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FSD50K: An Open Dataset of Human-Labeled Sound Events
TLDR
FSD50K is introduced, an open dataset containing over 51 k audio clips totalling over 100 h of audio manually labeled using 200 classes drawn from the AudioSet Ontology, to provide an alternative benchmark dataset and thus foster SER research.
View on IEEE
Freesound Datasets: A Platform for the Creation of Open Audio Datasets
Comunicacio presentada al 18th International Society for Music Information Retrieval Conference celebrada a Suzhou, Xina, del 23 al 27 d'cotubre de 2017.
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General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
TLDR
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.
View PDF on arXiv
Learning Sound Event Classifiers from Web Audio with Noisy Labels
TLDR
Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data, and it is shown that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.
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Audio tagging with noisy labels and minimal supervision
TLDR
This paper presents the task setup, the FSDKaggle2019 dataset prepared for this scientific evaluation, and a baseline system consisting of a convolutional neural network.
View PDF on arXiv
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
TLDR
This work proposes a simple and model-agnostic method based on a teacher-student framework with loss masking to first identify the most critical missing label candidates, and then ignore their contribution during the learning process, finding that a simple optimisation of the training label set improves recognition performance without additional computation.
View on IEEE
Unsupervised Contrastive Learning of Sound Event Representations
TLDR
This work proposes to use the pretext task of contrasting differently augmented views of sound events to suggest that unsupervised contrastive pre-training can mitigate the impact of data scarcity and increase robustness against noisy labels.
View on IEEE
Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks
Comunicacio presentada al Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), celebrat el dia 16 de novembre de 2017 a Munic, Alemanya.
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What’s all the Fuss about Free Universal Sound Separation Data?
TLDR
An open-source baseline separation model that can separate a variable number of sources in a mixture is introduced, based on an improved time-domain convolutional network (TDCN++), that achieves scale-invariant signal-to-noise ratio improvement (SI-SNRi) on mixtures with two to four sources.
View on IEEE
The Benefit of Temporally-Strong Labels in Audio Event Classification
TLDR
It is shown that fine-tuning with a mix of weak- and strongly-labeled data can substantially improve classifier performance, even when evaluated using only the original weak labels.
View on IEEE
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