Compare SciPy vs ANN in generating spectrograms

spectrogram_scipy_vs_nn.py

def make_wave():
  # Create four evenly-spaced tones
  waves = []
  hz = 0
  for i in range(0, size, 512):
    hz = hz + 50
    waves.append(np.sin(np.linspace(0, 2 * np.pi * hz, 512)))
  return np.hstack(waves)

wave = make_wave()

# Calculate spectrogram using math
f, t, wave_sxx = spectrogram(wave)
wave_sxx_graph = np.absolute(wave_sxx)

# Calculate spectrogram using neural network
nn_sxx = model.predict(np.reshape(wave, (1, size)))
nn_sxx_graph = np.reshape(np.absolute(nn_sxx[0][rows * cols:] + 1j * nn_sxx[0][:rows * cols]), (rows, cols))

# Plot spectrograms side-by-side
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.pcolormesh(t, f, wave_sxx_graph)
plt.subplot(1, 2, 2)
plt.pcolormesh(t, f, nn_sxx_graph)
plt.show()