Increase efficiency of IoU scores (#15) #129
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Shows how it was created as well.
…anges to isolation parameters (model and window_size parameters)
Updated simple_isolate function in IsoAutio.py to use numpy library instead of manually iterating through local_scores Tested on both Performance_Tests notebook and PyHa_Tutorial notebook
Used numpy library to find starts and ends of clips, create annotations using the window size, and join annotations if necessary
…inberg is smaller than time_per_score / 2
…ist using steinberg isolation
Used similar rolled score technique to simple and steinberg isolate Still has differences with original that will be fixed
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Closes #15. Uses matrix multiplication ($A \times B^T$ , with A = manual labels, B = bot labels) to calculate the intersection, NumPy operations to calculate the union, then divides the intersection matrix by the union matrix. See timing benchmarks in the associated issue or in the Jupyter Notebook.