116 citations · 236 across the 15 of their papers we have counts for
4 papers · 1 filter
Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers
Eduardo Fonseca, Frederic Font, Xavier Serra
Label noise is emerging as a pressing issue in sound event classification. This arises as we move towards larger datasets that are difficult to annotate manually, but it is even mo…
A hybrid parametric-deep learning approach for sound event localization and detection
Andres Perez-Lopez, Eduardo Fonseca, Xavier Serra
This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric sp…
Audio tagging with noisy labels and minimal supervision
Eduardo Fonseca, Manoj Plakal, Frederic Font +2
This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound…
Learning Sound Event Classifiers from Web Audio with Noisy Labels
Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis +3
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but…