1 citations · 1 across the 6 of their papers we have counts for
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Probing Large Audio-Language Models for Compositional Understanding of Sounding Actions
Michel Olvera, Paraskevas Stamatiadis, Changhong Wang +1
Large audio-language models (LALMs) excel at understanding and reasoning tasks over atomic sound events, yet their ability to infer higher-level human activities from such fine-gra…
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
Michel Olvera, Paraskevas Stamatiadis, Slim Essid
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followe…
SALT: Standardized Audio event Label Taxonomy
Paraskevas Stamatiadis, Michel Olvera, Slim Essid
Machine listening systems often rely on fixed taxonomies to organize and label audio data, key for training and evaluating deep neural networks (DNNs) and other supervised algorith…
On the choice of the optimal temporal support for audio classification with Pre-trained embeddings
Aurian Quelennec, Michel Olvera, Geoffroy Peeters +1
Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of ta…