Portrait de Cem Subakan

Cem Subakan

Membre académique associé
Professeur adjoint, Université Laval, Département d'informatique et de génie logiciel
Professeur associé, Concordia University, École de génie et d'informatique Gina-Cody
Sujets de recherche
Apprentissage multimodal
Site web
Google Scholar

Biographie

Cem Subakan est professeur adjoint à l'Université Laval, au sein du Département d'informatique et de génie logiciel. Il est également professeur adjoint affilié au Département d'informatique et de génie logiciel de l'Université Concordia, ainsi que membre académique associé à Mila – Institut québécois d'intelligence artificielle. Il a obtenu un doctorat en informatique de l'Université de l'Illinois à Urbana-Champaign (UIUC) et a effectué un postdoctorat à Mila. Il agit en tant que relecteur pour plusieurs conférences, notamment NeurIPS, ICML, ICLR, ICASSP et MLSP, ainsi que pour des revues telles que IEEE Signal Processing Letters (SPL) et IEEE Transactions on Audio, Speech, and Language Processing (TASL). Ses recherches portent principalement sur l'apprentissage automatique appliqué à la parole et à l'audio. Plus précisément, il travaille sur l'apprentissage profond pour la séparation de sources et l'amélioration de la parole dans des conditions réalistes, l'interprétabilité des réseaux neuronaux, l'apprentissage continu et l'apprentissage multimodal. Il a reçu le Prix du meilleur article étudiant lors de la conférence IEEE Machine Learning for Signal Processing (MLSP) en 2017, ainsi que la bourse Sabura Muroga du Département d'informatique de l'UIUC. Il est également un contributeur clé au projet SpeechBrain, où il dirige la partie consacrée à la séparation de la parole.

Étudiants actuels

Matthieu Cervera
Co-superviseur⋅e :
Mirco Ravanelli
Github
Jacob Comeau
Maîtrise recherche - Université Laval
Luca Della Libera
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Mirco Ravanelli
Github
Maab Elrashid Ahmed Mohamed
Doctorat - Concordia
Superviseur⋅e principal⋅e :
Mirco Ravanelli
Github
Shubham Gupta
Doctorat - Université Laval
Co-superviseur⋅e :
Laurent Charlin
Lovenya Jain
Stagiaire de recherche - Université Laval
Co-superviseur⋅e :
Mirco Ravanelli
Github
LinkedIn
Google Scholar
Jihoon Jeong
Doctorat - Université Laval
Co-superviseur⋅e :
Mirco Ravanelli
Site web
Google Scholar
Aravind Krishnan
Collaborateur·rice alumni - Saarland University
Eleonora Mancini
Collaborateur·rice alumni - UdeM
Co-superviseur⋅e :
Mirco Ravanelli
Github
Google Scholar
Francesco Paissan
Doctorat - Université Laval
Co-superviseur⋅e :
Aishwarya Agrawal
Site web
Github
Google Scholar

Publications

Learning Representations for New Sound Classes With Continual Self-Supervised Learning
Zhepei Wang
Cem Subakan
Xilin Jiang
Junkai Wu
Efthymios Tzinis
Mirco Ravanelli
Paris Smaragdis
In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framewor… (voir plus)k where the model can be updated without relying on labeled data. For this purpose, we propose adopting representation learning, where an encoder is trained using unlabeled data. This learning framework enables the study and implementation of a practically relevant use case where only a small amount of the labels is available in a continual learning context. We also make the empirical observation that a similarity-based representation learning method within this framework is robust to forgetting even if no explicit mechanism against forgetting is employed. We show that this approach obtains similar performance compared to several distillation-based continual learning methods when employed on self-supervised representation learning methods.
2021-12-31
IEEE Signal Processing Letters (publié)
doi.org
arxiv.org
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli
Titouan Parcollet
Peter Plantinga
Aku Rouhe
Samuele Cornell
Loren Lugosch
Cem Subakan
Nauman Dawalatabad
Abdelwahab Heba
Jianyuan Zhong
Ju-chieh Chou
Sung-Lin Yeh
Szu-wei Fu
Chien-Feng Liao
Elena Rastorgueva
François Grondin
William Aris
Hwidong Na
Yan Gao
Renato De Mori … (voir 1 de plus)
Yoshua Bengio
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech proc… (voir plus)essing technologies by being simple, flexible, user-friendly, and well-documented. This paper describes the core architecture designed to support several tasks of common interest, allowing users to naturally conceive, compare and share novel speech processing pipelines. SpeechBrain achieves competitive or state-of-the-art performance in a wide range of speech benchmarks. It also provides training recipes, pretrained models, and inference scripts for popular speech datasets, as well as tutorials which allow anyone with basic Python proficiency to familiarize themselves with speech technologies.
2020-12-31
arXiv (prépublication)
doi.org
arxiv.org
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Cem Subakan
Maxime Gasse
Laurent Charlin
Latent-variable generative models offer a principled solution for modeling and sampling from complex probability distributions. Implementing… (voir plus) a joint training objective with a complex prior, however, can be a tedious task, as one is typically required to derive and code a specific cost function for each new type of prior distribution. In this work, we propose a general framework for learning latent variable generative models in a two-step fashion. In the first step of the framework, we train an autoencoder, and in the second step we fit a prior model on the resulting latent distribution. This two-step approach offers a convenient alternative to joint training, as it allows for a straightforward combination of existing models without the hustle of deriving new cost functions, and the need for coding the joint training objectives. Through a set of experiments, we demonstrate that two-step learning results in performances similar to joint training, and in some cases even results in more accurate modeling.
2019-12-31
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org