class_docstr: Exportable, `torchaudio`-based implementation of Mel Spectrogram extraction.
  
  See `AudioToMelSpectrogramPreprocessor` for args.
class_docstr_tok: ["Exportable",",","`","torchaudio","`","-based","implementation","of","Mel","Spectrogram","extraction",".","See","`","AudioToMelSpectrogramPreprocessor","`","for","args","."]
code: def _apply_dithering(self, signals: torch.Tensor) -> torch.Tensor:
      if self.training and self.dither > 0.0:
          noise = torch.randn_like(signals) * self.dither
          signals = signals + noise
      return signals
  
imports: import math
  import random
  from typing import Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  import torch.nn
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the FilterbankFeaturesTA obj's underlying member method code with example usage: obj._apply_dithering(signals) and returns: signals