msaf.eval.process

msaf.eval.process(in_path, boundaries_id='sf', labels_id=None, annot_beats=False, framesync=False, feature='pcp', hier=False, save=False, out_file=None, n_jobs=4, annotator_id=0, config=None)

Main process to evaluate algorithms’ results.

Parameters:

in_path : str

Path to the dataset root folder.

boundaries_id : str

Boundaries algorithm identifier (e.g. siplca, cnmf)

labels_id : str

Labels algorithm identifier (e.g. siplca, cnmf)

ds_name : str

Name of the dataset to be evaluated (e.g. SALAMI). * stands for all.

annot_beats : boolean

Whether to use the annotated beats or not.

framesync: str

Whether to use framesync features or not (default: False -> beatsync)

feature: str

String representing the feature to be used (e.g. pcp, mfcc, tonnetz)

hier : bool

Whether to compute a hierarchical or flat segmentation.

save: boolean

Whether to save the results into the out_file csv file.

out_file: str

Path to the csv file to save the results (if None and save = True it will save the results in the default file name obtained by calling get_results_file_name).

n_jobs: int

Number of processes to run in parallel. Only available in collection mode.

annotator_id : int

Number identifiying the annotator.

config: dict

Dictionary containing custom configuration parameters for the algorithms. If None, the default parameters are used.