code: def autocorr_norm(signal):
      """Computes the autocorrelation.
  
      Implementation details and description in:
      https://ccrma.stanford.edu/~orchi/Documents/speaker_recognition_report.pdf
  
      Parameters
      ----------
      signal : nd-array
          Input from linear prediction coefficients are computed
  
      Returns
      -------
      nd-array
          Autocorrelation result
  
      """
  
      variance = np.var(signal)
      signal = np.copy(signal - signal.mean())
      r = scipy.signal.correlate(signal, signal)[-len(signal) :]
  
      if (signal == 0).all():
          return np.zeros(len(signal))
  
      acf = r / variance / len(signal)
  
      return acf
  
file_path: tsfel/tsfel/feature_extraction/features_utils.py
imports: import scipy
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: autocorr_norm(signal) and returns: acf, np