class_docstr: Bark frequency scale.
  
  Has wider bandwidths at lower frequencies, see:
  Critical bandwidth: BARK
  Zwicker and Terhardt, 1980
class_docstr_tok: ["Bark","frequency","scale",".","Has","wider","bandwidths","at","lower","frequencies",",","see",":","Critical","bandwidth",":","BARK","Zwicker","and","Terhardt",",","1980"]
docstr: Obtain initialization values for the Bark scale.
  
  Args:
      channels: Number of channels.
      fs: Sample rate.
  
  Returns:
      torch.Tensor: Filter start frequencíes.
      torch.Tensor: Filter stop frequencíes.
docstr_tok: ["Obtain","initialization","values","for","the","Bark","scale",".","Args",":","channels",":","Number","of","channels",".","fs",":","Sample","rate",".","Returns",":","torch.Tensor",":","Filter","start","frequenc\u00edes",".","torch.Tensor",":","Filter","stop","frequenc\u00edes","."]
code: def bank(cls, channels: int, fs: float) -> torch.Tensor:
      """Obtain initialization values for the Bark scale.
  
      Args:
          channels: Number of channels.
          fs: Sample rate.
  
      Returns:
          torch.Tensor: Filter start frequencíes.
          torch.Tensor: Filter stop frequencíes.
      """
      assert check_argument_types()
      # min and max BARK center frequencies by approximation
      min_center_frequency = torch.tensor(70.0)
      max_center_frequency = torch.tensor(fs * 0.45)
      center_frequencies = torch.linspace(
          cls.convert(min_center_frequency),
          cls.convert(max_center_frequency),
          channels,
      )
      center_frequencies = cls.invert(center_frequencies)
  
      f1 = center_frequencies - torch.div(
          cls.convert(center_frequencies), 2
      )
      f2 = center_frequencies + torch.div(
          cls.convert(center_frequencies), 2
      )
      return torch.stack([f1, f2], dim=1)
  
imports: import math
  from typing import Union
  import torch
  from typeguard import check_argument_types
label_desc: Use image node_id 3 for calling the BarkScale obj's underlying member method code with example usage: obj.bank(cls, channels, fs) and returns: torch