- We provide a straightforward calculation of annotation efficiency based on counting the number of shifts, insertions, and deletions, and pair it with an informative visualisation suited for the qualitative evaluation of beat tracking systems. It graphically displays the minimum set and type of operations required to transform a sequence of initial beat detections in such a way as to maximize the F-measure when comparing the transformed detections against a ground truth annotation sequence.
To use this library, the user should first clone this repository to the working python directory, and make sure the following dependencies are satisfied within your environment. Please check the Example Code section for usage scenarios.
In parenthesis we refer the versions with which this code was tested.
- Python (v3.8)
- Numpy (v1.19)
- Matplotlib (v3.3)
- ipywidgets (v7.5)
- widgetsnbextension (v3.5)
You can also create a similar (conda) development environment by using the attached environment.yml file:
conda env create -f environment.yml
Two detailed examples on how to use this code are provided:
- ShiftIfYouCan.ipynb where we present a walkthrough example for the ShiftIfYouCan visualisation code;
- example_script.py: this script provides an example of how to create a list of variations of beat detections, and process it with the corresponding ground-truth annotation. It saves the transformed beat detections, as well as all the corresponding visualisation plots as *.png files in the /figures/ folder.
António Sá Pinto antoniosapinto@gmail.com