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--- |
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task_categories: |
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- audio-classification |
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size_categories: |
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- 100B<n<1T |
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--- |
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# VGGSound |
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VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube. |
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- **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/vggsound/ |
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- **Paper:** https://arxiv.org/abs/2004.14368 |
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- **Github:** https://github.com/hche11/VGGSound |
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## Analysis |
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- **310+ classes:** VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. |
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- **200,000+ videos:** All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident. |
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- **550+ hours:** VGG-Sound consists of both audio and video. Each segment is 10 seconds long. |
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## Download |
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We provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here. |
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``` |
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# YouTube ID, start seconds, label, train/test split. |
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``` |
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And you can download VGGSound directly from this [repository](https://huggingface.co/datasets/Loie/VGGSound/tree/main). |
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## License |
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The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found [here](https://thor.robots.ox.ac.uk/datasets/vggsound/license_vggsound.txt). |
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## Citation |
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Please cite the following if you make use of the dataset. |
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``` |
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@InProceedings{Chen20, |
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author = "Honglie Chen and Weidi Xie and Andrea Vedaldi and Andrew Zisserman", |
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title = "VGGSound: A Large-scale Audio-Visual Dataset", |
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booktitle = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", |
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year = "2020", |
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} |
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``` |
