272 citations · 589 across the 14 of their papers we have counts for
18 papers
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…
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…
MuLan: A Joint Embedding of Music Audio and Natural Language
Qingqing Huang, Aren Jansen, Joonseok Lee +3
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This pa…
The Benefit Of Temporally-Strong Labels In Audio Event Classification
Shawn Hershey, Daniel P W Ellis, Eduardo Fonseca +4
To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (~0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset.…
Self-Supervised Learning from Automatically Separated Sound Scenes
Eduardo Fonseca, Aren Jansen, Daniel P. W. Ellis +7
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings. The associati…
What's All the FUSS About Free Universal Sound Separation Data?
Scott Wisdom, Hakan Erdogan, Daniel Ellis +6
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound typ…