5 citations · 5 across the 6 of their papers we have counts for
6 papers
It Takes Few to TANGO: A Quantized Distributed Model for Binaural Speech Enhancement
Zahra Benslimane, Pierre Chouteau, Martyna Poreba +4
Neural network-based multichannel speech enhancement systems achieve strong enhancement performance, but their computational and memory requirements limit deployment on resource-co…
RT-Tango: Real-Time Distributed Binaural Speech Enhancement for Low-Power Hearing Aid Devices
Z. Benslimane, P. Chouteau, M. Poreba +4
Real-time binaural speech enhancement is constrained by latency, computational cost, and inter-device communication, yet existing efficient solutions predominantly address single-c…
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…
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…
A fully differentiable model for unsupervised singing voice separation
Gael Richard, Pierre Chouteau, Bernardo Torres
A novel model was recently proposed by Schulze-Forster et al. in [1] for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existin…