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Datasets:
ccmusic-database
/
instrument_timbre

Tasks:
Audio Classification
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Audio
Image
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Languages:
Chinese
English
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Tags:
music
art
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 ---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Musical Instruments Timbre Evaluation Database
size_categories:
- n<1K
dataset_info:
- config_name: default
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: mel
dtype: image
- name: instrument
dtype:
class_label:
names:
'0': gao_hu
'1': er_hu
'2': zhong_hu
'3': ge_hu
'4': di_yin_ge_hu
'5': jing_hu
'6': ban_hu
'7': bang_di
'8': qu_di
'9': xin_di
'10': da_di
'11': gao_yin_sheng
'12': zhong_yin_sheng
'13': di_yin_sheng
'14': gao_yin_suo_na
'15': zhong_yin_suo_na
'16': ci_zhong_yin_suo_na
'17': di_yin_suo_na
'18': gao_yin_guan
'19': zhong_yin_guan
'20': di_yin_guan
'21': bei_di_yin_guan
'22': ba_wu
'23': xun
'24': xiao
'25': liu_qin
'26': xiao_ruan
'27': pi_pa
'28': yang_qin
'29': zhong_ruan
'30': da_ruan
'31': gu_zheng
'32': gu_qin
'33': kong_hou
'34': san_xian
'35': yun_luo
'36': bian_zhong
'37': violin
'38': viola
'39': cello
'40': double_bass
'41': piccolo
'42': flute
'43': oboe
'44': clarinet
'45': bassoon
'46': saxophone
'47': trumpet
'48': trombone
'49': horn
'50': tuba
'51': harp
'52': tubular_bells
'53': bells
'54': xylophone
'55': vibraphone
'56': marimba
'57': piano
'58': clavichord
'59': accordion
'60': organ
- name: slim
dtype: float32
- name: bright
dtype: float32
- name: dark
dtype: float32
- name: sharp
dtype: float32
- name: thick
dtype: float32
- name: thin
dtype: float32
- name: vigorous
dtype: float32
- name: silvery
dtype: float32
- name: raspy
dtype: float32
- name: full
dtype: float32
- name: coarse
dtype: float32
- name: pure
dtype: float32
- name: hoarse
dtype: float32
- name: consonant
dtype: float32
- name: mellow
dtype: float32
- name: muddy
dtype: float32
splits:
- name: Chinese
num_bytes: 15902
num_examples: 37
- name: Western
num_bytes: 10308
num_examples: 24
download_size: 106658464
dataset_size: 26210
configs:
- config_name: default
data_files:
- split: Chinese
path: default/Chinese/data-*.arrow
- split: Western
path: default/Western/data-*.arrow
---
# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database
The original dataset is sourced from the [National Musical Instruments Timbre Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou4), 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](#usage) 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](https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre/dataPeview).
## 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
```python
from datasets import load_dataset
ds = load_dataset("ccmusic-database/instrument_timbre")
for item in ds["Chinese"]:
print(item)
for item in ds["Western"]:
print(item)
```
## Maintenance
```bash
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.](https://www.mdpi.com/2076-3417/10/3/789)
### Citation Information
```bibtex
@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