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

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

Luca Della Libera

PhD - Concordia University

Co-supervisor :

Cem Subakan

Github

Wagner Drew

Undergraduate - Concordia University

Github

Gianfranco Dumoulin Bertucci

Master's Research - Concordia University

Website

Github

Maab Elrashid Ahmed Mohamed Elrashid Ahmed Mohamed

PhD - Concordia University

Co-supervisor :

Cem Subakan

Github

Bonzi Francesco

PhD - Concordia University

Github

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 :

PhD - Université de Montréal

Co-supervisor :

PhD - Concordia University

PhD - Concordia University

Co-supervisor :

Peter Peter

Postdoctorate - McGill University

PhD - Université de Montréal

Ritika Dhamija Ritika

Collaborating researcher - Concordia University University

Github

Victor Victor

Master's Research - Concordia University

Blog Posts

June 13, 2024

SpeechBrain 1.0: Making Conversational AI Accessible to Everyone

Mirco Ravanelli

Read the article

April 28, 2021

Introducing SpeechBrain: A General-Purpose PyTorch Speech Processing Toolkit

Mirco Ravanelli

Loren Lugosch

Read the 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… (see more)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 (preprint)

arxiv.org

A protocol for trustworthy EEG decoding with neural networks

Davide Borra

Elisa Magosso

Mirco Ravanelli

2024-11-01

Neural Networks (published)

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 (published)

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… (see more)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 (preprint)

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 (published)

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 (published)

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 (published)

doi.org

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers

Eleonora Mancini

Francesco Paissan

Mirco Ravanelli

Cem Subakan

2024-09-13

ArXiv (preprint)

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 … (see more)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 (published)

doi.org

arxiv.org

ProGRes: Prompted Generative Rescoring on ASR n-Best

Ada Defne Tur

Adel Moumen

Mirco Ravanelli

2024-08-30

ArXiv (preprint)

doi.org

arxiv.org