README.md

---
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://huggingface.co/datasets/ccmusic-database/instrument_timbre/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
```python
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
```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