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--- |
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license: mit |
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datasets: |
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- ccmusic-database/song_structure |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: audio-classification |
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tags: |
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- music |
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- art |
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--- |
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# Intro |
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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. |
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## Usage |
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```python |
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from modelscope import snapshot_download |
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model_dir = snapshot_download("ccmusic-database/song_structure") |
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``` |
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## Maintenance |
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```bash |
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git clone [email protected]:ccmusic-database/song_structure |
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cd song_structure |
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``` |
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## Dataset |
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<https://huggingface.co/datasets/ccmusic-database/song_structure> |
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## Mirror |
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<https://www.modelscope.cn/models/ccmusic-database/song_structure> |
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## Evaluation |
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[](https://github.com/monetjoe/ccmusic_eval/tree/msa) |
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## Cite |
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```bibtex |
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@dataset{zhaorui_liu_2021_5676893, |
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author = {Zhaorui Liu and Zijin Li}, |
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title = {Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}, |
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month = nov, |
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year = 2021, |
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publisher = {Zenodo}, |
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version = {1.1}, |
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doi = {10.5281/zenodo.5676893}, |
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url = {https://doi.org/10.5281/zenodo.5676893} |
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} |
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``` |
