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End of preview. Expand
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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 | 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
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