REVIEW 2 cited by
An open dataset for research on audio field recording archives: freefield1010
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We introduce a free and open dataset of 7690 audio clips sampled from the field-recording tag in the Freesound audio archive. The dataset is designed for use in research related to data mining in audio archives of field recordings / soundscapes. Audio is standardised, and audio and metadata are Creative Commons licensed. We describe the data preparation process, characterise the dataset descriptively, and illustrate its use through an auto-tagging experiment.
Forward citations
Cited by 2 Pith papers
-
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning Study
SSL convolutional audio models pre-trained on speech, non-speech, or both perform nearly equally well across speech and non-speech downstream tasks, while domain-specific baselines struggle outside their domains.
-
autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks
autrainer is a config-driven PyTorch toolkit for computer audition that supports low-code training, preprocessing pipelines, augmentation, and release of pretrained audio models.
Discussion (0). Continue with ORCID to comment.