code: def testRegressionHtkMode(self):
      from numpy import mean
  
      frameSize = 1102
      hopSize = 441
      fftSize = 2048
      zeroPadding = fftSize - frameSize
      spectrumSize = int(fftSize / 2) + 1
      expected = array(
          [
              88.78342438,
              6.36632776,
              -1.62886882,
              -5.30124903,
              -3.92886806,
              -13.52705765,
              -7.62559938,
              -18.51092339,
              2.24103594,
              3.10329103,
              -16.25634193,
              -5.46565485,
              -14.56163502,
          ]
      )
  
      audio = (
          MonoLoader(
              filename=join(testdata.audio_dir, "recorded/vignesh.wav"),
              sampleRate=44100,
          )()
          * 2**15
      )
  
      w = Windowing(
          type="hamming",
          size=frameSize,
          zeroPadding=zeroPadding,
          normalized=False,
          zeroPhase=False,
      )
  
      spectrum = Spectrum(size=fftSize)
  
      mfccEssentia = MFCC(
          inputSize=spectrumSize,
          type="magnitude",
          warpingFormula="htkMel",
          weighting="warping",
          highFrequencyBound=8000,
          numberBands=26,
          numberCoefficients=13,
          normalize="unit_max",
          dctType=3,
          logType="log",
          liftering=22,
      )
  
      pool = Pool()
  
      for frame in FrameGenerator(
          audio,
          frameSize=frameSize,
          hopSize=hopSize,
          startFromZero=True,
          validFrameThresholdRatio=1,
      ):
          pool.add("mfcc", mfccEssentia(spectrum(w(frame)))[1])
  
      self.assertAlmostEqualVector(mean(pool["mfcc"], 0), expected, 1e0)
  
file_path: essentia/test/src/unittests/spectral/test_mfcc.py
imports: from essentia_test import *
  from math import *
  import numpy
label_desc: Use image node_id 4 for calling the TestMFCC obj's underlying member method code with example usage: obj.testRegressionHtkMode() without return types