Added dataset and LFS
Browse files- .gitattributes +1 -0
- BaDumTss.pdf +3 -0
- LICENSE +21 -0
- README.md +13 -3
- dataset/dataset.tar.gz +3 -0
- dataset/metadata.csv +0 -0
.gitattributes
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BaDumTss.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:d30cac7d06f0a797cb55e32419dec1c2fc1129e58b915675ff71c58f9cfa2e8d
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size 1484072
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LICENSE
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MIT License
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Copyright (c) 2023 Max Ardito
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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# Beatbox Dataset
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Dataset consisting of isolated beatbox samples from the paper **[BaDumTss: Multi-task
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Learning for Beatbox
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Transcription](https://link.springer.com/chapter/10.1007/978-3-031-05981-0_14])**
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## Citations
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Mehta, P., Maheshwari, M., Joshi, B., Chakraborty, T. (2022). BaDumTss: Multi-task
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Learning for Beatbox Transcription. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y.,
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Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes
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in Computer Science(), vol 13282. Springer, Cham.
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https://doi.org/10.1007/978-3-031-05981-0_14
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dataset/dataset.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3157ac299c65cedab3d99e4be2985aeb485684645a74c489e4cafa3d7e5036c
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size 141042004
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dataset/metadata.csv
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