ccmusic-database
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song_structure

Audio Classification

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 # 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
-![](https://www.modelscope.cn/models/ccmusic-database/song_structure/resolve/master/segment_results.jpg)
 ## Usage
 ```python
 from modelscope import snapshot_download
@@ -36,4 +33,4 @@ cd song_structure
 <https://www.modelscope.cn/models/ccmusic-database/song_structure>
 ## Evaluation
-<https://github.com/monetjoe/ccmusic_eval/tree/msa>

 # 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
 <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)