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
Sujets de recherche
Apprentissage profond
Site web
Google Scholar
LinkedIn
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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

Hiba Akhaddar
Maîtrise recherche - Concordia
Seina Assadian
Collaborateur·rice de recherche - Concordia University
Github
Cordelle Briac
Collaborateur·rice de recherche - Concordia University
Victor Cruz
Maîtrise recherche - Concordia
Luca Della Libera
Doctorat - Concordia
Co-superviseur⋅e :
Cem (Yusuf) Subakan
Github
Wagner Drew
Baccalauréat - Concordia
Github
Gianfranco Dumoulin Bertucci
Maîtrise recherche - Concordia
Site web
Github
Maab Elrashid Ahmed Mohamed Elrashid Ahmed Mohamed
Doctorat - Concordia
Co-superviseur⋅e :
Cem (Yusuf) Subakan
Github
Bonzi Francesco
Doctorat - Concordia
Github
Salman Sami Hussain Ali
Collaborateur·rice de recherche - Concordia University
Github
Tristan Lueger
Collaborateur·rice de recherche - Concordia University
Eleonora Mancini
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Cem (Yusuf) Subakan
Github
Google Scholar
Jama Mohamud
Doctorat - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Pooneh Mousavi
Doctorat - Concordia
Site web
Github
Google Scholar
Fırat Öncel
Doctorat - Concordia
Co-superviseur⋅e :
Laurent Charlin
Site web
Github
Google Scholar
Peter Peter
Postdoctorat - McGill
Site web
Github
Google Scholar
Artem Ploujnikov
Doctorat - UdeM
Ritika Dhamija Ritika
Collaborateur·rice de recherche - Concordia University
Github

Publications

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Davide Borra
Francesco Paissan
Mirco Ravanelli
2024-11-01
Computers in Biology and Medicine (publié)
doi.org
What Are They Doing? Joint Audio-Speech Co-Reasoning
Yingzhi Wang
Pooneh Mousavi
Artem Ploujnikov
Mirco Ravanelli
2024-09-22
ArXiv (prépublication)
doi.org
arxiv.org
Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming
Shubham Gupta
Isaac Neri Gomez-Sarmiento
Faez Amjed Mezdari
Mirco Ravanelli
Cem (Yusuf) Subakan
2024-09-19
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task
Davide Borra
Mirco Ravanelli
2024-09-19
Lecture Notes in Computer Science (publié)
doi.org
Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks
Davide Borra
Matteo Fraternali
Mirco Ravanelli
Elisa Magosso
2024-09-19
Lecture Notes in Computer Science (publié)
doi.org
LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Eleonora Mancini
Francesco Paissan
Mirco Ravanelli
Cem (Yusuf) Subakan
2024-09-13
ArXiv (prépublication)
doi.org
arxiv.org
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan
Zhepei Wang
Mirco Ravanelli
Paris Smaragdis
Cem (Yusuf) Subakan
In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits … (voir plus)that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.
2024-09-01
Interspeech 2024 (publié)
doi.org
arxiv.org
ProGRes: Prompted Generative Rescoring on ASR n-Best
Ada Defne Tur
Adel Moumen
Mirco Ravanelli
2024-08-30
ArXiv (prépublication)
doi.org
arxiv.org
Listenable Maps for Audio Classifiers
Francesco Paissan
Mirco Ravanelli
Cem (Yusuf) Subakan
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
openreview.net
Open-Source Conversational AI with SpeechBrain 1.0
Mirco Ravanelli
Titouan Parcollet
Adel Moumen
Sylvain de Langen
Cem (Yusuf) Subakan
Peter William VanHarn Plantinga
Yingzhi Wang
Pooneh Mousavi
Luca Della Libera
Artem Ploujnikov
Francesco Paissan
Davide Borra
Salah Zaiem
Zeyu Zhao
Shucong Zhang
Georgios Karakasidis
Sung-Lin Yeh
Pierre Champion
Aku Rouhe
Rudolf Braun … (voir 11 de plus)
Florian Mai
Juan Pablo Zuluaga
Seyed Mahed Mousavi
Andreas Nautsch
Xuechen Liu
Sangeet Sagar
Jarod Duret
Salima Mdhaffar
G. Laperriere
Renato de Mori
Yannick Estève
SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech rec… (voir plus)ognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and replicability by releasing both the pre-trained models and the complete"recipes"of code and algorithms required for training them. This paper presents SpeechBrain 1.0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face. SpeechBrain 1.0 introduces new technologies to support diverse learning modalities, Large Language Model (LLM) integration, and advanced decoding strategies, along with novel models, tasks, and modalities. It also includes a new benchmark repository, offering researchers a unified platform for evaluating models across diverse tasks
2024-06-29
ArXiv (prépublication)
doi.org
arxiv.org
DASB -- Discrete Audio and Speech Benchmark
Pooneh Mousavi
Luca Della Libera
Jarod Duret
Artem Ploujnikov
Cem (Yusuf) Subakan
Mirco Ravanelli
Discrete audio tokens have recently gained considerable attention for their potential to connect audio and language processing, enabling the… (voir plus) creation of modern multimodal large language models. Ideal audio tokens must effectively preserve phonetic and semantic content along with paralinguistic information, speaker identity, and other details. While several types of audio tokens have been recently proposed, identifying the optimal tokenizer for various tasks is challenging due to the inconsistent evaluation settings in existing studies. To address this gap, we release the Discrete Audio and Speech Benchmark (DASB), a comprehensive leaderboard for benchmarking discrete audio tokens across a wide range of discriminative tasks, including speech recognition, speaker identification and verification, emotion recognition, keyword spotting, and intent classification, as well as generative tasks such as speech enhancement, separation, and text-to-speech. Our results show that, on average, semantic tokens outperform compression tokens across most discriminative and generative tasks. However, the performance gap between semantic tokens and standard continuous representations remains substantial, highlighting the need for further research in this field.
2024-06-20
ArXiv (prépublication)
doi.org
arxiv.org
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?
Pooneh Mousavi
Jarod Duret
Salah Zaiem
Luca Della Libera
Artem Ploujnikov
Cem (Yusuf) Subakan
Mirco Ravanelli
2024-06-15
ArXiv (prépublication)
doi.org
arxiv.org