def test_loudness_zwicker_wav():
"""Test function for the script loudness_zwicker_stationary
Test function for the script loudness_zwicker_stationary with
.wav file as input. The input file is provided by ISO 532-1 annex
B3, the compliance is assessed according to section 5.1 of the
standard. One .png compliance plot is generated.
Parameters
----------
None
Outputs
-------
None
"""
# Test signal as input for stationary loudness
# (from ISO 532-1 annex B3)
signal = {
"data_file":
"mosqito/tests/loudness/data/ISO_532-1/Test signal 3 (1 kHz 60 dB).wav",
"N": 4.019,
"N_specif_file":
"mosqito/tests/loudness/data/ISO_532-1/test_signal_3.csv",
}
# Load signal and compute third octave band spectrum
third_spec = load2oct3(True, signal["data_file"], calib=2 * 2**0.5)
# Compute Loudness
N, N_specific = loudness_zwicker_stationary(third_spec['values'])
# Check ISO 532-1 compliance
assert check_compliance(N, N_specific, signal)
def test_loudness_zwicker_3oct():
"""Test function for the script loudness_zwicker_stationary
Test function for the script loudness_zwicker_stationary with
third octave band spectrum as input. The input spectrum is
provided by ISO 532-1 annex B2, the compliance is assessed
according to section 5.1 of the standard. One .png compliance
plot is generated.
Parameters
----------
None
Outputs
-------
None
"""
# Third octave levels as input for stationary loudness
# (from ISO 532-1 annex B2)
test_signal_1 = np.array([
-60,
-60,
78,
79,
89,
72,
80,
89,
75,
87,
85,
79,
86,
80,
71,
70,
72,
71,
72,
74,
69,
65,
67,
77,
68,
58,
45,
30.0,
])
signal = {
"data_file": "Test signal 1.txt",
"N": 83.296,
"N_specif_file": "tests/input/test_signal_1.csv",
}
N, N_specific = loudness_zwicker_stationary(test_signal_1)
loudness = {"values": N, "specific values": N_specific}
tst = check_compliance(loudness, signal, "./tests/output/")
assert tst
def compute_loudness(self, field_type='free'):
""" Method to compute the loudness according to Zwicker's method
Parameter
----------
field-type: string
'free' by default or 'diffuse'
"""
if self.third_spec == None:
self.comp_3oct_spec()
if self.is_stationary == True:
N, N_specific = loudness_zwicker_stationary(
self.third_spec.values, self.third_spec.axes[0].values,
field_type)
elif self.is_stationary == False:
N, N_specific = loudness_zwicker_time(self.third_spec.values,
field_type)
barks = Data1D(name='Frequency Bark scale',
unit='Bark',
values=np.linspace(0.1, 24, int(24 / 0.1)))
if self.is_stationary == True:
self.loudness_zwicker = Data1D(values=[N],
name="Loudness",
unit="Sones")
self.loudness_zwicker_specific = DataFreq(symbol="N'",
axes=[barks],
values=N_specific,
name="Specific loudness",
unit="Sones")
elif self.is_stationary == False:
time = Data1D(symbol="T",
name="Time axis",
unit="s",
values=np.linspace(0,
len(self.signal.values) / self.fs,
num=N.size))
self.loudness_zwicker = DataTime(symbol="N",
axes=[time],
values=N,
name="Loudness",
unit="Sones")
self.loudness_zwicker_specific = DataFreq(symbol="N'",
axes=[barks, time],
values=N_specific,
name="Specific loudness",
unit="Sones")
def comp_loudness_from_3spec(is_stationary,
third_spec,
third_axis=[],
field_type="free"):
"""Acoustic loudness calculation according to Zwicker method for
stationary and time-varying signals.
Parameters
----------
is_stationary: boolean
TRUE if the signal is stationary, FALSE if it is time-varying
third_spec: numpy.array
third octave spectrum
third_axis: numpy.array
third-octav frequency axis
field-type: string
'free' by default or 'diffuse'
Outputs
-------
output : dict
{
"name": "Loudness",
"values": N: float/numpy.array
loudness value
"specific values": N_specific: numpy.array
specific loudness values
"freqs": bark_axis: numpy.array
frequency axis corresponding to N_specific values in bark
}
"""
if is_stationary == True:
N, N_specific = loudness_zwicker_stationary(third_spec, third_axis,
field_type)
elif is_stationary == False:
N, N_specific = loudness_zwicker_time(third_spec, field_type)
if third_axis == []:
# critical band rate scale
third_axis = np.linspace(0.1, 24, int(24 / 0.1))
output = {
"name": "Loudness",
"values": N,
"specific values": N_specific,
"freqs": third_axis,
}
return output
def comp_loudness(is_stationary, signal, fs, field_type="free"):
"""Acoustic loudness calculation according to Zwicker method for
stationary and time-varying signals.
Parameters
----------
is_stationary: boolean
TRUE if the signal is stationary, FALSE if it is time-varying
signal : numpy.array
time signal values
fs : integer
sampling frequency
field-type: string
'free' by default or 'diffuse'
Outputs
-------
output : dict
{
"name": "Loudness",
"values": N: float/numpy.array
loudness value
"specific values": N_specific: numpy.array
specific loudness values
"freqs": bark_axis: numpy.array
frequency axis corresponding to N_specific values in bark
}
"""
if is_stationary == True:
third_spec = comp_third_spec(is_stationary, signal, fs)
N, N_specific = loudness_zwicker_stationary(
third_spec["values"], third_spec["freqs"], field_type
)
elif is_stationary == False:
spec_third, _, _ = calc_third_octave_levels(signal, fs)
N, N_specific = loudness_zwicker_time(spec_third, field_type)
# critical band rate scale
bark_axis = np.linspace(0.1, 24, int(24 / 0.1))
output = {
"name": "Loudness",
"values": N,
"specific values": N_specific,
"freqs": bark_axis,
}
return output
