class_docstr: Parameterize impulse response with outputs of a filtered noise synth.
class_docstr_tok: ["Parameterize","impulse","response","with","outputs","of","a","filtered","noise","synth","."]
docstr: Constructor.
  
  Args:
    trainable: Learn the impulse_response as a single variable for the entire
      dataset.
    reverb_length: Length of the impulse response.
    window_size: Window size for filtered noise synthesizer.
    n_frames: Time resolution of magnitudes coefficients. Only used if
      trainable=True.
    n_filter_banks: Frequency resolution of magnitudes coefficients. Only used
      if trainable=True.
    scale_fn: Function by which to scale the magnitudes.
    initial_bias: Shift the filtered noise synth inputs by this amount
      (before scale_fn) to start generating noise in a resonable range when
      given magnitudes centered around 0.
    add_dry: Add dry signal to reverberated signal on output.
    name: Name of processor module.
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code: def __init__(
      self,
      trainable=False,
      reverb_length=48000,
      window_size=257,
      n_frames=1000,
      n_filter_banks=16,
      scale_fn=core.exp_sigmoid,
      initial_bias=-3.0,
      add_dry=True,
      name="filtered_noise_reverb",
  ):
      """Constructor.
  
      Args:
        trainable: Learn the impulse_response as a single variable for the entire
          dataset.
        reverb_length: Length of the impulse response.
        window_size: Window size for filtered noise synthesizer.
        n_frames: Time resolution of magnitudes coefficients. Only used if
          trainable=True.
        n_filter_banks: Frequency resolution of magnitudes coefficients. Only used
          if trainable=True.
        scale_fn: Function by which to scale the magnitudes.
        initial_bias: Shift the filtered noise synth inputs by this amount
          (before scale_fn) to start generating noise in a resonable range when
          given magnitudes centered around 0.
        add_dry: Add dry signal to reverberated signal on output.
        name: Name of processor module.
      """
      super().__init__(name=name, add_dry=add_dry, trainable=trainable)
      self._n_frames = n_frames
      self._n_filter_banks = n_filter_banks
      self._synth = synths.FilteredNoise(
          n_samples=reverb_length,
          window_size=window_size,
          scale_fn=scale_fn,
          initial_bias=initial_bias,
      )
  
imports: from ddsp import core
  from ddsp import processors
  from ddsp import synths
  import gin
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new FilteredNoiseReverb object from inherited base classes: Reverb with example: obj = FilteredNoiseReverb(trainable, reverb_length, window_size, n_frames, n_filter_banks, scale_fn, initial_bias, add_dry, name)