In the ADEME IMOTEP project context (Innovation MOTEur Propre - see hereat 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:
[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.