116 citations · 236 across the 15 of their papers we have counts for
18 papers · 1 filter
AudioFM: Towards General Purpose Audio Representation Learning
Yamin Li, Ruidong Zhang, Anran Wang +19
Learning audio representations that effectively bridge holistic global soundscapes with fine-grained acoustic moments is a long-standing goal in self-supervised learning. Recording…
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…
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…
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…
Unsupervised Contrastive Learning of Sound Event Representations
Eduardo Fonseca, Diego Ortego, Kevin McGuinness +2
Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound ev…
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…
Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes
Nicolas Turpault, Romain Serizel, Scott Wisdom +5
We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We anal…
FSD50K: An Open Dataset of Human-Labeled Sound Events
Eduardo Fonseca, Xavier Favory, Jordi Pons +2
Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2M tracks from YouTube videos an…
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Nicolas Turpault, Scott Wisdom, Hakan Erdogan +5
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or nois…
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,…
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…
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…
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
Eduardo Fonseca, Rong Gong, Xavier Serra
In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data drive…