1 citations · 1 across the 6 of their papers we have counts for
8 papers
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…
Unlocking Spatial Grounding in Large Audio-Visual Retrieval models
Hugo Malard, Michel Olvera, Sanjeel Parekh +3
Weak supervision sets a practical regime for audio-visual sound source localization as dense spatial annotations are costly to obtain at scale. The task, however, remains challengi…
Melody-Lyrics Matching with Contrastive Alignment Loss
Changhong Wang, Michel Olvera, Gaël Richard
The connection between music and lyrics is far beyond semantic bonds. Conceptual pairs in the two modalities such as rhythm and rhyme, note duration and syllabic stress, and struct…
TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization
Hugo Malard, Michel Olvera, Stephane Lathuiliere +1
Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of applications. Here, we tackle the spe…
An Eye for an Ear: Zero-shot Audio Description Leveraging an Image Captioner using Audiovisual Distribution Alignment
Hugo Malard, Michel Olvera, Stéphane Lathuiliere +1
Multimodal large language models have fueled progress in image captioning. These models, fine-tuned on vast image datasets, exhibit a deep understanding of semantic concepts. In th…
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…