Intro

The Chinese National Pentatonic Mode Recognition Model is trained on the Chinese National Pentatonic Mode Dataset, which combines manual annotation with computational analysis. This dataset collects and annotates audio files representing the five primary tonal modes in traditional Chinese music: Gong, Shang, Jiao, Zhi, and Yu (covering five-tone, six-tone, and seven-tone scales). Detailed annotations are provided for these modes, and an in-depth analysis of the methods for identifying Chinese ethnic five-tone modes is presented. The model employs feature extraction, spectral analysis, and pattern recognition techniques to efficiently and accurately identify and classify the five-tone modes in the music. This model's application not only facilitates the digital preservation of ethnic music but also offers robust data support and a technical framework for the analysis and retrieval of ethnic music features.

Demo (inference code)

https://huggingface.co/spaces/ccmusic-database/CNPM

Usage

from huggingface_hub import snapshot_download
model_dir = snapshot_download("ccmusic-database/CNPM")

Maintenance

GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/CNPM
cd CNPM

Results

Backbone	Size(M)	Mel	CQT	Chroma
vit_l_32	306.5	0.680	0.769	0.399
vit_l_16	304.3	*0.823*	*0.859*	*0.549*

vgg11_bn	132.9	*0.807*	*0.843*	*0.609*
regnet_y_16gf	83.6	0.590	0.832	0.535
wide_resnet50_2	68.9	0.694	0.757	0.531
alexnet	61.1	0.742	0.744	0.542
shufflenet_v2_x2_0	7.4	0.473	0.720	0.266

Best result

Loss curve
Training and validation accuracy
Confusion matrix

Dataset

https://huggingface.co/datasets/ccmusic-database/CNPM

Mirror

https://www.modelscope.cn/models/ccmusic-database/CNPM

Evaluation

https://github.com/monetjoe/ccmusic_eval

Cite

@article{Zhou-2025,
  author  = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
  title   = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
  journal = {Transactions of the International Society for Music Information Retrieval},
  volume  = {8},
  number  = {1},
  pages   = {22--38},
  month   = {Mar},
  year    = {2025},
  url     = {https://doi.org/10.5334/tismir.194},
  doi     = {10.5334/tismir.194}
}

ccmusic-database
/

CNPM