docstr: Regression of the mel-spectrogram for a continuous (DC) signal
code: def testRegressionDC(self):
      """Regression of the mel-spectrogram for a continuous (DC) signal"""
  
      expected_file = join(
          filedir(),
          "tensorflowinputfsdsinet",
          "dc_melspectrogram_fsdsinet.npy",
      )
      expected = numpy.load(expected_file)
  
      dc = numpy.ones(self.sr, dtype="float32")
      found = self.melspectrogram(dc)
  
      # Essentia generates an additional frame because of its zero-padding strategy.
      # We can discard it for testing purposes.
      found = found[: expected.shape[0], :]
  
      # The high relative error w.r.t. the original implementation comes mainly from using
      # floats instead of double precision. Two points where the precision plays an important
      # role are:
      # - 1. The FFT. If the signal is very tonal (or DC), the energy (and the truncation
      #  error) accumulates at certain FFT bins.
      # - 2. The mel-spectrogram frequency calculation. Some bins can suffer deviations in the
      #  order of the hundreds of Hz.
      #
      # While we acknowledge this deviation, it does not have a significant impact in our target
      # applications (i.e., inference with neural networks). - Pablo A.
      self.assertAlmostEqualMatrix(found, expected, 1e0)
  
file_path: essentia/test/src/unittests/spectral/test_tensorflowinputfsdsinet.py
imports: from essentia_test import *
label_desc: Use image node_id 3 for calling the TestTensorflowInputFSDSINet obj's underlying member method code with example usage: obj.testRegressionDC() without return types