README.md

---
license: mit
datasets:
  - ccmusic-database/song_structure
language:
  - en
metrics:
  - accuracy
pipeline_tag: audio-classification
tags:
  - music
  - art
---
# Intro
Our evaluation methodology adopted the approach for structural segmentation evaluation outlined in the Harmonix set, which employed Structural Features for boundary identification, and 2D-Fourier Magnitude Coefficients (2D-FMC) for segment labeling based on acoustic similarity. CQT features serve as input features for the algorithm. The algorithm is implemented using Music Structure Analysis Framework (MSAF). For evaluation metrics, the F-measure is reported for the following metrics: Hit Rate with 0.5 and 3-second windows for boundary retrieval, Pairwise Frame Clustering and Entropy Scores for segment labeling. The evaluation is implemented using mir_eval.

## Evaluation result
<img src="https://www.modelscope.cn/api/v1/models/ccmusic-database/song_structure/repo?Revision=master&FilePath=.%2Fsegment_results.jpg&View=true">

## Download
### By Git
```bash
git clone https://www.modelscope.cn/ccmusic-database/song_structure.git
pip install modelscope
```

### By API
```python
from modelscope import snapshot_download
model_dir = snapshot_download('ccmusic-database/song_structure')
```

## Dataset
<https://huggingface.co/datasets/ccmusic-database/song_structure>

## Mirror
<https://www.modelscope.cn/models/ccmusic-database/song_structure>

## Evaluation
<https://github.com/monetjoe/ccmusic_eval/tree/msa>

## Cite
```bibtex
@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}
}
```