ccmusic-database/Guzheng_Tech99

Dataset Card for Guzheng Technique 99 Dataset

The original dataset, sourced from Guzheng_Tech99, encompasses 99 solo compositions for the guzheng, recorded by professional musicians in a studio environment, with a cumulative duration of 9,064.6 seconds. Each composition has been annotated for every note, indicating the onset, offset, pitch, and playing techniques, which include chanyin, boxian, shanghua, xiahua, huazhi\guazou\lianmo\liantuo, yaozhi, and dianyin. This meticulous annotation has resulted in a total of 63,352 annotated labels across the dataset.

Based on the above original data, we performed data processing and constructed the default subset of the current integrated version of the dataset, and the details of its data structure can be viewed through the viewer. In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88×258×1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7×258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the eval subset, which is not embodied in our paper.

Viewer

https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/dataPeview

Dataset Structure

Default Subset

audio	mel	label
.flac, 44100Hz	.jpg, 44100Hz	{onset_time : float64, offset_time : float, IPT : 7-class, note : int8}
...	...	...

Eval Subset

data(logCQT spectrogram)	label
float64, 88 x 258 x 1	float64, 7 x 258
...	...

Data Instances

.zip(.flac, .csv)

Data Fields

The dataset comprises 99 Guzheng solo compositions, recorded by professionals in a studio, totaling 9064.6 seconds. It includes seven playing techniques labeled for each note (onset, offset, pitch, vibrato, point note, upward portamento, downward portamento, plucks, glissando, and tremolo), resulting in 63,352 annotated labels. The dataset is divided into 79, 10, and 10 songs for the training, validation, and test sets, respectively.

Data Splits

train, validation, test

Dataset Summary

The integrated version provides the original content and the spectrogram generated in the experimental part of the paper cited above. For the second part, the pre-process in the paper is replicated. Each audio clip is a 3-second segment sampled at 44,100Hz, which is subsequently converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with the dimension of 88 × 258 × 1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7 × 258, which indicates the presence of seven distinct techniques across each time frame. indicating the existence of the seven techniques in each time frame. In the end, given that the raw dataset has already been split into train, valid, and test sets, the integrated dataset maintains the same split method. This dataset can be used for frame-level guzheng playing technique detection.

Supported Tasks and Leaderboards

MIR, audio classification

Languages

Chinese, English

Usage

Default Subset

from datasets import load_dataset

ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="default", split="train")
for item in ds:
    print(item)

Eval Subset

from datasets import load_dataset

ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="eval")
for item in ds["train"]:
    print(item)

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

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

Maintenance

git clone [email protected]:datasets/ccmusic-database/Guzheng_Tech99
cd Guzheng_Tech99

Dataset Creation

Curation Rationale

Instrument playing technique (IPT) is a key element of musical presentation.

Source Data

Initial Data Collection and Normalization

Dichucheng Li, Monan Zhou

Who are the source language producers?

Students from FD-LAMT

Annotations

Annotation process

Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.

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

Dichucheng Li, Mingjin Che, Wenwu Meng, Yulun Wu, Yi Yu, Fan Xia and Wei Li. "Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023).

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