class_docstr: BandpassPerturbation
  
  Randomly dropout along the frequency axis.
  
  The original idea comes from the following:
      "randomly-selected frequency band was cut off under the constraint of
       leaving at least 1,000 Hz band within the range of less than 4,000Hz."
      (The Hitachi/JHU CHiME-5 system: Advances in speech recognition for
       everyday home environments using multiple microphone arrays;
       http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_kanda.pdf)
class_docstr_tok: ["BandpassPerturbation","Randomly","dropout","along","the","frequency","axis",".","The","original","idea","comes","from","the","following",":","``","randomly-selected","frequency","band","was","cut","off","under","the","constraint","of","leaving","at","least","1,000","Hz","band","within","the","range","of","less","than","4,000Hz",".","''","(","The","Hitachi\/JHU","CHiME-5","system",":","Advances","in","speech","recognition","for","everyday","home","environments","using","multiple","microphone","arrays",";","http",":","\/\/spandh.dcs.shef.ac.uk\/chime_workshop\/papers\/CHiME_2018_paper_kanda.pdf",")"]
code: def __init__(self, lower=0.0, upper=0.75, seed=None, axes=(-1,)):
      self.lower = lower
      self.upper = upper
      self.state = numpy.random.RandomState(seed)
      # x_stft: (Time, Channel, Freq)
      self.axes = axes
  
imports: import librosa
  import numpy
  import scipy
  import soundfile
  from espnet.utils.io_utils import SoundHDF5File
label_desc: Use image node_id 1 to create a new BandpassPerturbation object from inherited base classes: object with example: obj = BandpassPerturbation(lower, upper, seed, axes)