def test_batched_wpe_step(self):
with self.test_session() as sess:
Y_batch, inv_power_batch = self._get_batch_data()
enhanced_ref_1 = tf_wpe.wpe_step(Y_batch[0], inv_power_batch[0])
enhanced_ref_2 = tf_wpe.wpe_step(
Y_batch[0, ..., :self.T - 20],
inv_power_batch[0, ..., :self.T - 20])
step_enhanced = tf_wpe.batched_wpe_step(
Y_batch,
inv_power_batch,
num_frames=tf.convert_to_tensor([self.T, self.T - 20]))
enhanced, ref1, ref2 = sess.run(
[step_enhanced, enhanced_ref_1, enhanced_ref_2])
np.testing.assert_allclose(enhanced[0], ref1)
np.testing.assert_allclose(enhanced[1, ..., :-20], ref2)
def test_recursive_wpe(self):
with self.test_session() as sess:
T = 5000
D = 2
K = 1
delay = 3
Y = np.random.normal(size=(D, T)) \
+ 1j * np.random.normal(size=(D, T))
Y = tf.convert_to_tensor(Y[None])
power = tf.reduce_mean(tf.real(Y)**2 + tf.imag(Y)**2, axis=1)
inv_power = tf.reciprocal(power)
step_enhanced = tf_wpe.wpe_step(Y, inv_power, taps=K, delay=D)
recursive_enhanced = tf_wpe.recursive_wpe(
tf.transpose(Y, (2, 0, 1)),
tf.transpose(power),
1.,
taps=K,
delay=D,
only_use_final_filters=True)
recursive_enhanced = tf.transpose(recursive_enhanced, (1, 2, 0))
recursive_enhanced, step_enhanced = sess.run(
[recursive_enhanced, step_enhanced])
np.testing.assert_allclose(recursive_enhanced[..., -200:],
step_enhanced[..., -200:],
atol=0.01,
rtol=0.2)
def test_wpe_step(self):
with self.test_session() as sess:
Y = tf.convert_to_tensor(self.Y[None])
enhanced, inv_power = tf_wpe.single_frequency_wpe(Y[0],
iterations=3)
step_enhanced = tf_wpe.wpe_step(Y, inv_power[None])
enhanced, step_enhanced = sess.run([enhanced, step_enhanced])
np.testing.assert_allclose(enhanced, step_enhanced[0])