Portrait de Mirco Ravanelli

Mirco Ravanelli

Membre académique associé
Professeur adjoint, Concordia University, École de génie et d'informatique Gina-Cody
Professeur associé, Université de Montréal, Département d'informatique et de recherche opérationnelle
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Biographie

Mirco Ravanelli est professeur adjoint à l'Université Concordia, professeur associé à l'Université de Montréal et membre associé de Mila – Institut québécois d’intelligence artificielle. Lauréat du prix Amazon Research 2022, il est expert en apprentissage profond et en IA conversationnelle, et a publié plus de 60 articles dans ces domaines. Il se concentre principalement sur les nouveaux algorithmes d'apprentissage profond, y compris l'apprentissage autosupervisé, continu, multimodal, coopératif et économe en énergie. Mirco Ravanelli a effectué son postdoctorat à Mila, sous la direction du professeur Yoshua Bengio. Il est notamment le fondateur et le chef de file de SpeechBrain, l'une des boîtes à outils en code source ouvert les plus largement adoptées dans le domaine du traitement de la parole et de l'IA conversationnelle.

Étudiants actuels

Arian Morteza
Doctorat - Concordia University
arian.morteza@mila.quebec
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Artem Ploujnikov
Doctorat - Université de Montréal
artem.ploujnikov@mila.quebec
Cordelle Briac
Collaborateur·rice de recherche - Concordia University University
briac.cordelle@mila.quebec
Ritika Dhamija Ritika
Collaborateur·rice de recherche - Concordia University University
dhamija.ritika@mila.quebec
Github
Eleonora Mancini
Stagiaire de recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Cem (Yusuf) Subakan
eleonora.mancini@mila.quebec
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Fırat Öncel
Doctorat - Concordia University
Co-superviseur⋅e :
Laurent Charlin
firat.oncel@mila.quebec
Site web
Github
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Gianfranco Dumoulin
Maîtrise recherche - Concordia University
gianfranco.bertucci@mila.quebec
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Hiba Akhaddar
Maîtrise recherche - Concordia University
hiba.akhaddar@mila.quebec
Jama Mohamud
Doctorat - Université de Montréal
Co-superviseur⋅e :
Yoshua Bengio
hussein-mohamu.jama@mila.quebec
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Github
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Luca Della Libera
Doctorat - Concordia University
Co-superviseur⋅e :
Cem (Yusuf) Subakan
luca.dellalibera@mila.quebec
Github
Pooneh Mousavi
Doctorat - Concordia University
pooneh.mousavi@mila.quebec
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Salman Sami Hussain Ali
Collaborateur·rice de recherche - Concordia University University
salman.hussainali@mila.quebec
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Seina Assadian
Collaborateur·rice de recherche - Concordia University University
seina.assadian@mila.quebec
Github
Tristan Lueger Lueger
Collaborateur·rice de recherche - Concordia University University
tristan.lueger@mila.quebec
Victor Cruz
Maîtrise recherche - Concordia University
victor.cruz@mila.quebec
Wagner Drew
Baccalauréat - Concordia University
drew.wagner@mila.quebec
Github

Publications

Learning Representations for New Sound Classes With Continual Self-Supervised Learning
Zhepei Wang
Cem (Yusuf) Subakan
Xilin Jiang
Junkai Wu
Efthymios Tzinis
Mirco Ravanelli
Paris Smaragdis
In this article, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framew… (voir plus)ork 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.
2022-01-01
IEEE Signal Processing Letters (publié)
doi.org
arxiv.org
OSSEM: one-shot speaker adaptive speech enhancement using meta learning
Cheng Yu
Szu‐wei Fu
Tsun-An Hsieh
Yu-shan Tsao
Mirco Ravanelli
Although deep learning (DL) has achieved notable progress in speech enhancement (SE), further research is still required for a DL-based SE s… (voir plus)ystem to adapt effectively and efficiently to particular speakers. In this study, we propose a novel meta-learning-based speaker-adaptive SE approach (called OSSEM) that aims to achieve SE model adaptation in a one-shot manner. OSSEM consists of a modified transformer SE network and a speaker-specific masking (SSM) network. In practice, the SSM network takes an enrolled speaker embedding extracted using ECAPA-TDNN to adjust the input noisy feature through masking. To evaluate OSSEM, we designed a modified Voice Bank-DEMAND dataset, in which one utterance from the testing set was used for model adaptation, and the remaining utterances were used for testing the performance. Moreover, we set restrictions allowing the enhancement process to be conducted in real time, and thus designed OSSEM to be a causal SE system. Experimental results first show that OSSEM can effectively adapt a pretrained SE model to a particular speaker with only one utterance, thus yielding improved SE results. Meanwhile, OSSEM exhibits a competitive performance compared to state-of-the-art causal SE systems.
2021-11-10
ArXiv (preprint)
doi.org
arxiv.org
Real-M: Towards Speech Separation on Real Mixtures
Cem (Yusuf) Subakan
Mirco Ravanelli
Samuele Cornell
François Grondin
In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation mod… (voir plus)els on synthetic datasets, while the performance of state-of-the-art techniques on in-the-wild speech data remains an open question. This paper contributes to fill this gap in two ways. First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures. Secondly, we address the problem of performance evaluation of real-life mixtures, where the ground truth is not available. We bypass this issue by carefully designing a blind Scale-Invariant Signal-to-Noise Ratio (SI-SNR) neural estimator. Through a user study, we show that our estimator reliably evaluates the separation performance on real mixtures, i.e. we observe that the performance predictions of the SI-SNR estimator correlate well with human opinions. Moreover, when evaluating popular speech separation models, we observe that the performance trends predicted by our estimator on the REAL-M dataset closely follow the performance trends achieved on synthetic benchmarks.
2021-10-20
ArXiv (preprint)
doi.org
arxiv.org