88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
#!/usr/bin/env python3
|
|
"""fit_lut.py — полная LUT «уровень->маска» (B.11).
|
|
|
|
Все 3 набора (0/-7/-18 dBFS) ложатся на ОДНУ кривую C_norm = f(L0/res),
|
|
f = min+(max-min)*(k*z)^(1/c) — gamma-LUT из декомпиляции (FUN_180563440,
|
|
кривая param_1+0x188, линейный флаг: val=min+(max-min)*x^(1/c)).
|
|
"""
|
|
import numpy as np
|
|
from scipy.optimize import least_squares
|
|
|
|
FS = 44100.0
|
|
DEPTH = 0.8639736175537109
|
|
QS = [0.1, 0.2, 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0]
|
|
DUAL = np.array([(10.220, 15.224), (10.219, 13.963), (10.219, 12.947),
|
|
(10.219, 11.725), (10.218, 11.119), (10.216, 10.689),
|
|
(10.210, 10.412), (10.203, 10.305), (10.182, 10.225),
|
|
(10.113, 10.183), (9.822, 10.165)])
|
|
FCS = [800.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1200.0]
|
|
T1KQ = np.array([7.868, 8.536, 8.726, 8.788, 8.729, 8.575, 8.115])
|
|
T1K = np.array([14.548, 15.332, 15.553, 15.626, 15.557, 15.378, 14.840])
|
|
L_DUAL = 10 ** (-7.142 / 20)
|
|
L_T1KQ = 10 ** (-18.063 / 20)
|
|
L_T1K = 1.0
|
|
TILT = {500: 1.414, 1000: 1.454, 2000: 1.795}
|
|
|
|
|
|
def res_at(ft, fc, Q, g):
|
|
w0 = fc * 2 * np.pi / FS
|
|
c, s = np.cos(w0), np.sin(w0)
|
|
p = (s * 0.5) / Q
|
|
a, a2 = p * g, p / g
|
|
A = [a + 1, -2 * c, 1 - a]
|
|
B = [a2 + 1, -2 * c, 1 - a2]
|
|
w = 2 * np.pi * ft / FS
|
|
z = np.exp(-1j * w)
|
|
return np.abs(2.0 * (B[0] + B[1] * z + B[2] * z * z) /
|
|
(A[0] + A[1] * z + A[2] * z * z))
|
|
|
|
|
|
def lut(z, mn, mx, c, k):
|
|
v = mn + (mx - mn) * (k * z) ** (1.0 / c)
|
|
return np.minimum(v, mx)
|
|
|
|
|
|
def model(p):
|
|
Q, g, mn, mx, c, k = p
|
|
out = []
|
|
for q in QS:
|
|
for f in (500.0, 2000.0):
|
|
r = res_at(f, 500, q, g)
|
|
C = DEPTH * TILT[f] * lut(L_DUAL / r, mn, mx, c, k)
|
|
out.append(-20 * np.log10(max(1 - C, 1e-9)))
|
|
for i, fc in enumerate(FCS):
|
|
r = res_at(1000, fc, 0.9999978, g)
|
|
C = DEPTH * TILT[1000] * lut(L_T1KQ / r, mn, mx, c, k)
|
|
out.append(-20 * np.log10(max(1 - C, 1e-9)))
|
|
C = DEPTH * TILT[1000] * lut(L_T1K / r, mn, mx, c, k)
|
|
out.append(-20 * np.log10(max(1 - C, 1e-9)))
|
|
return np.array(out)
|
|
|
|
|
|
def run():
|
|
meas = np.array(list(DUAL.ravel()) + list(T1KQ) + list(T1K))
|
|
x0 = [0.9, 4.13, 0.0, 1.0, 6.0, 1.0]
|
|
r = least_squares(lambda p: model(p) - meas, x0,
|
|
bounds=([0.1, 0.5, 0.0, 0.5, 1.0, 1e-4],
|
|
[5, 12, 2.0, 5.0, 30.0, 50.0]),
|
|
max_nfev=20000, xtol=1e-12, ftol=1e-12)
|
|
Q, g, mn, mx, c, k = r.x
|
|
rmse = np.sqrt(np.mean((model(r.x) - meas) ** 2))
|
|
print(f'LUT-FIT rmse={rmse:.4f} dB Q={Q:.3f} gain={g:.3f}')
|
|
print(f'LUT: min={mn:.3f} max={mx:.3f} gamma={c:.3f} k={k:.4f}')
|
|
pred = model(r.x)
|
|
print('--- dual_b1q ---')
|
|
for i, q in enumerate(QS):
|
|
print(f'q={q:5.1f} {DUAL[i,0]:7.3f}/{pred[2*i]:7.3f} '
|
|
f'{DUAL[i,1]:7.3f}/{pred[2*i+1]:7.3f}')
|
|
print('--- t1kq -18dB ---')
|
|
for i, fc in enumerate(FCS):
|
|
print(f'fc={fc:5.0f} {T1KQ[i]:6.3f}/{pred[22+i]:6.3f}')
|
|
print('--- t1k 0dB ---')
|
|
for i, fc in enumerate(FCS):
|
|
print(f'fc={fc:5.0f} {T1K[i]:6.3f}/{pred[29+i]:6.3f}')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run()
|