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开源软件名称(OpenSource Name):ciaua/clip2frame开源软件地址(OpenSource Url):https://github.com/ciaua/clip2frame开源编程语言(OpenSource Language):Python 100.0%开源软件介绍(OpenSource Introduction):Clip2frameClip2frame provides Python codes for the models in "Event Localization in Music Auto-tagging" by Jen-Yu Liu and Yi-Hsuan Yang, published in ACM Multimedia 2016. DemoYou can put any youtube link into the UI and see the frame-level prediction of our model. The Gaussian filter is removed from the model to produce more sharp prediction (Gaussian is still used in training) ScriptsThe following lists the scripts in the TrainingScripts/train_model.py Testing -- Clip levelScripts/test_model.clip.py Testing -- Frame levelScripts/test_model.frame.py Network structureScripts/network_structure.py Feature extractionScripts/extract_feats.py Standardize dataScripts/standardize_data.py Process annotationsScripts/process_annotations.top_n_tags.py Make experimental data in Numpy formatScripts/make_exp_data.py Threshods derivationScripts/get_thresholds.with_magnatagatune.py Scripts/get_thresholds.with_medleydb.py Data for experiments in the paperThe learned parameters of the best model with Gaussian filter in the paper is included in the package:
The network structure can be found here: Scripts/network_structure.py This model has 188D output. The corresponding tags is here:
The correspondance between the 9 instruments for test in MedleyDB and the 188 tags in MagnaTagATune is here:
The training, validation and testing features can be downloaded from the following links. MagnaTagATune: clip-level training and testinghttp://mac.citi.sinica.edu.tw/~liu/data/exp_data.MagnaTagATune.188tags.zip Decompress the files into a folder
MedleyDB: frame-level testingFeature: http://mac.citi.sinica.edu.tw/~liu/data/feature.MedleyDB.zip Annotations: http://mac.citi.sinica.edu.tw/~liu/data/annotation.medleydb.top9.zip Data for deriving thresholds: http://mac.citi.sinica.edu.tw/~liu/data/exp_data.MedleyDB.length_320.zip Decompress the files
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