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
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