def test_all_features(self):
if self.matlab_engine is None:
self.skipTest("Matlab engine not available")
# WhiteNoise input
fs = 44100
x_py = np.random.uniform(-1, 1, size=(fs // 2, 1)) # 0.5 sec
x_m = matlab.double(x_py.tolist())
for feature in pyACA.getFeatureList():
self.logger.info('Testing feature:' + feature)
v_out_py, t_py = pyACA.computeFeature(feature, x_py, fs)
# Note: fs must be float when passing to Matlab
v_out_m, t_m = self.matlab_engine.ComputeFeature(feature,
x_m,
float(fs),
nargout=2)
v_out_py = v_out_py.squeeze()
v_out_m = np.asarray(v_out_m).squeeze()
with self.subTest(msg=feature):
npt.assert_almost_equal(
v_out_py,
v_out_m,
decimal=7,
err_msg="MATLAB crosscheck test failed for " + feature)
def test_compute_feature_num_blocks(self):
blockLength = 256 # Using smaller blocksize to reduce time required to test (~7.3 sec)
hopLength = 128
fs = 44100
inputData = np.random.uniform(-1, 1, size=(2 * blockLength, 1))
for inputSize in range(1, 2 * blockLength):
x = inputData[:inputSize]
expectedNumBlocks = self.calcNumBlocks(x.size + blockLength,
blockLength, hopLength)
for feature in pyACA.getFeatureList('all'):
with self.subTest(msg=feature + ':' + str(inputSize)):
out, t = pyACA.computeFeature(feature,
x,
fs,
iBlockLength=blockLength,
iHopLength=hopLength)
npt.assert_equal(out.shape[-1], expectedNumBlocks)
def test_spectral_features_for_1d_input(self):
fs = 44100
X = np.zeros(1025)
features = pyACA.getFeatureList('spectral')
features.remove('SpectralMfccs')
features.remove('SpectralPitchChroma')
for feature in features:
with self.subTest(msg=feature):
featureFunc = getattr(pyACA, "Feature" + feature)
out = featureFunc(X, fs)
npt.assert_equal(out.shape, ())
with self.subTest(msg='SpectralMfccs'):
out = pyACA.FeatureSpectralMfccs(X, fs)
npt.assert_equal(out.shape, (13, ))
with self.subTest(msg='SpectralPitchChroma'):
out = pyACA.FeatureSpectralPitchChroma(X, fs)
npt.assert_equal(out.shape, (12, ))
def test_spectral_features_for_2d_input(self):
fs = 44100
features = pyACA.getFeatureList('spectral')
features.remove('SpectralMfccs')
features.remove('SpectralPitchChroma')
# Test for 2d input with only one block
X = np.zeros((1025, 1))
for feature in features:
with self.subTest(msg=feature):
featureFunc = getattr(pyACA, "Feature" + feature)
out = featureFunc(X, fs)
npt.assert_equal(out.shape, (1, ))
with self.subTest(msg='SpectralMfccs'):
out = pyACA.FeatureSpectralMfccs(X, fs)
npt.assert_equal(out.shape, (13, 1))
with self.subTest(msg='SpectralPitchChroma'):
out = pyACA.FeatureSpectralPitchChroma(X, fs)
npt.assert_equal(out.shape, (12, 1))
# Test for 2d input with only multiple block
X = np.zeros((1025, 16))
for feature in features:
with self.subTest(msg=feature):
featureFunc = getattr(pyACA, "Feature" + feature)
out = featureFunc(X, fs)
npt.assert_equal(out.shape, (16, ))
with self.subTest(msg='SpectralMfccs'):
out = pyACA.FeatureSpectralMfccs(X, fs)
npt.assert_equal(out.shape, (13, 16))
with self.subTest(msg='SpectralPitchChroma'):
out = pyACA.FeatureSpectralPitchChroma(X, fs)
npt.assert_equal(out.shape, (12, 16))