class_docstr: Provide various chord evaluation scores.
  
  Parameters
  ----------
  detections : str
      File containing chords detections.
  annotations : str
      File containing chord annotations.
  name : str, optional
      Name of the evaluation object (e.g., the name of the song).
class_docstr_tok: ["Provide","various","chord","evaluation","scores",".","Parameters","--","--","--","--","--","detections",":","str","File","containing","chords","detections",".","annotations",":","str","File","containing","chord","annotations",".","name",":","str",",","optional","Name","of","the","evaluation","object","(","e.g.",",","the","name","of","the","song",")","."]
docstr: Normalized Hamming divergence (directional) between detections and
  annotations. Captures how fragmented the detected chord segments are.
docstr_tok: ["Normalized","Hamming","divergence","(","directional",")","between","detections","and","annotations",".","Captures","how","fragmented","the","detected","chord","segments","are","."]
code: def oversegmentation(self):
      """
      Normalized Hamming divergence (directional) between detections and
      annotations. Captures how fragmented the detected chord segments are.
      """
      if self._overseg is None:
          self._overseg = 1 - segmentation(
              self.ann_chords["start"],
              self.ann_chords["end"],
              self.det_chords["start"],
              self.det_chords["end"],
          )
      return self._overseg
  
imports: import numpy
  from .None import evaluation_io, EvaluationMixin
  from ..io import load_chords
label_desc: Use image node_id 9 for calling the ChordEvaluation obj's underlying member method code with example usage: obj.oversegmentation() and returns: self