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Python notebooks for training and implementing a convolutional neural network for multi-label classification of sound recordings
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Run each notebook in order to generate training data, train a CNN model, and perform predictions. Specify necessary input/output data paths in the top of each notebook.
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Arbimon Pattern Matching (https://arbimon.rfcx.org/) job results can be downloaded and used for training data annotation files. In the details of the desired Arbimon 2 pattern matching job, select Export Pattern Matching Data. Then specify the desired CSVs in the "sound_annotation_files" variable of 1_generate_training_files_from_annotation.ipynb
