1 citations · 1 across the 4 of their papers we have counts for
7 papers
Exposing the Cost of Deep Learning Audio Development
Constance Douwes, Paul Magron, Romain Serizel
The environmental impact of deep learning has attracted increasing attention over the past decade. Existing studies mainly focus on the energy and carbon emissions of model trainin…
Investigating the Performance and Energy Costs of Replicating Band-Split RNN for Music Source Separation
Paul Magron, Romain Serizel, Constance Douwes
Band-split recurrent neural network (BSRNN) is a popular music source separation model that yields close to state-of-the-art results using reasonable computational resources and pu…
Energy Consumption Trends in Sound Event Detection Systems
Constance Douwes, Romain Serizel
Deep learning systems have become increasingly energy- and computation-intensive, raising concerns about their environmental impact. As organizers of the Detection and Classificati…
Normalizing Energy Consumption for Hardware-Independent Evaluation
Constance Douwes, Romain Serizel
The increasing use of machine learning (ML) models in signal processing has raised concerns about their environmental impact, particularly during resource-intensive training phases…
From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems
Constance Douwes, Romain Serizel
The massive use of machine learning models, particularly neural networks, has raised serious concerns about their environmental impact. Indeed, over the last few years we have seen…
Latent Watermarking of Audio Generative Models
Robin San Roman, Pierre Fernandez, Antoine Deleforge +2
The advancements in audio generative models have opened up new challenges in their responsible disclosure and the detection of their misuse. In response, we introduce a method to w…
A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms
Nasser-Eddine Monir, Paul Magron, Romain Serizel
In the intricate acoustic landscapes where speech intelligibility is challenged by noise and reverberation, multichannel speech enhancement emerges as a promising solution for indi…