement du signal

Compétences opérationnelles : Programmation, Modélisation, Expérimentation

Compétences comportementales Travail en autonomie

Contexte de travail / Environnement de travail_______________________________

85% du temps au LMFA, 15% au CETIM de Senlis (séjours réguliers).

Travail au laboratoire avec un bureau et accès à une salle d’écoute instrumentée pour les expérimentations.

Au CETIM conditions similaires avec accès à une cabine de tracteur instrumentée pour les expérimentations