Plourde, Éric

Professeur, Faculté de génie
FAC. GÉNIE Électrique et informatique

Coordonnées

Courriel


819-821-8000, poste 63255


Site Web

Diplômes

(2011) Postdoctorat (Postdoctorat). Massachusetts Institute of Technology / Harvard Medical School / Massachusetts General Hospital.

(2009) Bayesian short-time spectral amplitude estimators for single-channel speech enhancement. Doctorat (Ph.D.). McGill University.

(2004) Diplôme (Attestation en formation à l’enseignement postsecondaire). Université de Montréal.

(2003) Mesures in vivo de l’impédance du myocarde durant l’ischémie et détermination de ses résistivités électriques intracellulaires et extracellulaire. Maîtrise avec mémoire (M.Sc.A.). École Polytechnique de Montréal.

(2003) Baccalauréat (B.Ing.). École Polytechnique de Montréal.

(1995) Diplôme (Diplôme des sciences de la santé). Université de Moncton.

Expérience académique

(2016) Professeur agrégé. Université de Sherbrooke.

(2011-2016) Professeur adjoint. Université de Sherbrooke.

Présentation

Sujets de recherche

Transmission et traitement des signaux numériques, Modélisation neuronale, Système auditif.

Disciplines de recherche

Génie électrique et génie électronique, Génie biomédical et génie biochimique.

Mots-clés

Traitement de signaux neuronaux, Modélisation de signaux neuronaux, Interfaces cerveau-machine, Implants auditifs, Neurosciences computationnelles, Décodage neuronal, Traitement de la parole, Rehaussement de la parole, Traitement statistique de signaux, Estimation bayésienne.

Intérêts de recherche

• Traitement de signaux neuronaux (modélisation et caractérisation de décharges neuronales, codage pour implants auditifs cérébraux) • Instrumentation biomédicale (implants auditifs) • Traitement de la parole (rehaussement de la parole, codage de la parole, traitement de la parole inspiré de recherches sur le système auditif) • Traitement statistique de signaux (estimation bayésienne, processus ponctuel, modèle linéaire généralisé)

Langues parlées et écrites

Anglais, Français

Prix et distinctions

  • Prix du mérite Jacques Bazinet 2014 - Génie informatique. Association générale des étudiants de génie de l'Université de Sherbrooke. (Prix / Récompense).
  • Prix du mérite Jacques Bazinet 2016 - Génie informatique. Association générale des étudiants de génie de l'Université de Sherbrooke. (Prix / Récompense).

Publications

Articles de revue

  • *Moinnereau MA, Rouat J, Whittingstall K, Plourde E. (2019). A frequency-band coupling model of EEG signals can capture input audio information. Hearing Research, (Article soumis).
  • *Moinnereau MA, Whittingstall K, Plourde E. (2019). Electroencephalogram (EEG) recordings obtained when simultaneously presenting audio stimulations. IEEE DataPort, DOI: 10.21227/e90n-s, (Article publié).
  • *Hosseini M, Rodriguez G, Guo H, Lim HH, Plourde E. (2019). The activity of inferior colliculus neurons in the presence of noisy vocalizations is dependant on the input noise type. Journal of Neuroscience, (Article soumis).
  • *Moinnereau MA, *Brienne T, Brodeur S, Rouat J, Whittingstall K, Plourde E. (2018). Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir. arXiv.org, 5 p. (Article publié).
  • *Chung H, Badeau R, Plourde E, Champagne B. (2018). Training and compensation of class-conditioned NMF bases for speech enhancement. Neurocomputing, 284, 107-118. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2017). Regularized non-negative matrix factorization with Gaussian mixtures and masking effects for speech enhancement. Speech Communication, 87, 18-30. (Article publié).
  • Adeli M, Rouat J, Wood S, Molotchnikoff S, Plourde E. (2016). A flexible bio-inspired hierarchical model for analyzing musical timbre. IEEE/ACM Transactions on Audio, Speech and Language Processing, 24(5), 875-889. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2016). Discriminative training of NMF model based on class probabilities for speech enhancement. IEEE Signal Processing Letters, 23(4), 502-506. (Article publié).
  • Parchami M, Zhu WP, Champagne B, Plourde E. (2016). Recent developments in speech enhancement in the short-time Fourier transform domain. IEEE Circuits and Systems Magazine, 16(3), 45-77. (Article publié).
  • Parchami M, Zhu W-P, Champagne B, Plourde E. (2015). Bayesian STSA estimation using masking properties and generalized Gamma prior for speech enhancement. EURASIP Journal on Advances in Signal Processing, (87), 21 p. (Article publié).
  • Plourde E, Delgutte B, Brown E N. (2011). A point process model for auditory neurons considering both their intrinsic dynamics and the spectro-temporal properties of an extrinsic signal. IEEE Transactions on Biomedical Engineering, 58(6), 1507-1510. (Article publié).
  • Plourde E, Champagne B. (2011). Multidimensional STSA estimators for speech enhancement with correlated spectral components. IEEE Transactions on Signal Processing, 7(59), 3013-3024. (Article publié).
  • Plourde E, Champagne B. (2009). Generalized Bayesian estimators of the spectral amplitude for speech enhancement. IEEE Signal Processing Letters, 16(6), 485-488. (Article publié).
  • Plourde E, Champagne B. (2008). Auditory based spectral amplitude estimators for speech enhancement. IEEE Transactions on Audio, Speech and Language Processing, 16(8), 1614-1623. (Article publié).

Rapports

  • Plourde E. (2010). Point process model of auditory nerve/cochlear nucleus spiking accounting for the baseline rate, spiking history and spectro-temporal properties of the input stimulus. Massachusetts Institute of Technology. 15 p.

Articles de conférence

  • *Hosseini M, *Azadmanesh M, Plourde E. (2019). Effect of simultaneously stimulating different ganglion cell types with the same stimulation strategy in epiretinal implants. Int. Conf. of the IEEE Engineering in Medicine and Biology Society. 1 p. (Article accepté).
  • *Hosseini M, Rodriguez G, Guo H, Lim HH, Plourde E. (2019). Novel metrics to measure the effect of additive inputs on the activity of sensory system neurons. Int. Conf. of the IEEE Engineering in Medicine and Biology Society. 5 p. (Article accepté).
  • Rouat J, Plourde E, Brodeur S. (2019). Unsupervised regulation for the self-organized CRITICAL recurrent neural network: Implementation and validation of end to end EEG classification. INRC Winter Workshop. (Article publié).
  • *LeFlahat E, Billard JC, Plourde E. (2018). A comparison of ECG waveform features for the classification of normal and abnormal heartbeats. Computing in Cardiology. 4 p. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2018). A supervised multi-channel speech enhancement algorithm based on Bayesian NMF model. IEEE Global Conf. on Signal and Information Process. (GlobalSIP). 221-225. (Article publié).
  • Lemaire W, Benhouria M, Koua K, Laplante E, Gauthier LP, Plourde E, Roy S, Fontaine R. (2018). Implant de neurostimulation rétinien. 1er Symposium québécois sur la neurostimulation. (Article publié).
  • *Chung H, Kim T, Plourde E, Champagne B. (2018). Noise-adaptive deep neural network for single-channel speech enhancement. IEEE Int. Workshop on Machine Learning for Signal Processing. 6 p. (Article publié).
  • *Hosseini M, Rodriguez G, Guo H, Lim HH, Plourde E. (2018). Noise processing in the central nucleus of the inferior colliculus. NeuroSymposium 2018. (Article publié).
  • *Hosseini M, Rodriguez G, Guo H, Lim H, Plourde E. (2018). The influence of noise on the spiking activity of inferior colliculus neurons under different stimulus levels and SNRs. Society for Neuroscience. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2017). Single-channel enhancement of convolutive noisy speech based on discriminative NMF algorithm. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2302-2306. (Article publié).
  • *Bosco J, Plourde E. (2017). Speech enhancement using both spectral and spectral modulation domains. IEEE Canadian Conf. on Electrical and Computer Engineering (CCECE). 4 p. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2016). Basis compensation in non-negative matrix factorization model for speech enhancement. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP). 2249-2253. (Article publié).
  • *Siahpoush S, Erfani Y, Rode T, Lim HH, Rouat J, Plourde E. (2015). Improving neural decoding in the central auditory system using bio-inspired spectro-temporal representations and a generalized bilinear model. 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. 5146-5150. (Article publié).
  • *Ghodoosipour G, Plourde E, Champagne B. (2015). On the use of a codebook-based modeling approach for Bayesian STSA speech enhancement. 28th IEEE Canadian Conf. on Electrical and Computer Engineering (CCECE). 1277-1282. (Article publié).
  • Plourde E, Rode T, Lim HH. (2015). The contribution of trained parameters to the goodness of fit of a Bayesian neural encoding model for the auditory system. IEEE EMBS Neural Engineering Conference. 910-913. (Article publié).
  • Rouat J, Brodeur S*, Plourde E. (2014). Auditory object feature maps with a hierarchical network of independent components. BMC Neuroscience, vol. 15 (Supp. 1), 66. (Article publié).
  • *Chung H, Plourde E, Champagne B. (2014). Regularized NMF-based speech enhancement with spectral components modeled by Gaussian mixtures. IEEE Machine Learning for Signal Processing Workshop. 6 p. (Article publié).
  • Plourde E. (2014). The effect of trained parameters in Bayesian neural encoding models for the auditory system. BMC Neuroscience, vol. 15 (Supp. 1), 67. (Article publié).
  • *Valade F, Plourde E. (2013). Évaluation de la justesse de la détection en fonction du bruit dans le tri de décharges neuronales. Association francophone pour le savoir (ACFAS) Conference. (Article publié).
  • Plourde E, Rode T, Lim H H, Brown E N. (2012). A point process study of the spiking activity and dynamics of inferior colliculus neurons. Society for Neuroscience Meeting (SFN). (Article publié).
  • Plourde E, Brown E N. (2012). The effect of different spectro-temporal representations of an input auditory stimulus on the fitting of a point process model of auditory neurons. Proc. 11th Int. Conf. Information Sci., Signal Processing and their Applications (ISSPA), 799-803. (Article publié).
  • Plourde E, Delgutte B, Brown E N. (2011). The relative importance in the auditory nerve spiking of a neuron’s internal dynamics versus an external input stimulus. Proc. IEEE Engineering in Medicine and Biology Society Conf. on Neural Eng., 9-12. (Article publié).
  • Plourde E, Champagne B. (2010). A family of Bayesian STSA estimators for the enhancement of speech with correlated frequency components. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 4766-4769. (Article publié).
  • Plourde E, Champagne B. (2009). Bayesian spectral amplitude estimation for speech enhancement with correlated spectral components. Proc. IEEE Workshop on Statistical Signal Processing, 397-400. (Article publié).
  • Plourde E, Champagne B. (2008). Perceptually based speech enhancement using the weighted beta-SA estimator. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 4193-4196. (Article publié).
  • Plourde E, Champagne B. (2007). Integrating the cochlea’s compressive nonlinearity in the Bayesian approach for speech enhancement. Proc. 15th European Signal Processing Conf. (EUSIPCO), 70-74. (Article publié).

Autres contributions

Gestion d'évènements

  • Member of the organizing commitee (Panel Co-Chair). (2019). IEEE Global Conference on Signal and Information Processing (GlobalSIP). (Conférence).
  • Member of the organizing commitee (Publication Chair). (2017). 15th Canadian Workshop on Information Theory (CWIT). (Atelier).
  • Session Chair - Data Compression and Coding. (2017). 15th Canadian Workshop on Information Theory (CWIT). (Atelier).
  • Technical Program Area Editor (Bio-Imaging and Signal Processing). (2017). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (Conférence).

Activités de collaboration internationale

  • Researcher. (2018-2021). États-Unis.

Présentations

  • Plourde E. (2019). Noisy speech processing: the brain vs. the algorithms. Harvard School of Engineering and Applied Sciences Electrical Engineering Seminar Series. Cambridge, États-Unis.
  • Plourde E. (2018). Neural encoding of visual information. iBIONICS Technical Seminars. Longueil, Canada.
  • Fontaine R, Roy S, Plourde E. (2017). Retinal implant development. iBIONICS Technical Seminars. Ottawa, Canada.
  • Plourde E. (2017). Speaking of neurons… speech and neural signal processing. ENB Symposium. Boston, États-Unis.
  • Plourde E. (2014). Encoding and decoding of neural signals from the auditory system. NSERC-CREATE Training Program in Integrated Sensor Systems Summer School. Montréal, Canada.
  • (2013). Neural signal processing. Discovery of 3IT research activities. Sherbrooke, Canada.
  • (2012). Statistical neural signal processing and its application to speech enhancement. NECOTIS-UdeM Multidisciplinary Colloquium. Sherbrooke, Canada.
  • (2011). The statistical coding of neural spikes in the auditory pathway. NECOTIS-UdeM Multidisciplinary Colloquium. Montréal, Canada.
  • (2011). The statistical processing of neural signals from the auditory system. Department of Electrical Engineering and Computer Engineering, Université de Sherbrooke. Sherbrooke, Canada.
  • (2010). Neuroscience Statistics Research Laboratory : Neural signal processing and general anesthesia. Brain and Cognitive Science Research Day. Cambridge, États-Unis.
  • (2008). Auditory based Bayesian estimators for speech enhancement. Neuroscience Statistics Research Laboratory Seminar, M.I.T. Cambridge, États-Unis.
  • (2008). Perceptually based speech enhancement using the weighted beta-SA estimator. SYTACom Student's Day. Montréal, Canada.

Les informations disponibles dans la base de données Expertus sont tirées du CV commun canadien.