ccmusic-database/instrument_timbre

Dataset Card for Chinese Musical Instruments Timbre Evaluation Database

The original dataset is sourced from the National Musical Instruments Timbre Evaluation Dataset, which includes subjective timbre evaluation scores using 16 terms such as bright, dark, raspy, etc., evaluated across 37 Chinese instruments and 24 Western instruments by Chinese participants with musical backgrounds in a subjective evaluation experiment. Additionally, it contains 10 spectrogram analysis reports for 10 instruments.

Based on the aforementioned original dataset, after data processing, we have constructed the default subset of the current integrated version of the dataset, dividing the Chinese section and the Western section into two splits. Each split consists of multiple data entries, with each entry structured across 18 columns. The Chinese split includes 37 entries, while the Western split comprises 24 entries. The first column of each data entry presents the instrument recordings in .wav format, sampled at a rate of 44,100 Hz. The second column provides the Chinese pinyin or English name of the instrument. The following 16 columns correspond to the 9-point scores of the 16 terms. This dataset is suitable for conducting timbre analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring. The data structure of the default subset can be viewed in the viewer.

Dataset Structure

<style> .datastructure td { vertical-align: middle !important; text-align: center; } .datastructure th { text-align: center; } </style>

<table class="datastructure"> <tr> <th>audio</th> <th>mel</th> <th>instrument_name</th> <th>slim / bright / ... / raspy (16 colums)</th> </tr> <tr> <td>.wav, 44100Hz</td> <td>.jpg, 44100Hz</td> <td>string</td> <td>float(1-9)</td> </tr> </table>

Data Instances

.zip(.wav), .csv

Data Fields

Chinese instruments / Western instruments

Data Splits

Chinese, Western

Dataset Description

Dataset Summary

During the integration, we have crafted the Chinese part and the Non-Chinese part into two splits. Each split is composed of multiple data entries, with each entry structured across 18 columns. The Chinese split encompasses 37 entries, while the Non-Chinese split includes 24 entries. The premier column of each data entry presents the instrument recordings in the .wav format, sampled at a rate of 22,050 Hz. The second column provides the Chinese pinyin or English name of the instrument. The subsequent 16 columns correspond to the 9-point score of the 16 terms. This dataset is suitable for conducting timber analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring.

Supported Tasks and Leaderboards

Musical Instruments Timbre Evaluation

Languages

Chinese, English

Usage

from datasets import load_dataset

ds = load_dataset(
    "ccmusic-database/instrument_timbre",
    name="default",
    split="Chinese",  # Chinese / Western
    cache_dir="./__pycache__",
)
for i in ds:
    print(i)

Maintenance

GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/instrument_timbre
cd instrument_timbre

Mirror

<https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre>

Dataset Creation

Curation Rationale

Lack of a dataset for musical instruments timbre evaluation

Source Data

Initial Data Collection and Normalization

Zhaorui Liu, Monan Zhou

Annotations

Annotation process

Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms rated by Chinese listeners in a subjective evaluation experiment

Who are the annotators?

Chinese music professionals

Considerations for Using the Data

Social Impact of Dataset

Promoting the development of AI in the music industry

Other Known Limitations

Limited data

Additional Information

Dataset Curators

Zijin Li

Reference & Evaluation

[1] Jiang W, Liu J, Zhang X, Wang S, Jiang Y. Analysis and Modeling of Timbre Perception Features in Musical Sounds. Applied Sciences. 2020; 10(3):789.

Citation Information

@article{Jiang2020AnalysisAM,
  title   = {Analysis and Modeling of Timbre Perception Features in Musical Sounds},
  author  = {Wei Jiang and Jingyu Liu and Xiaoyi Zhang and Shuang Wang and Yujian Jiang},
  journal = {Applied Sciences},
  year    = {2020},
  url     = {https://api.semanticscholar.org/CorpusID:210878781}
}

Contributions

Provide a dataset for musical instruments' timbre evaluation