ccmusic-database/Guzheng_Tech99

Dataset Card for Guzheng Technique 99 Dataset

The raw dataset encompasses 99 solo compositions for guzheng, recorded by professional musicians within a studio environment, amounting to a cumulative duration of 9,064.6 seconds. Each composition is annotated for every note, indicating the onset, offset, pitch and playing techniques, and the techniques included are chanyin, boxian, shanghua, xiahua, huazhi\guazou\lianmo\liantuo, yaozhi, and dianyin. This meticulous annotation results in a total of 63,352 annotated labels across the dataset. This dataset is different from the GZ IsoTech dataset introduced earlier; the annotations in this dataset were made at the note level for the entire recording, whereas the previous dataset had annotations made for each audio clip.

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

Eval Subset

from datasets import load_dataset

dataset = 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)

Raw Subset

from datasets import load_dataset

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

Maintenance

GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/Guzheng_Tech99
cd Guzheng_Tech99

Dataset Structure

Raw Subset

audio(.wav, 22050Hz)	mel(.jpg, 22050Hz)	label
		{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, valid, test

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).

Licensing Information

MIT License

Copyright (c) FD-LAMT

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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