task_categories:audio-classification task_categories:image-classification size_categories:10K<n<100K language:en license:mit music art arxiv:2310.04722 region:us
pianos_mel is a mel spectrogram dataset of piano sounds. 
It consists of 8 kinds of pianos_mel including
PearlRiver, YoungChang, Steinway-T, Hsinghai, Kawai, Steinway, Kawai-G and Yamaha. 
Data was annotated by students from the China Conservatory of Music (CCMUSIC) in Beijing.
@article{CSMT2023HEPSQ,
  title   = {A Holistic Evaluation of Piano Sound Quality},
  author  = {Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, Wei Li*},
  journal = {Springer},
  year    = {2023},
  url     = {https://github.com/george-chou/Piano-Classification}
}
---
license: mit
task_categories:
- audio-classification
- image-classification
language:
- en
tags:
- music
- art
pretty_name: Pianos
size_categories:
- 10K<n<100K
---

# Dataset card for pianos_mel
## Dataset Description
- **Homepage:** [CCOM/pianos_mel](https://huggingface.co/datasets/CCOM/pianos_mel)
- **Repository:** `git@hf.co:datasets/CCOM/pianos_mel`
- **Paper:** [A Holistic Evaluation of Piano Sound Quality](https://arxiv.org/pdf/2310.04722.pdf)
- **Leaderboard:** [arxiv:2309.13259](https://arxiv.org/abs/2310.04722)
- **Point of Contact:** CNN, ERB, piano sound quality, audio classification

## Dataset Summary
This dataset aims to develop a holistic evaluation method for piano sound quality to assist in purchasing decisions. Unlike previous studies that focused on the effect of piano performance techniques on sound quality, this study evaluates the inherent sound quality of different pianos. To derive quality evaluation systems, the study uses subjective questionnaires based on a piano sound quality dataset. The method selects the optimal piano classification models by comparing the fine-tuning results of different pre-training models of Convolutional Neural Networks (CNN). To improve the interpretability of the models, the study applies Equivalent Rectangular Bandwidth (ERB) analysis. The results reveal that musically trained individuals are better able to distinguish between the sound quality differences of different pianos. The best fine-tuned CNN pre-trained backbone achieves a high accuracy of 98.3%as the piano classifier. However, the dataset is limited, and the audio is sliced to increase its quantity, resulting in a lack of diversity and balance, so we use focal loss to reduce the impact of data imbalance.
To optimize the method, the dataset will be expanded, or few-shot learning techniques will be employed in future research.

## Supported Tasks and Leaderboards
Audio classification, pitch detection, etc

## Languages
English

## Usage
```
from datasets import load_dataset

data = load_dataset("CCOM/pianos_mel")
trainset = data['train']
validset = data['validation']
testset = data['test']
labels = trainset.features['label'].names

for item in trainset:
    print('image: ', item['image'].convert('RGB'))
    print('label name: ' + labels[item['label']])

for item in validset:
    print('image: ', item['image'].convert('RGB'))
    print('label name: ' + labels[item['label']])

for item in testset:
    print('image: ', item['image'].convert('RGB'))
    print('label name: ' + labels[item['label']])
```

## Maintenance
```
git clone git@hf.co:datasets/CCOM/pianos_mel
```

## Dataset Structure
### Data Instances
.jsonl, .zip(.jpg, .csv)

### Data Fields
piano sound mel, sound quality, pitch

### Data Splits
Train, validation, test

## Dataset Creation
### Curation Rationale
Promoting the development of AI in the music industry

### Source Data
#### Initial Data Collection and Normalization
Monan Zhou, Shangda Wu, Shaohua Ji, Zhaorui Liu

#### Who are the source language producers?
Composers of the songs in dataset

### Annotations
#### Annotation process
1. Record different piano sounds
2. Annotate sound files with quality labels

#### Who are the annotators?
Annotators from CCMUSIC, CCOM and Xinghai Conservatory of Music

### Personal and Sensitive Information
None

## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry

### Discussion of Biases
All are piano songs

### Other Known Limitations
Samples are not balanced enough

## Additional Information
### Dataset Curators
Monan Zhou

### Licensing Information
```
MIT License

Copyright (c) CCOM

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
```
@misc{zhou2023holistic,
      title={A Holistic Evaluation of Piano Sound Quality}, 
      author={Monan Zhou and Shangda Wu and Shaohua Ji and Zijin Li and Wei Li},
      year={2023},
      eprint={2310.04722},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}
```

### Contributions
Develop a holistic evaluation method for piano sound quality to assist in purchasing decisions.