2 citations · 2 across the 3 of their papers we have counts for
6 papers · 1 filter
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning
Antonin Gagnere, Geoffroy Peeters, Slim Essid
In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspiration from the Contra…
Less Forgetting for Better Generalization: Exploring Continual-learning Fine-tuning Methods for Speech Self-supervised Representations
Salah Zaiem, Titouan Parcollet, Slim Essid
Despite being trained on massive and diverse datasets, speech self-supervised encoders are generally used for downstream purposes as mere frozen feature extractors or model initial…
SAMbA: Speech enhancement with Asynchronous ad-hoc Microphone Arrays
Nicolas Furnon, Romain Serizel, Slim Essid +1
Speech enhancement in ad-hoc microphone arrays is often hindered by the asynchronization of the devices composing the microphone array. Asynchronization comes from sampling time of…
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations
Salah Zaiem, Titouan Parcollet, Slim Essid
Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datas…
Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?
Salah Zaiem, Youcef Kemiche, Titouan Parcollet +2
Self-supervised learning (SSL) has recently allowed leveraging large datasets of unlabeled speech signals to reach impressive performance on speech tasks using only small amounts o…
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative Study
Salah Zaiem, Robin Algayres, Titouan Parcollet +2
Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrate…