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
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 :
Cem 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
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Cem 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
Victor Victor
Maîtrise recherche - Concordia

Publications

Open-Source Conversational AI with SpeechBrain 1.0
Mirco Ravanelli
Titouan Parcollet
Adel Moumen
Sylvain de Langen
Cem 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 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 Subakan
Mirco Ravanelli
2024-06-15
ArXiv (prépublication)
doi.org
arxiv.org
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice
Shubham Gupta
Mirco Ravanelli
Pascal Germain
Cem Subakan
2024-06-14
ArXiv (prépublication)
doi.org
arxiv.org
Focal Modulation Networks for Interpretable Sound Classification
Luca Della Libera
Cem Subakan
Mirco Ravanelli
The increasing success of deep neural networks has raised concerns about their inherent black-box nature, posing challenges related to inter… (voir plus)pretability and trust. While there has been extensive exploration of interpretation techniques in vision and language, interpretability in the audio domain has received limited attention, primarily focusing on post-hoc explanations. This paper addresses the problem of interpretability by-design in the audio domain by utilizing the recently proposed attention-free focal modulation networks (FocalNets). We apply FocalNets to the task of environmental sound classification for the first time and evaluate their interpretability properties on the popular ESC-50 dataset. Our method outperforms a similarly sized vision transformer both in terms of accuracy and interpretability. Furthermore, it is competitive against PIQ, a method specifically designed for post-hoc interpretation in the audio domain.
2024-04-14
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)
doi.org
arxiv.org
Resource-Efficient Separation Transformer
Luca Della Libera
Cem Subakan
Mirco Ravanelli
Samuele Cornell
Frédéric Lepoutre
François Grondin
Transformers have recently achieved state-of-the-art performance in speech separation. These models, however, are computationally demanding … (voir plus)and require a lot of learnable parameters. This paper explores Transformer-based speech separation with a reduced computational cost. Our main contribution is the development of the Resource-Efficient Separation Transformer (RE-SepFormer), a self-attention-based architecture that reduces the computational burden in two ways. First, it uses non-overlapping blocks in the latent space. Second, it operates on compact latent summaries calculated from each chunk. The RE-SepFormer reaches a competitive performance on the popular WSJ0-2Mix and WHAM! datasets in both causal and non-causal settings. Remarkably, it scales significantly better than the previous Transformer-based architectures in terms of memory and inference time, making it more suitable for processing long mixtures.
2024-04-14
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
Luca Zampierin
Ghouthi Boukli Hacene
Bac Nguyen
Mirco Ravanelli
2024-04-14
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)
doi.org
arxiv.org
Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera
Jacopo Andreoli
Davide Dalle Pezze
Mirco Ravanelli
Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (voir plus)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.
2024-02-02
ArXiv (prépublication)
doi.org
arxiv.org
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini
Shenyang Huang
Joao Alex Cunha
Zhiyi Li
Gabriela Moisescu-Pareja
Oleksandr Dymov
Samuel Maddrell-Mander
Callum McLean
Frederik Wenkel
Luis Müller
Jama Hussein Mohamud
Ali Parviz
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Kerstin Klaser
Josef Dean
Cas Wognum … (voir 15 de plus)
Maciej Sypetkowski
Guillaume Rabusseau
Reihaneh Rabbany
Jian Tang
Christopher Morris
Ioannis Koutis
Mirco Ravanelli
Guy Wolf
Prudencio Tossou
Hadrien Mary
Therence Bois
Andrew William Fitzgibbon
Blazej Banaszewski
Chad Martin
Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, wh… (voir plus)ere datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. The Graphium library is publicly available on Github and the dataset links are available in Part 1 and Part 2.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Are LLMs Robust for Spoken Dialogues?
Seyed Mahed Mousavi
Gabriel Roccabruna
Simone Alghisi
Massimo Rizzoli
Mirco Ravanelli
Giuseppe Riccardi
Large Pre-Trained Language Models have demonstrated state-of-the-art performance in different downstream tasks, including dialogue state tra… (voir plus)cking and end-to-end response generation. Nevertheless, most of the publicly available datasets and benchmarks on task-oriented dialogues focus on written conversations. Consequently, the robustness of the developed models to spoken interactions is unknown. In this work, we have evaluated the performance of LLMs for spoken task-oriented dialogues on the DSTC11 test sets. Due to the lack of proper spoken dialogue datasets, we have automatically transcribed a development set of spoken dialogues with a state-of-the-art ASR engine. We have characterized the ASR-error types and their distributions and simulated these errors in a large dataset of dialogues. We report the intrinsic (perplexity) and extrinsic (human evaluation) performance of fine-tuned GPT-2 and T5 models in two subtasks of response generation and dialogue state tracking, respectively. The results show that LLMs are not robust to spoken noise by default, however, fine-tuning/training such models on a proper dataset of spoken TODs can result in a more robust performance.
2024-01-04
ArXiv (prépublication)
doi.org
arxiv.org
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
Firat Oncel
Matthias Bethge
Beyza Ermis
Mirco Ravanelli
Cem Subakan
cCaugatay Yildiz
2024-01-01
EMNLP (publié)
arxiv.org
arxiv.org
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Umberto Cappellazzo
Daniele Falavigna
Alessio Brutti
Mirco Ravanelli
The common modus operandi of fine-tuning large pre-trained Transformer models entails the adaptation of all their parameters (i.e., full fin… (voir plus)e-tuning). While achieving striking results on multiple tasks, this approach becomes unfeasible as the model size and the number of downstream tasks increase. In natural language processing and computer vision, parameter-efficient approaches like prompt-tuning and adapters have emerged as solid alternatives by fine-tuning only a small number of extra parameters, without sacrificing performance accuracy. For audio classification tasks, the Audio Spectrogram Transformer model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common parameter-efficient methods, revealing that adapters and LoRA consistently outperform the other methods across four benchmarks. Whereas adapters prove to be more efficient in few-shot learning settings, LoRA turns out to scale better as we increase the number of learnable parameters. We finally carry out ablation studies to find the best configuration for adapters and LoRA.
2024-01-01
2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org
arxiv.org