no code implementations • 1 Feb 2024 • Marta Varela-Eirin, Adrian Varela-Vazquez, Amanda Guitian-Caamano, Carlos Luis Paino, Virginia Mato, Raquel Largo, Trond Aasen, Arantxa Tabernero, Eduardo Fonseca, Mustapha Kandouz, Jose Ramon Caeiro, Alfonso Blanco, Maria D. Mayan
These findings support the use of Cx43 as an appropriate therapeutic target to halt OA progression and to promote cartilage regeneration.
no code implementations • 30 Jun 2023 • R. Channing Moore, Daniel P. W. Ellis, Eduardo Fonseca, Shawn Hershey, Aren Jansen, Manoj Plakal
We find, however, that while balancing improves performance on the public AudioSet evaluation data it simultaneously hurts performance on an unpublished evaluation set collected under the same conditions.
2 code implementations • ICCV 2023 • Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
Can we leverage the audiovisual information already present in video to improve self-supervised representation learning?
Ranked #1 on Audio Classification on EPIC-KITCHENS-100 (using extra training data)
no code implementations • 14 Oct 2022 • Francesca Ronchini, Samuele Cornell, Romain Serizel, Nicolas Turpault, Eduardo Fonseca, Daniel P. W. Ellis
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 an heterogeneous dataset.
3 code implementations • 6 Mar 2022 • Joseph Turian, Jordie Shier, Humair Raj Khan, Bhiksha Raj, Björn W. Schuller, Christian J. Steinmetz, Colin Malloy, George Tzanetakis, Gissel Velarde, Kirk McNally, Max Henry, Nicolas Pinto, Camille Noufi, Christian Clough, Dorien Herremans, Eduardo Fonseca, Jesse Engel, Justin Salamon, Philippe Esling, Pranay Manocha, Shinji Watanabe, Zeyu Jin, Yonatan Bisk
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
1 code implementation • 1 Jul 2021 • 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 predictions substantially.
1 code implementation • 5 May 2021 • Eduardo Fonseca, Aren Jansen, Daniel P. W. Ellis, Scott Wisdom, Marco Tagliasacchi, John R. Hershey, Manoj Plakal, Shawn Hershey, R. Channing Moore, Xavier Serra
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.
1 code implementation • 15 Nov 2020 • Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, Xavier Serra
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 event research.
no code implementations • 2 Nov 2020 • Scott Wisdom, Hakan Erdogan, Daniel Ellis, Romain Serizel, Nicolas Turpault, Eduardo Fonseca, Justin Salamon, Prem Seetharaman, John Hershey
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 types.
8 code implementations • 1 Oct 2020 • Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra
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 and encompassing over 500 sound classes.
no code implementations • 2 May 2020 • Eduardo Fonseca, Shawn Hershey, Manoj Plakal, Daniel P. W. Ellis, Aren Jansen, R. Channing Moore, Xavier Serra
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets.
1 code implementation • 26 Oct 2019 • Eduardo Fonseca, Frederic Font, Xavier Serra
We show that these simple methods can be effective in mitigating the effect of label noise, providing up to 2. 5\% of accuracy boost when incorporated to two different CNNs, while requiring minimal intervention and computational overhead.
1 code implementation • 27 Aug 2019 • 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.
2 code implementations • 7 Jun 2019 • Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Serra
The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes.
2 code implementations • 4 Jan 2019 • Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, Xavier Serra
To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42. 5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.
no code implementations • 21 Nov 2018 • Xavier Favory, Eduardo Fonseca, Frederic Font, Xavier Serra
It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections.
3 code implementations • 26 Jul 2018 • Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra
The goal of the task is to build an audio tagging system that can recognize the category of an audio clip from a subset of 41 diverse categories drawn from the AudioSet Ontology.
2 code implementations • 19 Jun 2018 • Eduardo Fonseca, Rong Gong, Xavier Serra
In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine.