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

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

Luca Della Libera

Doctorat - Concordia

Co-superviseur⋅e :

Cem 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 :

Doctorat - Concordia

Salman Sami Hussain Ali

Collaborateur·rice de recherche - Concordia University

Github

Tristan Lueger

Collaborateur·rice de recherche - Concordia University

Eleonora Mancini

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - Concordia

Doctorat - Concordia

Co-superviseur⋅e :

Peter Peter

Postdoctorat - McGill

Doctorat - UdeM

Ritika Dhamija Ritika

Collaborateur·rice de recherche - Concordia University

Github

Victor Victor

Maîtrise recherche - Concordia

Billets de blogue

13 juin 2024

SpeechBrain 1.0 : rendre l’IA conversationnelle accessible à tout le monde

par

Mirco Ravanelli

Lire l'article

Introducing SpeechBrain: A general-purpose PyTorch speech processing toolkit

28 avril 2021

Voici SpeechBrain : Une boîte à outils polyvalente de traitement de la parole basée sur PyTorch

par

Mirco Ravanelli

Loren Lugosch

Lire l'article

Publications

Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech

Eleonora Mancini

Francesco Paissan

Paolo Torroni

Mirco Ravanelli

Cem Subakan

Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (voir plus)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.

2024-11-12

ArXiv (prépublication)

arxiv.org

A protocol for trustworthy EEG decoding with neural networks

Davide Borra

Elisa Magosso

Mirco Ravanelli

2024-11-01

Neural Networks (publié)

doi.org

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

Listenable Maps for Zero-Shot Audio Classifiers

Francesco Paissan

Luca Della Libera

Mirco Ravanelli

Cem Subakan

Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthines… (voir plus)s of this technology. In this paper, we introduce LMAC-ZS (Listenable Maps for Audio Classifiers in the Zero-Shot context), which, to the best of our knowledge, is the first decoder-based post-hoc interpretation method for explaining the decisions of zero-shot audio classifiers. The proposed method utilizes a novel loss function that maximizes the faithfulness to the original similarity between a given text-and-audio pair. We provide an extensive evaluation using the Contrastive Language-Audio Pretraining (CLAP) model to showcase that our interpreter remains faithful to the decisions in a zero-shot classification context. Moreover, we qualitatively show that our method produces meaningful explanations that correlate well with different text prompts.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

doi.org

openreview.net

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 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 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 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