def lpc(blk, order=None):
  """
  Find the Linear Predictive Coding (LPC) coefficients as a ZFilter object,
  the analysis whitening filter. This implementation uses the autocorrelation
  method, using the Levinson-Durbin algorithm or Numpy pseudo-inverse for
  linear system solving, when needed.

  Parameters
  ----------
  blk :
    An iterable with well-defined length. Don't use this function with Stream
    objects!
  order :
    The order of the resulting ZFilter object. Defaults to ``len(blk) - 1``.

  Returns
  -------
  A FIR filter, as a ZFilter object. The mean squared error over the given
  block is in its "error" attribute.

  Hint
  ----
  See ``lpc.kautocor`` example, which should apply equally for this strategy.

  See Also
  --------
  levinson_durbin :
    Levinson-Durbin algorithm for solving Yule-Walker equations (Toeplitz
    matrix linear system).
  lpc.nautocor:
    LPC coefficients from linear system solved with Numpy pseudo-inverse.
  lpc.kautocor:
    LPC coefficients obtained with Levinson-Durbin algorithm.

  """
  if order < 100:
    return lpc.nautocor(blk, order)
  try:
    return lpc.kautocor(blk, order)
  except ParCorError:
    return lpc.nautocor(blk, order)
Find the Linear Predictive Coding (LPC) coefficients as a ZFilter object,
  the analysis whitening filter. This implementation uses the autocorrelation
  method, using the Levinson-Durbin algorithm or Numpy pseudo-inverse for
  linear system solving, when needed.

  Parameters
  ----------
  blk :
    An iterable with well-defined length. Don't use this function with Stream
    objects!
  order :
    The order of the resulting ZFilter object. Defaults to ``len(blk) - 1``.

  Returns
  -------
  A FIR filter, as a ZFilter object. The mean squared error over the given
  block is in its "error" attribute.

  Hint
  ----
  See ``lpc.kautocor`` example, which should apply equally for this strategy.

  See Also
  --------
  levinson_durbin :
    Levinson-Durbin algorithm for solving Yule-Walker equations (Toeplitz
    matrix linear system).
  lpc.nautocor:
    LPC coefficients from linear system solved with Numpy pseudo-inverse.
  lpc.kautocor:
    LPC coefficients obtained with Levinson-Durbin algorithm.