116 citations · 349 across the 21 of their papers we have counts for
5 papers · 1 filter
Full-band General Audio Synthesis with Score-based Diffusion
Santiago Pascual, Gautam Bhattacharya, Chunghsin Yeh +2
Recent works have shown the capability of deep generative models to tackle general audio synthesis from a single label, producing a variety of impulsive, tonal, and environmental s…
Adversarial Permutation Invariant Training for Universal Sound Separation
Emilian Postolache, Jordi Pons, Santiago Pascual +1
Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models t…
PodcastMix: A dataset for separating music and speech in podcasts
Nicolás Schmidt, Jordi Pons, Marius Miron
We introduce PodcastMix, a dataset formalizing the task of separating background music and foreground speech in podcasts. We aim at defining a benchmark suitable for training and e…
Universal Speech Enhancement with Score-based Diffusion
Joan Serrà, Santiago Pascual, Jordi Pons +2
Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings.…
On loss functions and evaluation metrics for music source separation
Enric Gusó, Jordi Pons, Santiago Pascual +1
We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most represe…