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 2 M tracks from YouTube videos and …
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …
Panns: Large-scale pretrained audio neural networks for audio pattern recognition
Audio pattern recognition is an important research topic in the machine learning area, and
includes several tasks such as audio tagging, acoustic scene classification, music …
includes several tasks such as audio tagging, acoustic scene classification, music …
The internet of audio things: State of the art, vision, and challenges
The Internet of Audio Things (IoAuT) is an emerging research field positioned at the
intersection of the Internet of Things, sound and music computing, artificial intelligence, and …
intersection of the Internet of Things, sound and music computing, artificial intelligence, and …
Anomalous sound detection with machine learning: A systematic review
EC Nunes - arXiv preprint arXiv:2102.07820, 2021 - arxiv.org
Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from
an object is normal or anomalous. In some cases, early detection of this anomaly can …
an object is normal or anomalous. In some cases, early detection of this anomaly can …
Cough against covid: Evidence of covid-19 signature in cough sounds
Testing capacity for COVID-19 remains a challenge globally due to the lack of adequate
supplies, trained personnel, and sample-processing equipment. These problems are even …
supplies, trained personnel, and sample-processing equipment. These problems are even …
Strong labeling of sound events using crowdsourced weak labels and annotator competence estimation
I Martín-Morató, A Mesaros - IEEE/ACM transactions on audio …, 2023 - ieeexplore.ieee.org
Crowdsourcing is a popular tool for collecting large amounts of annotated data, but the
specific format of the strong labels necessary for sound event detection is not easily …
specific format of the strong labels necessary for sound event detection is not easily …
Polyphonic sound event detection and localization using a two-stage strategy
Sound event detection (SED) and localization refer to recognizing sound events and
estimating their spatial and temporal locations. Using neural networks has become the …
estimating their spatial and temporal locations. Using neural networks has become the …
Learning sound event classifiers from web audio with noisy labels
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 …
inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but …
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 …
few manually labeled data but abundant unlabeled data—a common scenario in sound …
Sound event detection and time–frequency segmentation from weakly labelled data
Sound event detection (SED) aims to detect when and recognize what sound events happen
in an audio clip. Many supervised SED algorithms rely on strongly labelled data that …
in an audio clip. Many supervised SED algorithms rely on strongly labelled data that …