license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: GZ_IsoTech Dataset
size_categories:
- n<1K
Dataset Card for GZ_IsoTech Dataset
The raw dataset comprises 2,824 audio clips showcasing various guzheng playing techniques. Specifically, 2,328 clips were sourced from virtual sound banks, while 496 clips were performed by a skilled professional guzheng artist. These recordings encompass a comprehensive range of tones inherent to the guzheng instrument.
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://www.modelscope.cn/datasets/ccmusic/GZ_IsoTech
- Point of Contact: https://arxiv.org/abs/2209.08774
Dataset Summary
Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach. In contrast to utilizing platform-specific automated splitting mechanisms, we directly employ the pre-split data for subsequent integration steps.
Supported Tasks and Leaderboards
MIR, audio classification
Languages
Chinese, English
Usage
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/GZ_IsoTech")
for item in ds["train"]:
print(item)
for item in ds["test"]:
print(item)
Maintenance
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/GZ_IsoTech
cd GZ_IsoTech
Dataset Structure
| audio(.wav, 22050Hz) | mel(.jpg, 22050Hz) | label | cname |
|---|---|---|---|
 |
8-class | string | |
| ... | ... | ... | ... |
Data Instances
.zip(.flac, .csv)
Data Fields
Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng, the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuo…).
Data Splits
train, test
Dataset Creation
Curation Rationale
The Guzheng is a kind of traditional Chinese instrument 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 do not assure 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.
Source Data
Initial Data Collection and Normalization
Dichucheng Li, Monan Zhou
Who are the source language producers?
Students from FD-LAMT
Annotations
Annotation process
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.
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 the 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 = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
title = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
month = {mar},
year = {2024},
publisher = {HuggingFace},
version = {1.2},
url = {https://huggingface.co/ccmusic-database}
}
Contributions
Promoting the development of the music AI industry