code: def mfcc(
      signal,
      fs,
      pre_emphasis=0.97,
      nfft=512,
      nfilt=40,
      num_ceps=12,
      cep_lifter=22,
  ):
      """Computes the MEL cepstral coefficients.
  
      It provides the information about the power in each frequency band.
  
      Implementation details and description on:
      https://www.kaggle.com/ilyamich/mfcc-implementation-and-tutorial
      https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html#fnref:1
  
      Feature computational cost: 1
  
      Parameters
      ----------
      signal : nd-array
          Input from which MEL coefficients is computed
      fs : float
          Sampling frequency
      pre_emphasis : float
          Pre-emphasis coefficient for pre-emphasis filter application
      nfft : int
          Number of points of fft
      nfilt : int
          Number of filters
      num_ceps: int
          Number of cepstral coefficients
      cep_lifter: int
          Filter length
  
      Returns
      -------
      nd-array
          MEL cepstral coefficients
  
      """
      filter_banks = filterbank(signal, fs, pre_emphasis, nfft, nfilt)
  
      mel_coeff = scipy.fft.dct(
          filter_banks, type=2, axis=0, norm="ortho"
      )[
          1 : (num_ceps + 1)
      ]  # Keep 2-13
  
      mel_coeff -= np.mean(mel_coeff, axis=0) + 1e-8
  
      # liftering
      ncoeff = len(mel_coeff)
      n = np.arange(ncoeff)
      lift = 1 + (cep_lifter / 2) * np.sin(
          np.pi * n / cep_lifter
      )  # cep_lifter = 22 from python_speech_features library
  
      mel_coeff *= lift
  
      return tuple(mel_coeff)
  
imports: import scipy.signal
  from tsfel.feature_extraction.features_utils import *
label_desc: Use image node_id 52 for calling a global function with example usage: mfcc(signal, fs, pre_emphasis, nfft, nfilt, num_ceps, cep_lifter) and returns: tuple