def test_roughness():
"""Test function for the roughness calculation of a audio signal
Test function for the script "comp_roughness" method with signal array
as input. The input signals are chosen according to the article "Psychoacoustical
roughness: implementation of an optimized model" by Daniel and Weber in 1997.
The figure 3 is used to compare amplitude-modulated signals created according to
their carrier frequency and modulation frequency to the article results.
One .png compliance plot is generated.
Parameters
----------
None
Outputs
-------
None
"""
# Stimulus generation
stimulus = signal_test(fc=1000, fmod=70, mdepth=1, fs=44100, d=0.2, dB=60)
# Roughness calculation
R = comp_roughness(stimulus, fs=44100, overlap=0)
# Check compliance
tst = check_compliance(R)
assert tst
def validation_roughness(signal):
"""Validation function for the roughness calculation of an audio signal
Validation function for the Audio_signal class "comp_roughness" method with signal array
as input. The input signals are chosen according to the article "Psychoacoustical
roughness: implementation of an optimized model" by Daniel and Weber in 1997.
The figure 3 is used to compare amplitude-modulated signals created according to
their carrier frequency and modulation frequency to the article results.
One .png compliance plot is generated.
Parameters
----------
None
Outputs
-------
None
"""
# Stimulus parameters
duration = 0.2
fs = 44100
level = 60
mdepth = 1
# Overlapping definition for roughness calculation
overlap = 0
# Roughness calculation for each carrier frequency
R = np.zeros((len(signal["fc"])), dtype=dict)
for ind_fc in range(len(signal["fc"])):
stimulus = signal_test(signal["fc"][ind_fc], signal["fmod"], mdepth,
fs, duration, level)
roughness = comp_roughness(stimulus, fs, overlap)
R[ind_fc] = roughness["values"][0]
# Check compliance
tst = check_compliance(R, signal)
return tst
def comparison_roughness():
# Parameters definition for signals generation
fs = 44100
carrier = np.array([250, 1000, 4000])
fmod = np.array([
10, 20, 30, 40, 50, 60, 70, 80, 100, 120, 140, 160, 180, 200, 300, 400
])
mdepth = 1
duration = 0.2
dB = 60
# Overlapping definition for roughness calculation
overlap = 0
# Initialization
r_zf = np.zeros((len(carrier), len(fmod)))
r_dw = np.zeros((len(carrier), len(fmod)))
r_ps = np.zeros((len(carrier), len(fmod)))
R = np.zeros((len(carrier), len(fmod)))
# Each carrier frequency is considered separately
for ind_fc in range(3):
# Roughness reference values
r_zf[ind_fc, :] = ref_zf(carrier[ind_fc], fmod)
r_dw[ind_fc, :] = ref_dw(carrier[ind_fc], fmod)
r_ps[ind_fc, :] = ref_ps(carrier[ind_fc], fmod)
# Roughness calculation for each modulation frequency
for ind_fmod in range(fmod.size):
signal = signal_test(carrier[ind_fc], fmod[ind_fmod], mdepth, fs,
duration, dB)
rtemp = comp_roughness(signal, fs, overlap)
R[ind_fc, ind_fmod] = rtemp["values"]
fig, (axs1, axs2, axs3) = plt.subplots(3,
1,
figsize=(6, 6),
constrained_layout=True)
axs1.plot(fmod[0:12],
r_zf[0, 0:12],
linestyle="dotted",
color="black",
label="reference")
axs1.plot(fmod[0:12],
r_dw[0, 0:12],
marker="x",
color="red",
label="Daniel and Weber")
axs1.plot(fmod[0:12],
r_ps[0, 0:12],
marker="o",
color="#0069a1",
label="Psysound")
axs1.plot(fmod[0:12],
R[0, 0:12],
marker="s",
color="#69c3c5",
label="mosqito")
axs1.set(xlim=(0, 170), ylim=(0, 1.1))
axs1.set_title("Carrier frequency of 250 Hz", fontsize=11)
axs1.legend(loc="upper right", shadow=True)
axs1.set_ylabel("Roughness [asper]")
axs1.set_xlabel("Modulation frequency [Hz]")
axs2.plot(fmod[0:12], r_zf[1, 0:12], linestyle="dotted", color="black")
axs2.plot(fmod[0:12], r_dw[1, 0:12], marker="x", color="red")
axs2.plot(fmod[0:12], r_ps[1, 0:12], marker="o", color="#0069a1")
axs2.plot(fmod[0:12], R[1, 0:12], marker="s", color="#69c3c5")
axs2.set(xlim=(0, 170), ylim=(0, 1.1))
axs2.set_ylabel("Roughness [asper]")
axs2.set_xlabel("Modulation frequency [Hz]")
axs2.set_title("Carrier frequency of 1000 Hz", fontsize=11)
axs3.plot(fmod[0:12], r_zf[2, 0:12], linestyle="dotted", color="black")
axs3.plot(fmod[0:12], r_dw[2, 0:12], marker="x", color="red")
axs3.plot(fmod[0:12], r_ps[2, 0:12], marker="o", color="#0069a1")
axs3.plot(fmod[0:12], R[2, 0:12], marker="s", color="#69c3c5")
axs3.set(xlim=(0, 170), ylim=(0, 1.1))
axs3.set_ylabel("Roughness [asper]")
axs3.set_xlabel("Modulation frequency [Hz]")
axs3.set_title("Carrier frequency of 4000 Hz", fontsize=11)
fig.savefig("roughness_implementations_comparison" + ".png", format="png")