59 citations · 182 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Comparison of semi-supervised deep learning algorithms for audio classification
Léo Cances, Etienne Labbé, Thomas Pellegrini
In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two col…
Cosine-similarity penalty to discriminate sound classes in weakly-supervised sound event detection
Thomas Pellegrini, Léo Cances
The design of new methods and models when only weakly-labeled data are available is of paramount importance in order to reduce the costs of manual annotation and the considerable h…
The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection
Thomas Pellegrini, Jérôme Farinas, Estelle Delpech +1
In this paper, we describe the outcomes of the challenge organized and run by Airbus and partners in 2018. The challenge consisted of two tasks applied to Air Traffic Control (ATC)…
Densely Connected CNNs for Bird Audio Detection
Thomas Pellegrini
Detecting bird sounds in audio recordings automatically, if accurate enough, is expected to be of great help to the research community working in bio- and ecoacoustics, interested…