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

Download

By Git

git clone https://www.modelscope.cn/ccmusic-database/song_structure.git
pip install modelscope

By API

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

@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}
}
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Examples
Unable to determine this model's library. Check the docs .

Dataset used to train ccmusic-database/song_structure