17 citations · 19 across the 4 of their papers we have counts for
9 papers
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…
End-to-end acoustic modelling for phone recognition of young readers
Lucile Gelin, Morgane Daniel, Julien Pinquier +1
Automatic recognition systems for child speech are lagging behind those dedicated to adult speech in the race of performance. This phenomenon is due to the high acoustic and lingui…
Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning
Thomas Pellegrini, Timothée Masquelier
Multi-label audio tagging consists of assigning sets of tags to audio recordings. At inference time, thresholds are applied on the confidence scores outputted by a probabilistic cl…
Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
Thomas Pellegrini, Romain Zimmer, Timothée Masquelier
Deep Neural Networks (DNNs) are the current state-of-the-art models in many speech related tasks. There is a growing interest, though, for more biologically realistic, hardware fri…
Technical report: supervised training of convolutional spiking neural networks with PyTorch
Romain Zimmer, Thomas Pellegrini, Srisht Fateh Singh +1
Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly us…
Evaluation of post-processing algorithms for polyphonic sound event detection
Leo Cances, Patrice Guyot, Thomas Pellegrini
Sound event detection (SED) aims at identifying audio events (audio tagging task) in recordings and then locating them temporally (localization task). This last task ends with the…