Update README.md

README.md

@@ -1,3 +1,124 @@
 ---
 license: mit
+task_categories:
+- audio-classification
+language:
+- zh
+- en
+tags:
+- music
+- art
+pretty_name: Guzheng Technique 99 Dataset
+size_categories:
+- n<1K
 ---
+# Dataset Card for Guzheng Technique 99 Dataset
+## Dataset Description
+- **Homepage:** <https://ccmusic-database.github.io>
+- **Repository:** <https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99>
+- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
+- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
+- **Point of Contact:** <https://github.com/LiDCC/GuzhengTech99/tree/windows>
+
+### Dataset Summary
+Instrument playing technique (IPT) is a key element of musical presentation.
+
+Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.
+
+The dataset comprises 99 Guzheng solo compositions, recorded by professionals in a studio, totaling 9064.6 seconds. It includes seven playing techniques labeled for each note (onset, offset, pitch, vibrato, point note, upward portamento, downward portamento, plucks, glissando, and tremolo), resulting in 63,352 annotated labels. The dataset is divided into 79, 10, and 10 songs for the training, validation, and test sets, respectively.
+
+### Supported Tasks and Leaderboards
+MIR, audio classification
+
+### Languages
+Chinese, English
+
+## Dataset Structure
+### Data Instances
+.zip(.flac, .csv)
+
+### Data Fields
+The dataset comprises 99 Guzheng solo compositions, recorded by professionals in a studio, totaling 9064.6 seconds. It includes seven playing techniques labeled for each note (onset, offset, pitch, vibrato, point note, upward portamento, downward portamento, plucks, glissando, and tremolo), resulting in 63,352 annotated labels. The dataset is divided into 79, 10, and 10 songs for the training, validation, and test sets, respectively.
+
+### Data Splits
+train, valid, test
+
+## Dataset Creation
+### Curation Rationale
+Instrument playing technique (IPT) is a key element of musical presentation.
+
+### Source Data
+#### Initial Data Collection and Normalization
+Dichucheng Li, Monan Zhou
+
+#### Who are the source language producers?
+Students from FD-LAMT
+
+### Annotations
+#### Annotation process
+Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.
+
+#### Who are the annotators?
+Students from FD-LAMT
+
+### Personal and Sensitive Information
+None
+
+## Considerations for Using the Data
+### Social Impact of Dataset
+Promoting the development of music AI industry
+
+### Discussion of Biases
+Only for Traditional Chinese Instruments
+
+### Other Known Limitations
+Insufficient sample 
+
+## Additional Information
+### Dataset Curators
+Dichucheng Li
+
+### Evaluation
+[Dichucheng Li, Mingjin Che, Wenwu Meng, Yulun Wu, Yi Yu, Fan Xia and Wei Li. "Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023).](https://arxiv.org/pdf/2303.13272.pdf)
+
+### Licensing Information
+```
+MIT License
+
+Copyright (c) FD-LAMT
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+### Citation Information
+```
+@dataset{zhaorui_liu_2021_5676893,
+  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Wei Li, Zhaowen Wang and Zijin Li},
+  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
+  month        = {nov},
+  year         = {2021},
+  publisher    = {Zenodo},
+  version      = {1.1},
+  doi          = {10.5281/zenodo.5676893},
+  url          = {https://doi.org/10.5281/zenodo.5676893}
+}
+```
+
+### Contributions
+Promoting the development of music AI industry