36 citations · 114 across the 10 of their papers we have counts for
21 papers
Recomposer: Event-roll-guided generative audio editing
Daniel P. W. Ellis, Eduardo Fonseca, Ron J. Weiss +7
Editing complex real-world sound scenes is difficult because individual sound sources overlap in time. Generative models can fill-in missing or corrupted details based on their str…
Dataset balancing can hurt model performance
R. Channing Moore, Daniel P. W. Ellis, Eduardo Fonseca +3
Machine learning from training data with a skewed distribution of examples per class can lead to models that favor performance on common classes at the expense of performance on ra…
Audiovisual Masked Autoencoders
Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu +3
Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pretraining archi…
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
Francesca Ronchini, Samuele Cornell, Romain Serizel +3
The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using a…
HEAR: Holistic Evaluation of Audio Representations
Joseph Turian, Jordie Shier, Humair Raj Khan +20
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to dev…
Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks
Eduardo Fonseca, Andres Ferraro, Xavier Serra
Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predict…