5 citations · 5 across the 5 of their papers we have counts for
5 papers
S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models
Mohammed Ali El Adlouni, Aurian Quelennec, Pierre Chouteau +2
General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state-of-the-art models remain extremely larg…
TinyMU: A Compact Audio-Language Model for Music Understanding
Xiquan Li, Aurian Quelennec, Slim Essid
Music understanding and reasoning are central challenges in the Music Information Research field, with applications ranging from retrieval and recommendation to music agents and vi…
MATPAC++: Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters +1
Masked latent prediction has emerged as a leading paradigm in self-supervised learning (SSL), especially for general audio and music representation learning. While recent methods h…
Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters +1
Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned l…
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…