code: def lpcoeff(speech_frame, model_order):
      # (1) Compute Autocor lags
      winlength = speech_frame.shape[0]
      R = []
      for k in range(model_order + 1):
          first = speech_frame[: (winlength - k)]
          second = speech_frame[k:winlength]
          R.append(np.sum(first * second))
  
      # (2) Lev-Durbin
      a = np.ones((model_order,))
      E = np.zeros((model_order + 1,))
      rcoeff = np.zeros((model_order,))
      E[0] = R[0]
      for i in range(model_order):
          if i == 0:
              sum_term = 0
          else:
              a_past = a[:i]
              sum_term = np.sum(a_past * np.array(R[i:0:-1]))
          rcoeff[i] = (R[i + 1] - sum_term) / E[i]
          a[i] = rcoeff[i]
          if i > 0:
              a[:i] = a_past[:i] - rcoeff[i] * a_past[::-1]
          E[i + 1] = (1 - rcoeff[i] * rcoeff[i]) * E[i]
      acorr = np.array(R, dtype=np.float32)
      refcoeff = np.array(rcoeff, dtype=np.float32)
      a = a * -1
      lpparams = np.array([1] + list(a), dtype=np.float32)
      acorr = np.array(acorr, dtype=np.float32)
      refcoeff = np.array(refcoeff, dtype=np.float32)
      lpparams = np.array(lpparams, dtype=np.float32)
  
      return acorr, refcoeff, lpparams
  
file_path: speechbrain/recipes/Voicebank/MTL/ASR_enhance/composite_eval.py
imports: from scipy.linalg import toeplitz
  from tqdm import tqdm
  from pesq import pesq
  import librosa
  import numpy
  import os
  import sys
label_desc: Use image node_id 3 for calling a global function with example usage: lpcoeff(speech_frame, model_order) and returns: acorr,  refcoeff,  lpparams