Cem Subakan

Maab Elrashid Ahmed Mohamed

Github

Doctorat - Concordia

Superviseur⋅e principal⋅e :

francescopaissan@gmail.com

Github

Shubham Gupta

Doctorat - Université Laval

Co-superviseur⋅e :

Laurent Charlin

Eleonora Mancini

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Visiteur de recherche indépendant

Maîtrise recherche - Université Laval

Publications

DASB -- Discrete Audio and Speech Benchmark

Jarod Duret

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)

openreview.net

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

Pooneh Mousavi

Jarod Duret

Salah Zaiem

Discrete audio tokens have recently gained attention for their potential to bridge the gap between audio and language processing. Ideal audi… (voir plus)o tokens must preserve content, paralinguistic elements, speaker identity, and many other audio details. Current audio tokenization methods fall into two categories: Semantic tokens, acquired through quantization of Self-Supervised Learning (SSL) models, and Neural compression-based tokens (codecs). Although previous studies have benchmarked codec models to identify optimal configurations, the ideal setup for quantizing pretrained SSL models remains unclear. This paper explores the optimal configuration of semantic tokens across discriminative and generative tasks. We propose a scalable solution to train a universal vocoder across multiple SSL layers. Furthermore, an attention mechanism is employed to identify task-specific influential layers, enhancing the adaptability and performance of semantic tokens in diverse audio applications.

2024-06-15

ArXiv (prépublication)

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

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)

Resource-Efficient Separation Transformer

Luca Della Libera

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)

Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?

Fırat Öncel

Matthias Bethge

Beyza Ermis

cCaugatay Yildiz

2024-01-01

EMNLP (publié)

CL-MASR: A Continual Learning Benchmark for Multilingual ASR

Salah Zaiem

2024-01-01

IEEE ACM Trans. Audio Speech Lang. Process. (publié)

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

Pooneh Mousavi

Jarod Duret

Salah Zaiem

2024-01-01

INTERSPEECH (publié)

Unsupervised Improvement of Audio-Text Cross-Modal Representations

Zhepei Wang

Krishna Subramani

Junkai Wu

Tiago Tavares

Fabio Ayres

Paris Smaragdis

Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional trai… (voir plus)ning approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.

2023-10-22

2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (publié)

Self-Supervised Learning for Infant Cry Analysis

Arsenii Gorin

Sajjad Abdoli

Junhao Wang

Samantha Latremouille

Charles Onu

In this paper, we explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical in… (voir plus)dications of more than a thousand newborns. Specifically, we target cry-based detection of neurological injury as well as identification of cry triggers such as pain, hunger, and discomfort. Annotating a large database in the medical setting is expensive and timeconsuming, typically requiring the collaboration of several experts over years. Leveraging large amounts of unlabeled audio data to learn useful representations can lower the cost of building robust models and, ultimately, clinical solutions. In this work, we experiment with self-supervised pre-training of a convolutional neural network on large audio datasets. We show that pre-training with SSL contrastive loss (SimCLR) performs significantly better than supervised pre-training for both neuro injury and cry triggers. In addition, we demonstrate further performance gains through SSL-based domain adaptation using unlabeled infant cries. We also show that using such SSL-based pre-training for adaptation to cry sounds decreases the need for labeled data of the overall system.

2023-06-04

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)

Posthoc Interpretation via Quantization

Francesco Paissan

In this paper, we introduce a new approach, called Posthoc Interpretation via Quantization (PIQ), for interpreting decisions made by trained… (voir plus) classifiers. Our method utilizes vector quantization to transform the representations of a classifier into a discrete, class-specific latent space. The class-specific codebooks act as a bottleneck that forces the interpreter to focus on the parts of the input data deemed relevant by the classifier for making a prediction. Our model formulation also enables learning concepts by incorporating the supervision of pretrained annotation models such as state-of-the-art image segmentation models. We evaluated our method through quantitative and qualitative studies involving black-and-white images, color images, and audio. As a result of these studies we found that PIQ generates interpretations that are more easily understood by participants to our user studies when compared to several other interpretation methods in the literature.

2023-03-22

ArXiv (prépublication)