Cem Subakan

Maab Elrashid Ahmed Mohamed Elrashid Ahmed Mohamed

Github

Anthony Deschênes

Postdoctorat - Université Laval

Google Scholar

Doctorat - Concordia

Superviseur⋅e principal⋅e :

francescopaissan@gmail.com

Github

Shubham Gupta

Doctorat - Université Laval

Eleonora Mancini

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Visiteur de recherche indépendant

Maîtrise recherche - Université Laval

Publications

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

Eleonora Mancini

Francesco Paissan

Paolo Torroni

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)

Listenable Maps for Zero-Shot Audio Classifiers

Francesco Paissan

Luca Della Libera

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)

openreview.net

Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming

Shubham Gupta

Isaac Neri Gomez-Sarmiento

Faez Amjed Mezdari

2024-09-19

Lecture Notes in Computer Science (publié)

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers

Eleonora Mancini

Francesco Paissan

2024-09-13

ArXiv (prépublication)

Audio Editing with Non-Rigid Text Prompts

Francesco Paissan

Zhepei Wang

Paris Smaragdis

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)

Listenable Maps for Audio Classifiers

Francesco Paissan

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

openreview.net

Open-Source Conversational AI with SpeechBrain 1.0

Titouan Parcollet

Adel Moumen

Sylvain de Langen

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)

DASB -- Discrete Audio and Speech Benchmark

Pooneh Mousavi

Luca Della Libera

Jarod Duret

Artem Ploujnikov

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)

How Should We Extract Discrete Audio Tokens from Self-Supervised Models?

Pooneh Mousavi

Jarod Duret

Salah Zaiem

Luca Della Libera

Artem Ploujnikov

2024-06-15

ArXiv (prépublication)

Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice

Shubham Gupta

Pascal Germain

2024-06-14

ArXiv (prépublication)

CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

David Budaghyan

Arsenii Gorin

Charles Onu

Doina Precup

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (voir plus)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

2024-04-14

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Focal Modulation Networks for Interpretable Sound Classification

Luca Della Libera

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)