ccmusic-database/song_structure

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

from modelscope import snapshot_download

model_dir = snapshot_download(
    "ccmusic-database/song_structure",
    cache_dir="./__pycache__",
)
print(model_dir)

Maintenance

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


Cite

@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}
}