1 citations · 1 across the 4 of their papers we have counts for
5 papers
BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations
Ludovic K. Tuncay, Etienne Labbé, Thomas Pellegrini
Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that retains frozen randomprojection-ba…
Audio-JEPA: Joint-Embedding Predictive Architecture for Audio Representation Learning
Ludovic Tuncay, Etienne Labbé, Emmanouil Benetos +1
Building on the Joint-Embedding Predictive Architecture (JEPA) paradigm, a recent self-supervised learning framework that predicts latent representations of masked regions in high-…
Adapting a ConvNeXt model to audio classification on AudioSet
Thomas Pellegrini, Ismail Khalfaoui-Hassani, Etienne Labbé +1
In computer vision, convolutional neural networks (CNN) such as ConvNeXt, have been able to surpass state-of-the-art transformers, partly thanks to depthwise separable convolutions…
Multitask learning in Audio Captioning: a sentence embedding regression loss acts as a regularizer
Etienne Labbé, Julien Pinquier, Thomas Pellegrini
In this work, we propose to study the performance of a model trained with a sentence embedding regression loss component for the Automated Audio Captioning task. This task aims to…
Is my automatic audio captioning system so bad? spider-max: a metric to consider several caption candidates
Etienne Labbé, Thomas Pellegrini, Julien Pinquier
Automatic Audio Captioning (AAC) is the task that aims to describe an audio signal using natural language. AAC systems take as input an audio signal and output a free-form text sen…