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
from modelscope import snapshot_download
model_dir = snapshot_download("ccmusic-database/song_structure")
Maintenance
git clone [email protected]: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}
}