116 citations · 169 across the 9 of their papers we have counts for
12 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…
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal +4
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help…
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…
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,…
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…
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
Eduardo Fonseca, Manoj Plakal, Frederic Font +4
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platfo…
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…
CNN Architectures for Large-Scale Audio Classification
Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis +10
Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of…