--- 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 ### Data Instances .zip(.wav, jpg) ### Data Fields ```txt 1_PearlRiver 2_YoungChang 3_Steinway-T 4_Hsinghai 5_Kawai 6_Steinway 7_Kawai-G 8_Yamaha (For Non-default subset) ``` ### Data Splits | 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` | ## Usage ### Default Subset ```python 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 ```python from datasets import load_dataset ds = load_dataset("ccmusic-database/pianos", name="8_class") for item in ds["train"]: print(item) for item in ds["validation"]: print(item) for item in ds["test"]: print(item) ``` ### Eval Subset ```python from datasets import load_dataset # 8-class label 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 ```bash GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/pianos cd pianos ``` ## Mirror ## Dataset Description ### 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 the future work of [[1]](https://arxiv.org/pdf/2310.04722.pdf). 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 18,352 spectrogram slices in the eval subset. 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 [1] [Monan Zhou, Shangda Wu, Shaohua Ji, Zijin Li, and Wei Li. A Holistic Evaluation of Piano Sound Quality[C]//Proceedings of the 10th Conference on Sound and Music Technology (CSMT). Springer, Singapore, 2023.](https://arxiv.org/pdf/2310.04722.pdf)
(Note: this paper only uses the first 7 piano classes in the dataset, its future work has finished the 8-class evaluation in [2])
[2] ### Citation Information ```bibtex @inproceedings{zhou2023holistic, title = {A Holistic Evaluation of Piano Sound Quality}, author = {Monan Zhou and Shangda Wu and Shaohua Ji and Zijin Li and Wei Li}, booktitle = {National Conference on Sound and Music Technology}, pages = {3--17}, year = {2023}, organization = {Springer} } ``` ### Contributions Provide a dataset for piano sound quality