def test_inverse_stft_window_fn(self):
"""Test that inverse_stft_window_fn has unit gain at each window phase."""
# Tuples of (frame_length, frame_step).
test_configs = [
(256, 32),
(256, 64),
(128, 25),
(127, 32),
(128, 64),
]
for (frame_length, frame_step) in test_configs:
hann_window = window_ops.hann_window(frame_length,
dtype=dtypes.float32)
inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
inverse_window = inverse_window_fn(frame_length,
dtype=dtypes.float32)
with self.cached_session(use_gpu=True) as sess:
hann_window, inverse_window = self.evaluate(
[hann_window, inverse_window])
# Expect unit gain at each phase of the window.
product_window = hann_window * inverse_window
for i in range(frame_step):
self.assertAllClose(1.0, np.sum(product_window[i::frame_step]))
def test_inverse_stft_window_fn_special_case(self):
"""Test inverse_stft_window_fn in special overlap = 3/4 case."""
# Cases in which frame_length is an integer multiple of 4 * frame_step are
# special because they allow exact reproduction of the waveform with a
# squared Hann window (Hann window in both forward and reverse transforms).
# In the case where frame_length = 4 * frame_step, that combination
# produces a constant gain of 1.5, and so the corrected window will be the
# Hann window / 1.5.
# Tuples of (frame_length, frame_step).
test_configs = [
(256, 64),
(128, 32),
]
for (frame_length, frame_step) in test_configs:
hann_window = window_ops.hann_window(frame_length,
dtype=dtypes.float32)
inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
inverse_window = inverse_window_fn(frame_length,
dtype=dtypes.float32)
with self.cached_session(use_gpu=True) as sess:
hann_window, inverse_window = self.evaluate(
[hann_window, inverse_window])
self.assertAllClose(hann_window, inverse_window * 1.5)
def test_stft_round_trip(self, signal_length, frame_length, frame_step,
fft_length, np_rtype, threshold,
corrected_threshold):
# Generate a random white Gaussian signal.
signal = np.random.normal(size=signal_length).astype(np_rtype)
stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length,
pad_end=False)
inverse_stft = spectral_ops.inverse_stft(stft, frame_length, frame_step,
fft_length)
inverse_stft_corrected = spectral_ops.inverse_stft(
stft, frame_length, frame_step, fft_length,
window_fn=spectral_ops.inverse_stft_window_fn(frame_step))
inverse_stft, inverse_stft_corrected = self.evaluate(
[inverse_stft, inverse_stft_corrected])
# Truncate signal to the size of inverse stft.
signal = signal[:inverse_stft.shape[0]]
# Ignore the frame_length samples at either edge.
signal = signal[frame_length:-frame_length]
inverse_stft = inverse_stft[frame_length:-frame_length]
inverse_stft_corrected = inverse_stft_corrected[
frame_length:-frame_length]
# Check that the inverse and original signal are close up to a scale
# factor.
inverse_stft_scaled = inverse_stft / np.mean(np.abs(inverse_stft))
signal_scaled = signal / np.mean(np.abs(signal))
self.assertLess(np.std(inverse_stft_scaled - signal_scaled), threshold)
# Check that the inverse with correction and original signal are close.
self.assertLess(np.std(inverse_stft_corrected - signal),
corrected_threshold)
def test_inverse_stft_window_fn_special_case(self):
"""Test inverse_stft_window_fn in special overlap = 3/4 case."""
# Cases in which frame_length is an integer multiple of 4 * frame_step are
# special because they allow exact reproduction of the waveform with a
# squared Hann window (Hann window in both forward and reverse transforms).
# In the case where frame_length = 4 * frame_step, that combination
# produces a constant gain of 1.5, and so the corrected window will be the
# Hann window / 1.5.
# Tuples of (frame_length, frame_step).
test_configs = [
(256, 64),
(128, 32),
]
for (frame_length, frame_step) in test_configs:
hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32)
inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32)
with self.cached_session(use_gpu=True) as sess:
hann_window, inverse_window = self.evaluate(
[hann_window, inverse_window])
self.assertAllClose(hann_window, inverse_window * 1.5)
def test_stft_round_trip(self):
# Tuples of (signal_length, frame_length, frame_step, fft_length,
# threshold, corrected_threshold).
test_configs = [
# 87.5% overlap.
(4096, 256, 32, 256, 1e-5, 1e-6),
# 75% overlap.
(4096, 256, 64, 256, 1e-5, 1e-6),
# Odd frame hop.
(4096, 128, 25, 128, 1e-3, 1e-6),
# Odd frame length.
(4096, 127, 32, 128, 1e-3, 1e-6),
# 50% overlap.
(4096, 128, 64, 128, 0.40, 1e-6),
]
for (signal_length, frame_length, frame_step, fft_length, threshold,
corrected_threshold) in test_configs:
# Generate a random white Gaussian signal.
signal = random_ops.random_normal([signal_length])
with spectral_ops_test_util.fft_kernel_label_map(), (
self.cached_session(use_gpu=True)) as sess:
stft = spectral_ops.stft(signal,
frame_length,
frame_step,
fft_length,
pad_end=False)
inverse_stft = spectral_ops.inverse_stft(
stft, frame_length, frame_step, fft_length)
inverse_stft_corrected = spectral_ops.inverse_stft(
stft,
frame_length,
frame_step,
fft_length,
window_fn=spectral_ops.inverse_stft_window_fn(frame_step))
signal, inverse_stft, inverse_stft_corrected = sess.run(
[signal, inverse_stft, inverse_stft_corrected])
# Truncate signal to the size of inverse stft.
signal = signal[:inverse_stft.shape[0]]
# Ignore the frame_length samples at either edge.
signal = signal[frame_length:-frame_length]
inverse_stft = inverse_stft[frame_length:-frame_length]
inverse_stft_corrected = inverse_stft_corrected[
frame_length:-frame_length]
# Check that the inverse and original signal are close up to a scale
# factor.
inverse_stft_scaled = inverse_stft / np.mean(
np.abs(inverse_stft))
signal_scaled = signal / np.mean(np.abs(signal))
self.assertLess(np.std(inverse_stft_scaled - signal_scaled),
threshold)
# Check that the inverse with correction and original signal are close.
self.assertLess(np.std(inverse_stft_corrected - signal),
corrected_threshold)
def test_inverse_stft_window_fn(self, frame_length, frame_step):
"""Test that inverse_stft_window_fn has unit gain at each window phase."""
hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32)
inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32)
hann_window, inverse_window = self.evaluate([hann_window, inverse_window])
# Expect unit gain at each phase of the window.
product_window = hann_window * inverse_window
for i in range(frame_step):
self.assertAllClose(1.0, np.sum(product_window[i::frame_step]))
def test_inverse_stft_window_fn_special_case(self, frame_length, frame_step): """Test inverse_stft_window_fn in special overlap = 3/4 case.""" # Cases in which frame_length is an integer multiple of 4 * frame_step are # special because they allow exact reproduction of the waveform with a # squared Hann window (Hann window in both forward and reverse transforms). # In the case where frame_length = 4 * frame_step, that combination # produces a constant gain of 1.5, and so the corrected window will be the # Hann window / 1.5. hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32) inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step) inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32) self.assertAllClose(hann_window, inverse_window * 1.5)
def test_stft_round_trip(self):
# Tuples of (signal_length, frame_length, frame_step, fft_length,
# threshold, corrected_threshold).
test_configs = [
# 87.5% overlap.
(4096, 256, 32, 256, 1e-5, 1e-6),
# 75% overlap.
(4096, 256, 64, 256, 1e-5, 1e-6),
# Odd frame hop.
(4096, 128, 25, 128, 1e-3, 1e-6),
# Odd frame length.
(4096, 127, 32, 128, 1e-3, 1e-6),
# 50% overlap.
(4096, 128, 64, 128, 0.40, 1e-6),
]
for (signal_length, frame_length, frame_step, fft_length, threshold,
corrected_threshold) in test_configs:
# Generate a random white Gaussian signal.
signal = random_ops.random_normal([signal_length])
with spectral_ops_test_util.fft_kernel_label_map(), (
self.cached_session(use_gpu=True)) as sess:
stft = spectral_ops.stft(signal, frame_length, frame_step, fft_length,
pad_end=False)
inverse_stft = spectral_ops.inverse_stft(stft, frame_length, frame_step,
fft_length)
inverse_stft_corrected = spectral_ops.inverse_stft(
stft, frame_length, frame_step, fft_length,
window_fn=spectral_ops.inverse_stft_window_fn(frame_step))
signal, inverse_stft, inverse_stft_corrected = sess.run(
[signal, inverse_stft, inverse_stft_corrected])
# Truncate signal to the size of inverse stft.
signal = signal[:inverse_stft.shape[0]]
# Ignore the frame_length samples at either edge.
signal = signal[frame_length:-frame_length]
inverse_stft = inverse_stft[frame_length:-frame_length]
inverse_stft_corrected = inverse_stft_corrected[
frame_length:-frame_length]
# Check that the inverse and original signal are close up to a scale
# factor.
inverse_stft_scaled = inverse_stft / np.mean(np.abs(inverse_stft))
signal_scaled = signal / np.mean(np.abs(signal))
self.assertLess(np.std(inverse_stft_scaled - signal_scaled), threshold)
# Check that the inverse with correction and original signal are close.
self.assertLess(np.std(inverse_stft_corrected - signal),
corrected_threshold)
def test_inverse_stft_window_fn(self):
"""Test that inverse_stft_window_fn has unit gain at each window phase."""
# Tuples of (frame_length, frame_step).
test_configs = [
(256, 32),
(256, 64),
(128, 25),
(127, 32),
(128, 64),
]
for (frame_length, frame_step) in test_configs:
hann_window = window_ops.hann_window(frame_length, dtype=dtypes.float32)
inverse_window_fn = spectral_ops.inverse_stft_window_fn(frame_step)
inverse_window = inverse_window_fn(frame_length, dtype=dtypes.float32)
with self.cached_session(use_gpu=True) as sess:
hann_window, inverse_window = self.evaluate(
[hann_window, inverse_window])
# Expect unit gain at each phase of the window.
product_window = hann_window * inverse_window
for i in range(frame_step):
self.assertAllClose(1.0, np.sum(product_window[i::frame_step]))
