def test_get_contour(self):
S = np.array([
[0, 0, 0, 0],
[0, 0.002, 0, 0],
[1, 0, 5, 0],
[0, 0.3, 0.1, 0],
[0.1, 0, 0.2, 0],
[0, 0.5, 0, 0.002],
[0, 0, 0, 0]
])
times = np.array([0.05, 0.1, 0.15, 0.2])
freqs = np.array([97.0, 100.0, 103.0, 105.0, 107.0, 109.0, 112.0])
psh = utils.PeakStreamHelper(S, times, freqs, 0.9, 0.9, 3.456, 80)
psh.get_contour()
actual_contour_idx = psh.contour_idx
expected_contour_idx = [2, 3, 1]
self.assertEqual(expected_contour_idx, actual_contour_idx)
actual_c_len = psh.c_len
expected_c_len = [3]
self.assertEqual(expected_c_len, actual_c_len)
psh.get_contour()
actual_contour_idx = psh.contour_idx
expected_contour_idx = [2, 3, 1, 6, 5, 7, 4]
self.assertEqual(expected_contour_idx, actual_contour_idx)
actual_c_len = psh.c_len
expected_c_len = [3, 4]
self.assertEqual(expected_c_len, actual_c_len)
def test_peak_streaming(self):
S = np.array([
[0, 0, 0, 0],
[0, 0.002, 0, 0],
[1, 0, 5, 0],
[0, 0.3, 0.1, 0],
[0.1, 0, 0.2, 0],
[0, 0.5, 0, 0.2],
[0, 0, 0, 0]
])
times = np.array([0.05, 0.1, 0.15, 0.2])
freqs = np.array([97.0, 100.0, 103.0, 105.0, 107.0, 109.0, 112.0])
psh = utils.PeakStreamHelper(S, times, freqs, 0.9, 0.9, 3.456, 80)
expected_c_numbers = np.array([0, 0, 0, 1, 1, 1, 1])
expected_c_times = np.array([0.15, 0.1, 0.05, 0.1, 0.15, 0.2, 0.05])
expected_c_freqs = np.array([103., 105., 103., 109., 107., 109., 107.])
expected_c_sal = np.array([5, 0.3, 1.0, 0.5, 0.2, 0.2, 0.1])
(actual_c_numbers,
actual_c_times,
actual_c_freqs,
actual_c_sal) = psh.peak_streaming()
self.assertTrue(np.allclose(expected_c_numbers, actual_c_numbers))
self.assertTrue(np.allclose(expected_c_times, actual_c_times))
self.assertTrue(np.allclose(expected_c_freqs, actual_c_freqs))
self.assertTrue(np.allclose(expected_c_sal, actual_c_sal))
def compute_contours(self, audio_filepath):
"""Compute contours as in Justin Salamon's melodia.
This calls a vamp plugin in the background, which creates a csv file.
The csv file is loaded into memory and the file is deleted.
Parameters
----------
audio_filepath : str
Path to audio file.
Returns
-------
Instance of Contours object
"""
if not os.path.exists(audio_filepath):
raise IOError(
"The audio file {} does not exist".format(audio_filepath)
)
if self.preprocess:
fpath = self._preprocess_audio(
audio_filepath, normalize_format=True,
normalize_volume=True
)
else:
fpath = audio_filepath
print("Computing salience...")
if self.use_salamon_salience:
times, freqs, S = self._compute_salience_salamon(fpath)
else:
y, sr = librosa.load(fpath, sr=self.audio_samplerate)
times, freqs, S = self._compute_salience(y, sr)
psh = utils.PeakStreamHelper(
S, times, freqs, self.amp_thresh, self.dev_thresh, self.n_gap,
self.pitch_cont, peak_thresh=None
)
c_numbers, c_times, c_freqs, c_sal = psh.peak_streaming()
if len(c_numbers) > 0:
c_numbers, c_times, c_freqs, c_sal = self._sort_contours(
np.array(c_numbers), np.array(c_times), np.array(c_freqs),
np.array(c_sal)
)
(c_numbers, c_times, c_freqs, c_sal) = self._postprocess_contours(
c_numbers, c_times, c_freqs, c_sal
)
return Contours(
c_numbers, c_times, c_freqs, c_sal, self.sample_rate,
audio_filepath
)
def compute_contours(self, salience_npy_file, audio_duration):
"""Compute contours by peak streaming a salience output.
Parameters
----------
salience_npy_file : str
Path to precomputed salience numpy file.
Returns
-------
Instance of Contours object
"""
if not os.path.exists(salience_npy_file):
raise IOError(
"The numpy file {} does not exist".format(salience_npy_file))
S = np.load(salience_npy_file)
S[S < self.low_amp_thresh] = 0.0
times = self.get_times(S.shape[1])
freqs = self.get_freqs()
S[freqs < self.min_contour_freq, :] = 0
S[freqs > self.max_contour_freq, :] = 0
psh = utils.PeakStreamHelper(S,
times,
freqs,
0,
0,
self.n_gap,
self.pitch_cont,
peak_thresh=self.peak_thresh)
c_numbers, c_times, c_freqs, c_sal = psh.peak_streaming()
if len(c_numbers) > 0:
c_numbers, c_times, c_freqs, c_sal = self._sort_contours(
np.array(c_numbers), np.array(c_times), np.array(c_freqs),
np.array(c_sal))
(c_numbers, c_times, c_freqs,
c_sal) = self._postprocess_contours(c_numbers, c_times, c_freqs,
c_sal)
return Contours(c_numbers,
c_times,
c_freqs,
c_sal,
self.sample_rate,
audio_duration=audio_duration)
def test_get_closest_peak(self):
S = np.array([
[0, 0, 0, 0],
[0, 0.002, 0, 0],
[1, 0, 5, 0],
[0, 0.3, 0.1, 0],
[0.1, 0, 0.2, 0],
[0, 0.5, 0, 0.2],
[0, 0, 0, 0]
])
times = np.array([0.05, 0.1, 0.15, 0.2])
freqs = np.array([97.0, 100.0, 103.0, 105.0, 107.0, 109.0, 112.0])
psh = utils.PeakStreamHelper(S, times, freqs, 0.9, 0.9, 3.456, 80)
actual = psh.get_closest_peak(237.2, [2, 4, 5])
expected = 2
self.assertEqual(expected, actual)
def setUp(self):
self.S = np.array([
[0, 0, 0],
[1, 0, 5],
[0, 0.002, 1],
[0.1, 0, 0],
[0, 0, 0]
])
self.times = np.array([0.0, 0.5, 1.0])
self.freqs = np.array([10., 100., 150., 200., 300.])
self.amp_thresh = 0.9
self.dev_thresh = 0.9
self.n_gap = 3.234
self.pitch_cont = 80
self.psh = utils.PeakStreamHelper(
self.S, self.times, self.freqs, self.amp_thresh, self.dev_thresh,
self.n_gap, self.pitch_cont
)
def test_update_largest_peak_list(self):
S = np.array([
[0, 0, 0, 0],
[0, 0.002, 0, 0],
[1, 0, 5, 0],
[0, 0.3, 0.1, 0],
[0.1, 0, 0.2, 0],
[0, 0.5, 0, 0.2],
[0, 0, 0, 0]
])
times = np.array([0.05, 0.1, 0.15, 0.2])
freqs = np.array([97.0, 100.0, 103.0, 105.0, 107.0, 109.0, 112.0])
psh = utils.PeakStreamHelper(S, times, freqs, 0.9, 0.9, 3.456, 80)
expected_avail = np.array([True, True, True, True])
actual_avail = psh.good_peaks_sorted_avail
self.assertTrue(np.allclose(expected_avail, actual_avail))
expected_smallest_idx = 0
actual_smallest_idx = psh.smallest_good_peak_idx
self.assertEqual(expected_smallest_idx, actual_smallest_idx)
psh.update_largest_peak_list(1)
expected_avail = np.array([True, False, True, True])
actual_avail = psh.good_peaks_sorted_avail
self.assertTrue(np.allclose(expected_avail, actual_avail))
expected_smallest_idx = 0
actual_smallest_idx = psh.smallest_good_peak_idx
self.assertEqual(expected_smallest_idx, actual_smallest_idx)
psh.update_largest_peak_list(2)
expected_avail = np.array([False, False, True, True])
actual_avail = psh.good_peaks_sorted_avail
self.assertTrue(np.allclose(expected_avail, actual_avail))
expected_smallest_idx = 2
actual_smallest_idx = psh.smallest_good_peak_idx
self.assertEqual(expected_smallest_idx, actual_smallest_idx)
def test_get_peak_candidates3(self):
S = np.array([
[0, 0, 0, 0],
[0, 0.002, 0, 0],
[1, 0, 5, 0],
[0, 0.3, 0.1, 0],
[0.1, 0, 0.2, 0],
[0, 0.5, 0, 0.002],
[0, 0, 0, 0]
])
times = np.array([0.05, 0.1, 0.15, 0.2])
freqs = np.array([97.0, 100.0, 103.0, 105.0, 107.0, 109.0, 112.0])
psh = utils.PeakStreamHelper(S, times, freqs, 0.9, 0.9, 3.456, 80)
frame_idx = 3
f0_val = 0
expected_cands = None
expected_from_good = None
actual_cands, actual_from_good = psh.get_peak_candidates(
frame_idx, f0_val
)
self.assertEqual(expected_cands, actual_cands)
self.assertEqual(expected_from_good, actual_from_good)
