4 citations · 5 across the 9 of their papers we have counts for
10 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-…
Hierarchical Label Propagation: A Model-Size-Dependent Performance Booster for AudioSet Tagging
Ludovic Tuncay, Etienne Labbé, Thomas Pellegrini
AudioSet is one of the most used and largest datasets in audio tagging, containing about 2 million audio samples that are manually labeled with 527 event categories organized into…
Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning
Lucas Block Medin, Thomas Pellegrini, Lucile Gelin
Child speech recognition is still an underdeveloped area of research due to the lack of data (especially on non-English languages) and the specific difficulties of this task. Havin…
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space
Sebastião Quintas, Isabelle Ferrané, Thomas Pellegrini
The use of synthetic speech as data augmentation is gaining increasing popularity in fields such as automatic speech recognition and speech classification tasks. Despite novel text…
CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor Cycle System
Justin Reverdi, Sixin Zhang, Saïd Aoues +3
In Vapor Cycle Systems, the mass flow sensor playsa key role for different monitoring and control purposes. However,physical sensors can be inaccurate, heavy, cumbersome, expensive…
Multilingual Audio Captioning using machine translated data
Matéo Cousin, Étienne Labbé, Thomas Pellegrini
Automated Audio Captioning (AAC) systems attempt to generate a natural language sentence, a caption, that describes the content of an audio recording, in terms of sound events. Exi…
Audio classification with Dilated Convolution with Learnable Spacings
Ismail Khalfaoui-Hassani, Timothée Masquelier, Thomas Pellegrini
Dilated convolution with learnable spacings (DCLS) is a recent convolution method in which the positions of the kernel elements are learned throughout training by backpropagation.…
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…