def test_estimate_gradient(fir_filter, art_warning, expected_values):
try:
# Load data for testing
expected_data = expected_values()
x1 = expected_data[0]
x2 = expected_data[1]
x3 = expected_data[2]
grad0 = expected_data[3]
grad1 = expected_data[4]
grad2 = expected_data[5]
result0 = expected_data[6]
result1 = expected_data[7]
result2 = expected_data[8]
# Create signal data
x = np.array([np.array(x1 * 2),
np.array(x2 * 2),
np.array(x3 * 2)],
dtype=object)
# Create input gradient
grad = np.array([np.array(grad0),
np.array(grad1),
np.array(grad2)],
dtype=object)
# Filter params
numerator_coef = np.array([0.1, 0.2, -0.1, -0.2])
if fir_filter:
denominator_coef = np.array([1.0])
else:
denominator_coef = np.array([1.0, 0.1, 0.3, 0.4])
# Create filter
audio_filter = LFilter(numerator_coef=numerator_coef,
denominator_coef=denominator_coef)
# Estimate gradient
estimated_grad = audio_filter.estimate_gradient(x, grad=grad)
# Test
assert estimated_grad.shape == x.shape
np.testing.assert_array_almost_equal(result0,
estimated_grad[0],
decimal=0)
np.testing.assert_array_almost_equal(result1,
estimated_grad[1],
decimal=0)
np.testing.assert_array_almost_equal(result2,
estimated_grad[2],
decimal=0)
except ARTTestException as e:
art_warning(e)
def test_relation_clip_values_error(art_warning):
try:
exc_msg = "Invalid `clip_values`: min >= max."
with pytest.raises(ValueError, match=exc_msg):
LFilter(numerator_coef=np.array([0.1, 0.2, 0.3]),
denominator_coef=np.array([0.1, 0.2, 0.3]),
clip_values=(1, 0))
except ARTTestException as e:
art_warning(e)
def test_triple_clip_values_error(art_warning):
try:
exc_msg = "`clip_values` should be a tuple of 2 floats containing the allowed data range."
with pytest.raises(ValueError, match=exc_msg):
LFilter(
numerator_coef=np.array([0.1, 0.2, 0.3]),
denominator_coef=np.array([0.1, 0.2, 0.3]),
clip_values=(0, 1, 2),
)
except ARTTestException as e:
art_warning(e)
def test_default(art_warning):
try:
# Small data for testing
x = np.array([[0.37, 0.68, 0.63, 0.48, 0.48, 0.18, 0.19]])
# Create filter
audio_filter = LFilter()
# Apply filter
result = audio_filter(x)
# Test
assert result[1] is None
np.testing.assert_array_almost_equal(x, result[0], decimal=0)
except ARTTestException as e:
art_warning(e)
def load_audio_channel(delay, attenuation, pytorch=True):
"""
Return an art LFilter object for a simple delay (multipath) channel
If attenuation == 0 or delay == 0, return an identity channel
Otherwise, return a channel with length equal to delay + 1
NOTE: lfilter truncates the end of the echo, so output length equals input length
"""
delay = int(delay)
attenuation = float(attenuation)
if delay < 0:
raise ValueError(
f"delay {delay} must be a nonnegative number (of samples)")
if delay == 0 or attenuation == 0:
logger.warning("Using an identity channel")
numerator_coef = np.array([1.0])
denominator_coef = np.array([1.0])
else:
if not (-1 <= attenuation <= 1):
logger.warning(f"filter attenuation {attenuation} not in [-1, 1]")
# Simple FIR filter with a single multipath delay
numerator_coef = np.zeros(delay + 1)
numerator_coef[0] = 1.0
numerator_coef[delay] = attenuation
denominator_coef = np.zeros_like(numerator_coef)
denominator_coef[0] = 1.0
if pytorch:
try:
return LFilterPyTorch(numerator_coef=numerator_coef,
denominator_coef=denominator_coef)
except ImportError:
logger.exception(
"PyTorch not available. Resorting to scipy filter")
logger.warning(
"Scipy LFilter does not currently implement proper gradients")
return LFilter(numerator_coef=numerator_coef,
denominator_coef=denominator_coef)
def test_audio_filter(fir_filter, art_warning, expected_values):
try:
# Load data for testing
expected_data = expected_values()
x1 = expected_data[0]
x2 = expected_data[1]
x3 = expected_data[2]
result_0 = expected_data[3]
result_1 = expected_data[4]
result_2 = expected_data[5]
# Create signal data
x = np.array([np.array(x1 * 2),
np.array(x2 * 2),
np.array(x3 * 2)],
dtype=object)
# Filter params
numerator_coef = np.array([0.1, 0.2, -0.1, -0.2])
if fir_filter:
denominator_coef = np.array([1.0])
else:
denominator_coef = np.array([1.0, 0.1, 0.3, 0.4])
# Create filter
audio_filter = LFilter(numerator_coef=numerator_coef,
denominator_coef=denominator_coef)
# Apply filter
result = audio_filter(x)
# Test
assert result[1] is None
np.testing.assert_array_almost_equal(result_0, result[0][0], decimal=0)
np.testing.assert_array_almost_equal(result_1, result[0][1], decimal=0)
np.testing.assert_array_almost_equal(result_2, result[0][2], decimal=0)
except ARTTestException as e:
art_warning(e)
def test_check_params(art_warning):
try:
with pytest.raises(ValueError):
_ = LFilter(numerator_coef=np.array([0.1, 0.2, -0.1, -0.2]),
denominator_coef=[0.0, 0.1, 0.3, 0.4])
with pytest.raises(ValueError):
_ = LFilter(numerator_coef=np.array([0.1, 0.2, -0.1, -0.2]),
denominator_coef=np.array([0.0, 0.1, 0.3, 0.4]))
with pytest.raises(ValueError):
_ = LFilter(numerator_coef=[0.1, 0.2, -0.1, -0.2],
denominator_coef=np.array([1.0, 0.1, 0.3, 0.4]))
with pytest.raises(ValueError):
_ = LFilter(
numerator_coef=np.array([0.1, 0.2, -0.1, -0.2]),
denominator_coef=np.array([1.0, 0.1, 0.3, 0.4]),
axis=1.0,
)
with pytest.raises(ValueError):
_ = LFilter(
numerator_coef=np.array([0.1, 0.2, -0.1, -0.2]),
denominator_coef=np.array([1.0, 0.1, 0.3, 0.4]),
initial_cond=1.0,
)
with pytest.raises(ValueError):
_ = LFilter(
numerator_coef=np.array([0.1, 0.2, -0.1, -0.2]),
denominator_coef=np.array([1.0, 0.1, 0.3, 0.4]),
verbose="True",
)
except ARTTestException as e:
art_warning(e)
