Portrait of Cem Subakan

Cem Subakan

Associate Academic Member
Assistant Professor, Université Laval, Department of Computer Science and Software Engineering
Affiliate Assistant Professor, Concordia University, Gina Cody School of Engineering and Computer Science
Research Topics
Multimodal Learning
Website
Google Scholar

Biography

Cem Subakan is an assistant professor in the Computer Science and Software Engineering Department at Université Laval, and an affiliate assistant professor in the Computer Science and Software Engineering Department at Concordia University. He is also an associate academic member of Mila – Quebec Artificial Intelligence Institute. After receiving his PhD in computer science from the University of Illinois at Urbana-Champaign (UIUC), Subakan did a postdoc at Mila. He serves as a reviewer for many conferences including NeurIPS, ICML, ICLR, ICASSP and MLSP, as well as for journals, such as IEEE Signal Processing Letters and IEEE Transactions on Audio, Speech, and Language Processing. His principal research interest is machine learning for speech and audio. More specifically, he works on deep learning for source separation and speech enhancement under realistic conditions, neural network interpretability, continual learning and multi-modal learning.

Subakan was awarded the Best Student Paper Award at the 2017 IEEE Machine Learning for Signal Processing Conference, and also obtained a Sabura Muroga Fellowship from UIUC’s Department of Computer Science. He is a core contributor to the SpeechBrain project, leading the speech separation component.

Current Students

Jacob Comeau
Master's Research - Université Laval
Luca Della Libera
PhD - Concordia University
Principal supervisor :
Mirco Ravanelli
Github
Anthony Deschênes
Postdoctorate - Université Laval
Google Scholar
Maab Elrashid Ahmed Mohamed Elrashid Ahmed Mohamed
PhD - Concordia University
Principal supervisor :
Mirco Ravanelli
Github
Shubham Gupta
PhD - Université Laval
Co-supervisor :
Laurent Charlin
Eleonora Mancini
Collaborating Alumni - Université de Montréal
Co-supervisor :
Mirco Ravanelli
Github
Google Scholar
Francesco Paissan
Independent visiting researcher
francescopaissan@gmail.com
Website
Github
Google Scholar
Mathis Rezzouk
Master's Research - Université Laval

Publications

Continual Learning of New Sound Classes Using Generative Replay
Zhepei Wang
Cem Subakan
Efthymios Tzinis
Paris Smaragdis
Laurent Charlin
Continual learning consists in incrementally training a model on a sequence of datasets and testing on the union of all datasets. In this pa… (see more)per, we examine continual learning for the problem of sound classification, in which we wish to refine already trained models to learn new sound classes. In practice one does not want to maintain all past training data and retrain from scratch, but naively updating a model with new data(sets) results in a degradation of already learned tasks, which is referred to as "catastrophic forgetting." We develop a generative replay procedure for generating training audio spectrogram data, in place of keeping older training datasets. We show that by incrementally refining a classifier with generative replay a generator that is 4% of the size of all previous training data matches the performance of refining the classifier keeping 20% of all previous training data. We thus conclude that we can extend a trained sound classifier to learn new classes without having to keep previously used datasets.
2019-10-20
2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (published)
doi.org
arxiv.org