Portrait of Mirco Ravanelli

Mirco Ravanelli

Associate Academic Member
Assistant Professor, Concordia University, Gina Cody School of Engineering and Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning
Website
Google Scholar
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Biography

Mirco Ravanelli is an assistant professor at Concordia University, adjunct professor at Université de Montréal and associate member of Mila – Quebec Artificial Intelligence Institute.

Ravanelli is an expert in deep learning and conversational AI, publishing over sixty papers in these fields. His contributions were honoured with a 2022 Amazon Research Award.

His research focuses primarily on novel deep learning algorithms, including self-supervised, continual, multimodal, cooperative and energy-efficient learning.

Formerly a postdoctoral fellow at Mila under Yoshua Bengio, he founded and now leads SpeechBrain, one of the most extensively used open-source toolkits in the field of speech processing and conversational AI.

Current Students

Hiba Akhaddar
Master's Research - Concordia University
Seina Assadian
Collaborating researcher - Concordia University University
Github
Cordelle Briac
Collaborating researcher - Concordia University University
Github
Victor Cruz
Master's Research - Concordia University
Luca Della Libera
PhD - Concordia University
Co-supervisor :
Cem Subakan
Github
Wagner Drew
Master's Research - Concordia University
Co-supervisor :
Irina Rish
Github
Gianfranco Dumoulin Bertucci
Master's Research - Concordia University
Website
Github
Nadine El-Mufti
Master's Research - Concordia University
nadine.el-mufti@mila.quebec
Website
Github
Google Scholar
Maab Elrashid Ahmed Mohamed
PhD - Concordia University
Co-supervisor :
Cem Subakan
Github
Bonzi Francesco
PhD - Concordia University
Github
Alessio Giuseppe Alessio
Collaborating researcher - International School for Advanced Studies (Trieste, Italy)
Salman Sami Hussain Ali
Collaborating researcher - Concordia University University
Github
Tristan Lueger
Collaborating researcher - Concordia University University
Eleonora Mancini
Collaborating Alumni - Université de Montréal
Principal supervisor :
Cem Subakan
Github
Google Scholar
Jama Mohamud
PhD - Université de Montréal
Co-supervisor :
Yoshua Bengio
Website
Github
Google Scholar
Pooneh Mousavi
PhD - Concordia University
Website
Github
Google Scholar
Fırat Öncel
PhD - Concordia University
Co-supervisor :
Laurent Charlin
Website
Github
Google Scholar
Peter Peter
Postdoctorate - McGill University
Website
Github
Google Scholar
Artem Ploujnikov
PhD - Université de Montréal
Ritika Dhamija Ritika
Collaborating researcher - Concordia University University
Github

Publications

SoundChoice: Grapheme-to-Phoneme Models with Semantic Disambiguation
Artem Ploujnikov
Mirco Ravanelli
2022-09-18
Interspeech 2022 (published)
doi.org
arxiv.org
Real-M: Towards Speech Separation on Real Mixtures
Cem 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… (see more)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.
2022-05-23
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
arxiv.org
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 article, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framew… (see more)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.
2022-05-15
ArXiv (preprint)
doi.org
arxiv.org
Biasly: a machine learning based platform for automatic racial discrimination detection in online texts
David Bamman
Chris Dyer
Noah A. Smith. 2014
Steven Bird
Ewan Klein
Edward Loper
Nat-527
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova. 2019
Bert
Samuel Gehman
Suchin Gururangan
Maarten Sap
Dan Hendrycks
Kevin Gimpel. 2020
Gaussian
Alex Lamb
Di He … (see 22 more)
Anirudh Goyal
Guolin Ke
Feng Liao
Mirco Ravanelli
Yoshua Bengio
Zhenzhong Lan
Mingda Chen
Sebastian Goodman
Yann LeCun
Bernhard E. Boser
J. Denker
Don-608 nie Henderson
Robin Howard
Wayne Hubbard
Yinhan Liu
Myle Ott
Naman Goyal
Jingfei Du
Mandar Joshi
Danqi Chen
Omer Levy
Mike Lewis
Warning : this paper contains content that may 001 be offensive or upsetting. 002 Detecting hateful, toxic, and otherwise racist 003 or sexi… (see more)st language in user-generated online con-004 tents has become an increasingly important task 005 in recent years. Indeed, the anonymity, the 006 transience, the size of messages, and the dif-007 ficulty of management, facilitate the diffusion 008 of racist or hateful messages across the Inter-009 net. The critical influence of this cyber-racism 010 is no longer limited to social media, but also 011 has a significant effect on our society : corpo-012 rate business operation, users’ health, crimes, 013 etc. Traditional racist speech reporting chan-014 nels have proven inadequate due to the enor-015 mous explosion of information, so there is an 016 urgent need for a method to automatically and 017 promptly detect texts with racial discrimination. 018 We propose in this work, a machine learning-019 based approach to enable automatic detection 020 of racist text content over the internet. State-of-021 the-art machine learning models that are able 022 to grasp language structures are adapted in this 023 study. Our main contribution include 1) a large 024 scale racial discrimination data set collected 025 from three distinct sources and annotated ac-026 cording to a guideline developed by specialists, 027 2) a set of machine learning models with vari-028 ous architectures for racial discrimination de-029 tection, and 3) a web-browser-based software 030 that assist users to debias their texts when us-031 ing the internet. All these resources are made 032 publicly available.
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 article, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framew… (see more)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 (published)
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… (see more)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 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… (see more)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
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli
Titouan Parcollet
Peter William VanHarn 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
E. Rastorgueva
Franccois Grondin
William Aris
Hwidong Na
Yan Gao
Renato De Mori … (see 1 more)
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… (see more)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.
2021-06-08
ArXiv (preprint)
arxiv.org
arxiv.org
Multi-Task Self-Supervised Learning for Robust Speech Recognition
Mirco Ravanelli
Jianyuan Zhong
Santiago Pascual
Pawel Swietojanski
Joao Monteiro
Jan Trmal
Yoshua Bengio
Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To t… (see more)ake a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). PASE was shown to capture relevant speech information, including speaker voice-print and phonemes. This paper proposes PASE+, an improved version of PASE for robust speech recognition in noisy and reverberant environments. To this end, we employ an online speech distortion module, that contaminates the input signals with a variety of random disturbances. We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, we refine the set of workers used in self-supervision to encourage better cooperation.Results on TIMIT, DIRHA and CHiME-5 show that PASE+ significantly outperforms both the previous version of PASE as well as common acoustic features. Interestingly, PASE+ learns transferable representations suitable for highly mismatched acoustic conditions.
2020-05-04
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
arxiv.org
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Santiago Pascual
Mirco Ravanelli
Joan Parets I Serra
Antonio Bonafonte
Yoshua Bengio
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech sig… (see more)nals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech representations by employing a self-supervised encoder-discriminator approach. This paper proposes an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different self-supervised tasks. The needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones. Experiments show that the proposed approach can learn transferable, robust, and problem-agnostic features that carry on relevant information from the speech signal, such as speaker identity, phonemes, and even higher-level features such as emotional cues. In addition, a number of design choices make the encoder easily exportable, facilitating its direct usage or adaptation to different problems.
2019-09-15
Interspeech 2019 (published)
doi.org
arxiv.org
Learning Speaker Representations with Mutual Information
Mirco Ravanelli
Yoshua Bengio
Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependen… (see more)ce are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Generative Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions.
2019-09-15
Interspeech 2019 (published)
doi.org
arxiv.org
Speech Model Pre-training for End-to-End Spoken Language Understanding
Loren Lugosch
Mirco Ravanelli
Patrick Ignoto
Vikrant Singh Tomar
Yoshua Bengio
Whereas conventional spoken language understanding (SLU) systems map speech to text, and then text to intent, end-to-end SLU systems map spe… (see more)ech directly to intent through a single trainable model. Achieving high accuracy with these end-to-end models without a large amount of training data is difficult. We propose a method to reduce the data requirements of end-to-end SLU in which the model is first pre-trained to predict words and phonemes, thus learning good features for SLU. We introduce a new SLU dataset, Fluent Speech Commands, and show that our method improves performance both when the full dataset is used for training and when only a small subset is used. We also describe preliminary experiments to gauge the model's ability to generalize to new phrases not heard during training.
2019-09-15
Interspeech 2019 (published)
doi.org
arxiv.org