README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Chinese Traditional Instrument Sound Dataset
size_categories:
- 1K<n<10K
dataset_info:
  - config_name: default
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 44100
      - name: mel
        dtype: image
      - name: label
        dtype: int16
      - name: cname
        dtype: string
      - name: pinyin
        dtype: string
    splits:
      - name: train
        num_bytes: 2337167
        num_examples: 4956
    download_size: 6640960937
    dataset_size: 2337167
  - config_name: eval
    features:
      - name: mel
        dtype: image
      - name: cqt
        dtype: image
      - name: chroma
        dtype: image
      - name: label
        dtype: int16
    splits:
      - name: train
        num_bytes: 18475805
        num_examples: 34630
      - name: validation
        num_bytes: 2247162
        num_examples: 4212
      - name: test
        num_bytes: 2247240
        num_examples: 4212
    download_size: 3443906087
    dataset_size: 22970207
configs:
  - config_name: default
    data_files:
      - split: train
        path: default/train/data-*.arrow
  - config_name: eval
    data_files:
      - split: train
        path: eval/train/data-*.arrow
      - split: validation
        path: eval/validation/data-*.arrow
      - split: test
        path: eval/test/data-*.arrow
---

# Dataset Card for Chinese Traditional Instrument Sound
## Original Content
The original dataset is created by [[1]](https://link.springer.com/content/pdf/10.1007/978-981-13-8707-4_5.pdf), with no evaluation provided. The original CTIS dataset contains recordings from 287 varieties of Chinese traditional instruments, reformed Chinese musical instruments, and instruments from ethnic minority groups. Notably, some of these instruments are rarely encountered by the majority of the Chinese populace. The dataset was later utilized by [[2]](https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/ccs2.12047) for Chinese instrument recognition, where only 78 instruments—approximately one-third of the total instrument classes—were used.

## Integration
We begin by performing data cleaning to remove recordings without specific instrument labels. Additionally, recordings that are not instrumental sounds, such as interview recordings, are removed to enhance usability. 
Finally, instrument categories lacking specific labels are excluded. The filtered dataset contains recordings of 209 types of Chinese traditional musical instruments. Compared to the original 287 instrument types, 78 were removed due to missing instrument labels. Among the remaining instruments, seven have two variants each, and one instrument, Yangqin, has four variants. We treat variants as separate classes, thus 219 labels are included at last.

In the original dataset, the Chinese character label for each instrument was represented by the folder name housing its audio files.
During integration, we add Chinese pinyin label to make the dataset more accessible to researchers who are not familiar in Chinese.
Then, we've reorganized the data into a dictionary with five columns, which includes: audio with a sampling rate of 44,100 Hz, pre-processed mel spectrogram, numerical label, instrument name in Chinese, and instrument name in Chinese pinyin. The provision of mel spectrograms primarily serves to enhance the visualization of the audio in the [viewer](https://huggingface.co/datasets/ccmusic-database/CTIS/viewer). For the remaining datasets, these mel spectrograms will also be included in the integrated data structure. The total data number is 4,956, with a duration of 32.63 hours. The average duration of the recordings is 23.7 seconds.

We have constructed the [default subset](#default-subset) of the current integrated version of the dataset. Building on the default subset, we applied silence removal with a threshold of top_db=40 to the audio files, converting them into mel, CQT, and chroma spectrograms. The audio was then segmented into 2-second clips, with segments shorter than 2 seconds padded using circular padding. This process resulted in the construction of the [eval subset](#eval-subset) for dataset evaluation experiments.

## Statistics
| ![](https://www.modelscope.cn/datasets/ccmusic-database/CTIS/resolve/master/data/ctis_bar.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/CTIS/resolve/master/data/ctis_cls.jpg) |
| :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: |
|                                           **Fig. 1**                                           |                                           **Fig. 2**                                           |

Due to the large number of categories in this dataset, we are unable to provide the audio duration per category and the proportion of audio clips by category, as we have done for the other datasets. Instead, we provide a chart showing the distribution of the number of audio clips across different durations, as shown in **Fig. 1**. A second graph, shown in **Fig. 2**, shows the distribution of instrument categories over various durations. From **Fig. 1**, 3611 clips (73%) are concentrated in the range 0-27.5 s, with a steep drop in the number of samples in longer durations. In **Fig. 2**, about half of the instruments, totaling 117, have a duration of less than 437 seconds, while 102 instruments have a duration greater than this number. After the total duration exceeds 881 seconds, the number of instruments drops sharply. This indicates that the dataset has a certain degree of class imbalance.

|              Statistical items               |        Values         |
| :------------------------------------------: | :-------------------: |
|                 Total count                  |        `4956`         |
|              Total duration(s)               | `117482.75025085056`  |
|               Mean duration(s)               | `23.705155417847124`  |
|               Min duration(s)                | `0.27639583333333334` |
|               Max duration(s)                |  `494.2522902494331`  |
|               Instrument types               |         `209`         |
|                Label Numbers                 |         `219`         |
|              Eval subset total               |        `43054`        |
|    Class with the longest audio duartion     | `中阮 (Zhong1 ruan3)` |
| Class in the longest audio duartion interval |  `箜篌 (Kong1 hou2)`  |

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/CTIS
cd CTIS
```

## Mirror
<https://www.modelscope.cn/datasets/ccmusic-database/CTIS>

## Dataset Description
### Dataset Summary
A dataset of Chinese instrument audio

### Supported Tasks and Leaderboards
MIR, audio classification

### Languages
Chinese, English

### Data Instances
.zip(.wav), .csv

### Data Fields
219 Chinese instruments

### Data Splits
train, validation, test

## Usage
### Default Subset
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/CTIS", name="default", split="train")
for item in dataset:
    print(item)
```

### Eval Subset
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/CTIS", name="eval")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)
```

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
[1] [Liang, Xiaojing et al. “Constructing a Multimedia Chinese Musical Instrument Database.” Lecture Notes in Electrical Engineering (2019): n. pag.](https://link.springer.com/content/pdf/10.1007/978-981-13-8707-4_5.pdf)<br>
[2] [Li, R., & Zhang, Q. (2022). Audio recognition of Chinese traditional instruments based on machine learning. Cogn. Comput. Syst., 4, 108-115.](https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/ccs2.12047)<br>
[3] <https://huggingface.co/ccmusic-database/CTIS><br>

### Citation Information
```bibtex
@inproceedings{10.1007/978-981-13-8707-4_5,
  author    = {Xiaojing Liang and Zijin Li and Jingyu Liu and Wei Li and Jiaxing Zhu and Baoqiang Han},
  booktitle = {Proceedings of the 6th Conference on Sound and Music Technology (CSMT)},
  pages     = {53-60},
  publisher = {Springer Singapore},
  address   = {Singapore},
  title     = {Constructing a Multimedia Chinese Musical Instrument Database},
  year      = {2019}
}
```

### Contributions
An audio dataset for Chinese Instrument