metadata

license: cc-by-nc-nd-4.0
task_categories:
  - audio-classification
language:
  - zh
  - en
tags:
  - music
  - art
pretty_name: GZ_IsoTech Dataset
size_categories:
  - n<1K
viewer: false

Dataset Card for GZ_IsoTech Dataset

The raw dataset, sourced from GZ_IsoTech, 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. 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…).

Based on the aforementioned raw dataset, we conducted data processing to construct the default subset of the current integrated version of the dataset. 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 for the default subset. The data structure of the default subset can be viewed in the viewer. The eval subset was not further constructed as the original dataset had already been cited and used in published articles.

Viewer

https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview

Dataset Structure

audio	mel	label	cname
.wav, 44100Hz	.jpg, 44100Hz	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 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-database/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 [email protected]:datasets/ccmusic-database/GZ_IsoTech
cd GZ_IsoTech

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

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).

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