def sprengel_binary_mask_from_wave_file(filepath):
fs, x = utils.read_wave_file(filepath)
Sxx = sp.wave_to_amplitude_spectrogram(x, fs)
Sxx_log = sp.wave_to_log_amplitude_spectrogram(x, fs)
# plot spectrogram
fig = plt.figure(1)
subplot_image(Sxx_log, 411, "Spectrogram")
Sxx = pp.normalize(Sxx)
binary_image = pp.median_clipping(Sxx, 3.0)
subplot_image(binary_image + 0, 412, "Median Clipping")
binary_image = morphology.binary_erosion(binary_image,
selem=np.ones((4, 4)))
subplot_image(binary_image + 0, 413, "Erosion")
binary_image = morphology.binary_dilation(binary_image,
selem=np.ones((4, 4)))
subplot_image(binary_image + 0, 414, "Dilation")
mask = np.array([np.max(col) for col in binary_image.T])
mask = morphology.binary_dilation(mask, np.ones(4))
mask = morphology.binary_dilation(mask, np.ones(4))
# plot_vector(mask, "Mask")
fig.set_size_inches(10, 12)
plt.tight_layout()
fig.savefig(utils.get_basename_without_ext(filepath) + "_binary_mask.png",
dpi=100)
def preprocess_wave(wave, fs):
""" Preprocess a signal by computing the noise and signal mask of the
signal, and extracting each part from the signal
"""
Sxx = sp.wave_to_amplitude_spectrogram(wave, fs)
n_mask = compute_noise_mask(Sxx)
s_mask = compute_signal_mask(Sxx)
n_mask_scaled = reshape_binary_mask(n_mask, wave.shape[0])
s_mask_scaled = reshape_binary_mask(s_mask, wave.shape[0])
signal_wave = extract_masked_part_from_wave(s_mask_scaled, wave)
noise_wave = extract_masked_part_from_wave(n_mask_scaled, wave)
return signal_wave, noise_wave
def plot_spectrogram_from_wave_file(filename):
fs, x = utils.read_gzip_wave_file(filename)
Sxx = sp.wave_to_amplitude_spectrogram(x, fs, 512, 128)[:256]
plot_matrix(Sxx, "Amplitude Spectrogram")
def img_log_spectrogram_from_wave_file(filepath):
fs, x = utils.read_gzip_wave_file(filepath)
Sxx = sp.wave_to_amplitude_spectrogram(x, fs, 512, 128)[:256]
baseName = utils.get_basename_without_ext(filepath)
save_matrix_to_file(Sxx, "Amplitude Spectrogram", baseName + "_LOG.png")
def signal_structure(wave, samplerate):
spectrogram = sp.wave_to_amplitude_spectrogram(wave, samplerate)
norm_spectrogram = pp.normalize(spectrogram)
binary_image = pp.median_clipping(norm_spectrogram, 3)
return np.sum(binary_image)
def signal_energy(wave, samplerate):
spectrogram = sp.wave_to_amplitude_spectrogram(wave, samplerate)
return np.sum(spectrogram)