code: def lpcc(signal, n_coeff=12):
      """Computes the linear prediction cepstral coefficients.
  
      Implementation details and description in:
      http://www.practicalcryptography.com/miscellaneous/machine-learning/tutorial-cepstrum-and-lpccs/
  
      Feature computational cost: 1
  
      Parameters
      ----------
      signal : nd-array
          Input from linear prediction cepstral coefficients are computed
      n_coeff : int
          Number of coefficients
  
      Returns
      -------
      nd-array
          Linear prediction cepstral coefficients
  
      """
      # 12-20 cepstral coefficients are sufficient for speech recognition
      lpc_coeffs = lpc(signal, n_coeff)
  
      if np.sum(lpc_coeffs) == 0:
          return tuple(np.zeros(len(lpc_coeffs)))
  
      # Power spectrum
      powerspectrum = np.abs(np.fft.fft(lpc_coeffs)) ** 2
      lpcc_coeff = np.fft.ifft(np.log(powerspectrum))
  
      return tuple(np.abs(lpcc_coeff))
  
imports: import scipy.signal
  from tsfel.feature_extraction.features_utils import *
label_desc: Use image node_id 55 for calling a global function with example usage: lpcc(signal, n_coeff) and returns: tuple, tuple