code: def testRegressionRastaMode(self):
      # Test the BFCC extractor compared to Rastamat specifications
      audio = (
          MonoLoader(
              filename=join(testdata.audio_dir, "recorded/vignesh.wav"),
              sampleRate=44100,
          )()
          * 2**15
      )
  
      # Expected values generated in Rastamat/MATLAB
      expected = [
          20.28919141,
          23.80362425,
          26.69797305,
          27.10461133,
          26.64508125,
          26.7758322,
          27.1787682,
          27.10699792,
          26.29040982,
          25.04243486,
          24.24791966,
          24.17377063,
          24.61976518,
          25.29554584,
          24.87617598,
          23.79018513,
          23.04026225,
          23.20707811,
          23.09716777,
          23.33050168,
          22.8201923,
          21.49477903,
          21.63639095,
          22.12937291,
          22.01981441,
          21.70728156,
      ]
  
      frameSize = 1102
      hopSize = 441
      fftsize = 2048
      paddingSize = fftsize - frameSize
      spectrumSize = int(fftsize / 2) + 1
      w = Windowing(
          type="hann",
          size=frameSize,
          zeroPadding=paddingSize,
          normalized=False,
          zeroPhase=False,
      )
  
      spectrum = Spectrum(size=fftsize)
  
      mbands = TriangularBarkBands(
          inputSize=spectrumSize,
          type="power",
          highFrequencyBound=8000,
          lowFrequencyBound=0,
          numberBands=26,
          weighting="linear",
          normalize="unit_max",
      )
  
      pool = Pool()
      for frame in FrameGenerator(
          audio,
          frameSize=frameSize,
          hopSize=hopSize,
          startFromZero=True,
          validFrameThresholdRatio=1,
      ):
          pool.add("TriangularBarkBands", mbands(spectrum(w(frame))))
  
      # Save results in a csv file. Use only for debugging purposes.
      # np.savetxt("out.csv", np.mean(np.log(pool['TriangularBarkBands']),0), delimiter=',')
  
      self.assertAlmostEqualVector(
          np.mean(np.log(pool["TriangularBarkBands"]), 0),
          expected,
          1e-2,
      )
  
file_path: essentia/test/src/unittests/spectral/test_triangularbarkbands.py
imports: from essentia_test import *
  import numpy
label_desc: Use image node_id 3 for calling the TestTriangularBarkBands obj's underlying member method code with example usage: obj.testRegressionRastaMode() without return types