Description of the project
Starting date: January or February 2020
Duration: 12 months renewable 6 months
Application deadline date: October 18th 2019
Decision announcement date: November 4th 2019
Description of the project
In the ADEME IMOTEP project context (Innovation MOTEur Propre - see here at the bottom of the
web page for a presentation of the partners of this project), this PostDoc subject is proposed by the
Image team of the Hubert Curien laboratory in collaboration with the “Laboratory of Tribology and
Dynamic of Systems” (LTDS) of the “Ecole Centrale” in Lyon and IREIS (Institut de Recherche En
Ingénierie des Surfaces), the research and development department of HEF group.
The work will also be carried out in collaboration with a PhD student whose PhD thesis began since
December 2018, dealing with the denoising or source separation of cyclostationary signals based on
deep learning methodologies. Cyclostationary signals are random signals whose statistical
characteristics have periodicities [GN06]. Signals of this type are encountered in many fields such as
telecommunications, radar signals, acoustic signals, mechanical signals related to rotations in engines,
etc. Note that it is also possible to model, with this kind of approaches, random patterns that may
have spatial periodicities : cyclostationary signals are not limited to signals indexed by the time as
soon as the nature of the support is different (spatial, spatio-temporal, …).
Whatever the field, the problems addressed by the scientific community concerning these signals are
often related to the denoising and the separation of a mixture of several signals (or sources). One can
notice that the denoising task can also be seen as the separation of the useful signal from the noise
[BAM97, AMX01]. It is also possible to consider improving the considered methods by detecting (or
recognizing) the type of signals involved in the mixture, which then makes it possible to choose the
most appropriate models.
Recently, new methods of denoising and source separation, exploiting the current advances in
machine learning based on deep learning (LSTM-RNN [KY17], deep NN [KK18], Autoencoders
[GP17], ...) have emerged for speech or audio signal processing. Our main objective is to extend this
work to cyclostationary signals and apply the developed methods to friction measurement signals
(tribological measurements) acquired on test benches for the engines of the future.
The PostDoc work will consist in studying the existing mechanical models adapted for the tribological
signals acquired in the different experimental contexts of the IMOTEP project in order to propose
appropriate models. These models will provide methods for the synthesis of the signals appearing in
the test benches and will thus improve deep-learning-based denoising (or source separation)
methodologies developed by the PhD student. At last, the objective will be to estimate from the
experimental signals, some physical parameters characterizing friction mechanisms.
We are looking for a motivated student with a PhD degree in mechanics with an experience in signal
processing or a PhD degree in applied mathematics with an experience in mechanics and/or signal
processing. A good background in software development (algorithmic, Matlab/Octave/Scilab or
Python, ...) is expected. Knowledge in machine learning would also be appreciated.
Net salary: 2200 euros. Teaching activities are eventually possible.
Your application should include the following documents:
• Letter of intent
• Scientific CV
• List of publications
• Names of Referees (at least 2)
• Olivier Alata <firstname.lastname@example.org>
• Fabien Momey <email@example.com>
• Christophe Ducottet <firstname.lastname@example.org>
[AMX01] K. Abed-Meraim, Y. Xiang, J.H. Manton, Y. Hua, "Blind source-separation using second-order
cyclostationary statistics" IEEE Transactions on Signal Processing, 49(4), 2001.
[BAM97] A. Belachrouni, K. Abed-Meraim, J.-F. Cardoso, E. Moulines, "A Blind Source Separation
Technique Using Second-Order Statistics" IEEE Transactions on Signal Processing, 45(2), 1997.
[GN06] W. A. Gardner, Antonio Napolitano, Luigi Paura, "Cyclostationarity: Half a century of research",
Volume 86, Issue 4, April 2006, Pages 639-697.
[GP17] E. M. Grais, M. D. Plumbley, "Single Channel Audio Source Separation using Convolutional
Denoising Autoencoders" In: 5th IEEE Global Conference on Signal and Information Processing
(GlobalSIP2017), 14 - 16 November 2017, Montreal, Canada.
[KK18] M. Kulin, T. Kazaz, I. Moerman, E. De Poorter, "End-to-end learning from spectrum data: A
deep learning approach for wireless signal identification in spectrum monitoring applications", IEEE
Access, 2018. https://arxiv.org/abs/1712.03987
[KY17] M. Kolbæk, D. Yu, Z.-H. Tan, J. Jensen, "Joint Separation and Denoising of Noisy Multi-talker
Speech using Recurrent Neural Networks and Permutation Invariant Training" In: IEEE Int. Workshop
on Machine Learning for Signal Processing, 2017, Tokyo, Japan. https://arxiv.org/abs/1708.09588
(c) GdR 720 ISIS - CNRS - 2011-2019.