class_docstr: Impulse response filtering
docstr: Impulse response filtering
  
  Parameters
  ----------
  
  ir_files : str or list of str
      Path to audio files on disk containing the impulse responses
  
  n_fft : int > 0
      FFT window size
  
  rolloff_value : int > 0
      If provided, only estimate the groupd delay of the passband that
      above threshold which is 'rolloff_value' below the peak
      on frequency response.
      Positive input will be changed to negative
docstr_tok: ["Impulse","response","filtering","Parameters","--","--","--","--","--","ir_files",":","str","or","list","of","str","Path","to","audio","files","on","disk","containing","the","impulse","responses","n_fft",":","int",">","0","FFT","window","size","rolloff_value",":","int",">","0","If","provided",",","only","estimate","the","groupd","delay","of","the","passband","that","above","threshold","which","is","'rolloff_value","'","below","the","peak","on","frequency","response",".","Positive","input","will","be","changed","to","negative"]
code: def __init__(self, ir_files, n_fft=2048, rolloff_value=-24):
      """Impulse response filtering
  
      Parameters
      ----------
  
      ir_files : str or list of str
          Path to audio files on disk containing the impulse responses
  
      n_fft : int > 0
          FFT window size
  
      rolloff_value : int > 0
          If provided, only estimate the groupd delay of the passband that
          above threshold which is 'rolloff_value' below the peak
          on frequency response.
          Positive input will be changed to negative
      """
  
      if isinstance(ir_files, six.string_types):
          ir_files = [ir_files]
  
      BaseTransformer.__init__(self)
      self.ir_files = ir_files
      self.n_fft = n_fft
      self.rolloff_value = rolloff_value
      self._register(".*", self.deform_times)
  
file_path: muda/muda/deformers/ir.py
imports: import six
  import numpy
  import pandas
  import scipy
  import soundfile
  import librosa
  import jams
  from ..base import BaseTransformer
label_desc: Use image node_id 1 to create a new IRConvolution object from inherited base classes: BaseTransformer with example: obj = IRConvolution(ir_files, n_fft, rolloff_value)