Datasetpublic

confit/fsdkaggle2019-parquet

FSDKaggle2019 FSDKaggle2019[1] is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019. All audio clips are provided as uncompressed PCM 16 bit, 44.1 kHz, mono audio files. This version of database could be found and downloaded from here. Data Split Statistics Curated Noisy… See the full description on the dataset page: https://huggingface.co/datasets/confit/fsdkaggle2019-parquet.

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Dataset Card

FSDKaggle2019

FSDKaggle2019<sup>[1]</sup> is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019. All audio clips are provided as uncompressed PCM 16 bit, 44.1 kHz, mono audio files. This version of database could be found and downloaded from here.

Data Split Statistics

	Curated	Noisy	Test
Number of clips/class	75	300	50 ~ 100
Total number of clips	4,970	19,815	4,481
Average number of labels/clip	1.2	1.2	1.4
Total durations	10.5 hours	80 hours	12.9 hours
Label quality	Correct but potentially imcomplete	noisy labels	correct and complete labels
Sources	FSD	YFCC	FSD

Citations

[1] Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Serra. "Audio tagging with noisy labels and minimal supervision". Proceedings of the DCASE 2019 Workshop, NYC, US (2019)

[2] Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, "Freesound Datasets: A Platform for the Creation of Open Audio Datasets", In Proceedings of the 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017