Datasets:
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
- image-classification
language:
- en
tags:
- music
- art
pretty_name: Piano Sound Quality Dataset
size_categories:
- 10K<n<100K
viewer: false
Dataset Card for Piano Sound Quality Dataset
The original dataset is sourced from the Piano Sound Quality Dataset, which includes 12 full-range audio files in .wav/.mp3/.m4a format representing seven models of pianos: Kawai upright piano, Kawai grand piano, Young Change upright piano, Hsinghai upright piano, Grand Theatre Steinway piano, Steinway grand piano, and Pearl River upright piano. Additionally, there are 1,320 split monophonic audio files in .wav/.mp3/.m4a format, bringing the total number of files to 1,332. The dataset also includes a score sheet in .xls format containing subjective evaluations of piano sound quality provided by 29 participants with musical backgrounds.
Based on the aforementioned original dataset, after data processing, we constructed the default subset of the current integrated version of the dataset, and its data structure can be viewed in the viewer. Due to the need to increase the dataset size and the absence of a popular piano brand, Yamaha, the default subset is expanded by recording an upright Yamaha piano into the 8_class subset. Since the current dataset has been validated by published articles, based on the 8_class subset, we adopted the data processing method for dataset evaluation from the article and constructed the eval subset, whose result has been shown in pianos. Except for the default subset, the rest of the subsets are not represented in our paper. Below is a brief introduction to the data structure of each subset.
Dataset Structure
Default Subset
| audio | mel | label (8-class) | pitch (88-class) |
|---|---|---|---|
| .wav, 44100Hz | .jpg, 44100Hz | PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha | 88 pitches on piano |
| ... | ... | ... | ... |
Eval Subset
| mel | label (8-class) | pitch (88-class) |
|---|---|---|
| .jpg, 0.18s 44100Hz | PearlRiver / YoungChang / Steinway-T / Hsinghai / Kawai / Steinway / Kawai-G / Yamaha | 88 pitches on piano |
| ... | ... | ... |
Data Instances
.zip(.wav, jpg)
Data Fields
1_PearlRiver
2_YoungChang
3_Steinway-T
4_Hsinghai
5_Kawai
6_Steinway
7_Kawai-G
8_Yamaha (For Non-default subset)
Data Splits for Eval Subset
| Split | Default | 8_class | Eval |
|---|---|---|---|
| train(80%) | 461 | 531 | 14678 |
| validation(10%) | 59 | 68 | 1835 |
| test(10%) | 60 | 69 | 1839 |
| total | 580 | 668 | 18352 |
| Total duration(s) | 2851.6933333333354 |
3247.941395833335 |
3247.941395833335 |
Viewer
https://www.modelscope.cn/datasets/ccmusic-database/pianos/dataPeview
Usage
Default Subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/pianos", name="default")
for item in ds["train"]:
print(item)
for item in ds["validation"]:
print(item)
for item in ds["test"]:
print(item)
8-class Subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/pianos", name="8_classes")
for item in ds["train"]:
print(item)
for item in ds["validation"]:
print(item)
for item in ds["test"]:
print(item)
Eval Subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/pianos", 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 git@hf.co:datasets/ccmusic-database/pianos
cd pianos
Dataset Description
- Homepage: https://ccmusic-database.github.io
- Repository: https://huggingface.co/datasets/CCMUSIC/pianos
- Paper: https://doi.org/10.5281/zenodo.5676893
- Leaderboard: https://www.modelscope.cn/datasets/ccmusic/pianos
- Point of Contact: https://arxiv.org/abs/2310.04722
Dataset Summary
Due to the need to increase the dataset size and the absence of a popular piano brand, Yamaha, the dataset is expanded by recording an upright Yamaha piano in [1], in which the recording details can be found. This results in a total of 2,020 audio files. As models used in that article require a larger dataset, data augmentation was performed. The original audio was transformed into Mel spectrograms and sliced into 0.18-second segments, a parameter chosen based on empirical experience. This results in 18,745 spectrogram slices. Although 0.18 seconds may seem narrow, this duration is sufficient for the task at hand, as the classification of piano sound quality does not heavily rely on the temporal characteristics of the audio segments.
Supported Tasks and Leaderboards
Piano Sound Classification, pitch detection
Languages
English
Dataset Creation
Curation Rationale
Lack of a dataset for piano sound quality
Source Data
Initial Data Collection and Normalization
Zhaorui Liu, Shaohua Ji, Monan Zhou
Who are the source language producers?
Students from CCMUSIC & CCOM
Annotations
Annotation process
Students from CCMUSIC recorded different piano sounds and labeled them, and then a subjective survey of sound quality was conducted to score them.
Who are the annotators?
Students from CCMUSIC & CCOM
Personal and Sensitive Information
Piano brands
Considerations for Using the Data
Social Impact of Dataset
Help develop piano sound quality scoring apps
Discussion of Biases
Only for pianos
Other Known Limitations
Lack of black keys for Steinway, data imbalance
Additional Information
Dataset Curators
Zijin Li
Evaluation
(Note: this paper only uses the first 7 piano classes in the dataset, its future work has finished the 8-class evaluation)
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
Provide a dataset for piano sound quality