---
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.

## Usage
```python
from modelscope import snapshot_download
model_dir = snapshot_download("ccmusic-database/song_structure")
```

## Maintenance
```bash
git clone git@hf.co:ccmusic-database/song_structure
cd song_structure
```

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

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

## Evaluation
[![](https://www.modelscope.cn/models/ccmusic-database/song_structure/resolve/master/segment_results.jpg)](https://github.com/monetjoe/ccmusic_eval/tree/msa)

## Cite
```bibtex
@dataset{zhaorui_liu_2021_5676893,
  author    = {Zhaorui Liu and Zijin Li},
  title     = {Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)},
  month     = nov,
  year      = 2021,
  publisher = {Zenodo},
  version   = {1.1},
  doi       = {10.5281/zenodo.5676893},
  url       = {https://doi.org/10.5281/zenodo.5676893}
}
```