50 citations · 74 across the 9 of their papers we have counts for
10 papers
CoNeTTE: An efficient Audio Captioning system leveraging multiple datasets with Task Embedding
Étienne Labbé, Thomas Pellegrini, Julien Pinquier
Automated Audio Captioning (AAC) involves generating natural language descriptions of audio content, using encoder-decoder architectures. An audio encoder produces audio embeddings…
Killing two birds with one stone: Can an audio captioning system also be used for audio-text retrieval?
Etienne Labbé, Thomas Pellegrini, Julien Pinquier
Automated Audio Captioning (AAC) aims to develop systems capable of describing an audio recording using a textual sentence. In contrast, Audio-Text Retrieval (ATR) systems seek to…
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…
Audio-video fusion strategies for active speaker detection in meetings
Lionel Pibre, Francisco Madrigal, Cyrille Equoy +4
Meetings are a common activity in professional contexts, and it remains challenging to endow vocal assistants with advanced functionalities to facilitate meeting management. In thi…
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…
Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data
Vincent Roger, Jérôme Farinas, Julien Pinquier
Most state-of-the-art speech systems are using Deep Neural Networks (DNNs). Those systems require a large amount of data to be learned. Hence, learning state-of-the-art frameworks…
Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension
Geoffrey Roman-Jimenez, Patrice Guyot, Thierry Malon +7
This paper addresses the problem of vehicle re-identification using distance comparison of images in CNN latent spaces. Firstly, we study the impact of the distance metrics, compar…
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
Svebor Karaman, Jenny Benois-Pineau, Vladislavs Dovgalecs +5
This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recor…
Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics
Svebor Karaman, Jenny Benois-Pineau, Jean-François Dartigues +3
This paper presents a method for indexing human ac- tivities in videos captured from a wearable camera being worn by patients, for studies of progression of the dementia diseases.…