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--- |
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license: mit |
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task_categories: |
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- audio-classification |
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language: |
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- zh |
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- en |
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tags: |
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- music |
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- art |
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pretty_name: GZ_IsoTech Dataset |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for GZ_IsoTech Dataset |
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## Dataset Description |
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- **Homepage:** <https://ccmusic-database.github.io> |
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- **Repository:** <https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99> |
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- **Paper:** <https://doi.org/10.5281/zenodo.5676893> |
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- **Leaderboard:** <https://ccmusic-database.github.io/team.html> |
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- **Point of Contact:** <https://arxiv.org/abs/2209.08774> |
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### Dataset Summary |
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The Guzheng is a kind of traditional Chinese instruments with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and provide no assurance in the generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1-score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection. |
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This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer. These clips cover almost all the tones in the range of guzheng and the most commonly used playing techniques in guzheng performance. According to the different playing techniques of guzheng, the clips are divided into 8 categories: Vibrato(chanyin), Upward Portamento(shanghuayin), Downward Portamento(xiahuayin), Returning Portamento(huihuayin), Glissando (guazou, huazhi), Tremolo(yaozhi), Harmonic(fanyin), Plucks(gou,da,mo,tuo…). |
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### Supported Tasks and Leaderboards |
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MIR, audio classification |
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### Languages |
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Chinese, English |
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## Dataset Structure |
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### Data Instances |
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.zip(.flac, .csv) |
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### Data Fields |
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This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer. These clips cover almost all the tones in the range of guzheng and the most commonly used playing techniques in guzheng performance. According to the different playing techniques of guzheng, the clips are divided into 8 categories: Vibrato(chanyin), Upward Portamento(shanghuayin), Downward Portamento(xiahuayin), Returning Portamento(huihuayin), Glissando (guazou, huazhi), Tremolo(yaozhi), Harmonic(fanyin), Plucks(gou,da,mo,tuo…). |
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### Data Splits |
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train, valid, test |
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## Dataset Creation |
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### Curation Rationale |
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The Guzheng is a kind of traditional Chinese instruments with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and provide no assurance in the generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1-score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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Dichucheng Li, Monan Zhou |
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#### Who are the source language producers? |
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Students from FD-LAMT |
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### Annotations |
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#### Annotation process |
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This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer. These clips cover almost all the tones in the range of guzheng and the most commonly used playing techniques in guzheng performance. According to the different playing techniques of guzheng, the clips are divided into 8 categories: Vibrato(chanyin), Upward Portamento(shanghuayin), Downward Portamento(xiahuayin), Returning Portamento(huihuayin), Glissando (guazou, huazhi), Tremolo(yaozhi), Harmonic(fanyin), Plucks(gou,da,mo,tuo…). |
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#### Who are the annotators? |
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Students from FD-LAMT |
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### Personal and Sensitive Information |
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None |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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Promoting the development of music AI industry |
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### Discussion of Biases |
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Only for Traditional Chinese Instruments |
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### Other Known Limitations |
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Insufficient sample |
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## Additional Information |
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### Dataset Curators |
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Dichucheng Li |
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### Evaluation |
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[Li, Dichucheng, Yulun Wu, Qinyu Li, Jiahao Zhao, Yi Yu, Fan Xia and Wei Li. “Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance.” International Society for Music Information Retrieval Conference (2022).](https://archives.ismir.net/ismir2022/paper/000037.pdf) |
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### Licensing Information |
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``` |
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MIT License |
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Copyright (c) FD-LAMT |
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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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``` |
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### Citation Information |
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``` |
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@dataset{zhaorui_liu_2021_5676893, |
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author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li}, |
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title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research}, |
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month = {nov}, |
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year = {2021}, |
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publisher = {Zenodo}, |
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version = {1.1}, |
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doi = {10.5281/zenodo.5676893}, |
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url = {https://doi.org/10.5281/zenodo.5676893} |
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} |
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``` |
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### Contributions |
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Promoting the development of music AI industry |
