Search Results for author:
Found 18 papers, 12 papers with code
Targeting of chondrocyte plasticity via connexin43 modulation attenuates cellular senescence and fosters a pro-regenerative environment in osteoarthritis
These findings support the use of Cx43 as an appropriate therapeutic target to halt OA progression and to promote cartilage regeneration.
Dataset balancing can hurt model performance
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while balancing improves performance on the public AudioSet evaluation data it simultaneously hurts performance on an unpublished evaluation set collected under the same conditions.
Audiovisual Masked Autoencoders
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)
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
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.
HEAR: Holistic Evaluation of Audio Representations
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.
Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks
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.
Self-Supervised Learning from Automatically Separated Sound Scenes
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.
Unsupervised Contrastive Learning of Sound Event Representations
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.
What's All the FUSS About Free Universal Sound Separation Data?
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.
FSD50K: An Open Dataset of Human-Labeled Sound Events
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.
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets.
Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers
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.
A hybrid parametric-deep learning approach for sound event localization and detection
This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge.
Audio tagging with noisy labels and minimal supervision
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.
Learning Sound Event Classifiers from Web Audio with Noisy Labels
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.
Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections.
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
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.
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
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.
