23 citations · 28 across the 4 of their papers we have counts for
6 papers
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
Eduardo Fonseca, Shawn Hershey, Manoj Plakal +4
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets. This work addresses the problem of missing labels,…
On Neural Phone Recognition of Mixed-Source ECoG Signals
Ahmed Hussen Abdelaziz, Shuo-Yiin Chang, Nelson Morgan +5
The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech…
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision
Aren Jansen, Daniel P. W. Ellis, Shawn Hershey +4
Humans do not acquire perceptual abilities in the way we train machines. While machine learning algorithms typically operate on large collections of randomly-chosen, explicitly-lab…
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…
Unsupervised Learning of Semantic Audio Representations
Aren Jansen, Manoj Plakal, Ratheet Pandya +5
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We cons…