#!/usr/bin/env python3
#
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import numpy as np
LTPF_H12K8 = np.array([
-2.04305583e-05, -4.46345894e-05, -7.16366399e-05, -1.00101113e-04,
-1.28372848e-04, -1.54543830e-04, -1.76544567e-04, -1.92256960e-04,
-1.99643819e-04, -1.96888686e-04, -1.82538332e-04, -1.55639427e-04,
-1.15860365e-04, -6.35893034e-05, 2.81006480e-19, 7.29218021e-05,
1.52397076e-04, 2.34920777e-04, 3.16378650e-04, 3.92211738e-04,
4.57623849e-04, 5.07824294e-04, 5.38295523e-04, 5.45072918e-04,
5.25022155e-04, 4.76098424e-04, 3.97571380e-04, 2.90200217e-04,
1.56344667e-04, -5.81880142e-19, -1.73252713e-04, -3.56385965e-04,
-5.41155231e-04, -7.18414023e-04, -8.78505232e-04, -1.01171451e-03,
-1.10876706e-03, -1.16134522e-03, -1.16260169e-03, -1.10764097e-03,
-9.93941563e-04, -8.21692190e-04, -5.94017766e-04, -3.17074654e-04,
9.74695082e-19, 3.45293760e-04, 7.04480871e-04, 1.06133447e-03,
1.39837473e-03, 1.69763080e-03, 1.94148675e-03, 2.11357591e-03,
2.19968245e-03, 2.18860625e-03, 2.07294546e-03, 1.84975249e-03,
1.52102188e-03, 1.09397426e-03, 5.81108062e-04, -1.42248266e-18,
-6.27153730e-04, -1.27425140e-03, -1.91223839e-03, -2.51026925e-03,
-3.03703830e-03, -3.46222687e-03, -3.75800672e-03, -3.90053247e-03,
-3.87135231e-03, -3.65866558e-03, -3.25835851e-03, -2.67475555e-03,
-1.92103305e-03, -1.01925433e-03, 1.86962369e-18, 1.09841545e-03,
2.23113197e-03, 3.34830927e-03, 4.39702277e-03, 5.32342672e-03,
6.07510531e-03, 6.60352025e-03, 6.86645399e-03, 6.83034270e-03,
6.47239234e-03, 5.78237521e-03, 4.76401273e-03, 3.43586351e-03,
1.83165284e-03, -2.25189837e-18, -1.99647619e-03, -4.08266886e-03,
-6.17308037e-03, -8.17444895e-03, -9.98882386e-03, -1.15169871e-02,
-1.26621006e-02, -1.33334458e-02, -1.34501120e-02, -1.29444881e-02,
-1.17654154e-02, -9.88086732e-03, -7.28003640e-03, -3.97473021e-03,
2.50961778e-18, 4.58604422e-03, 9.70324900e-03, 1.52512477e-02,
2.11120585e-02, 2.71533724e-02, 3.32324245e-02, 3.92003203e-02,
4.49066644e-02, 5.02043309e-02, 5.49542017e-02, 5.90297032e-02,
6.23209727e-02, 6.47385023e-02, 6.62161245e-02, 6.67132287e-02,
6.62161245e-02, 6.47385023e-02, 6.23209727e-02, 5.90297032e-02,
5.49542017e-02, 5.02043309e-02, 4.49066644e-02, 3.92003203e-02,
3.32324245e-02, 2.71533724e-02, 2.11120585e-02, 1.52512477e-02,
9.70324900e-03, 4.58604422e-03, 2.50961778e-18, -3.97473021e-03,
-7.28003640e-03, -9.88086732e-03, -1.17654154e-02, -1.29444881e-02,
-1.34501120e-02, -1.33334458e-02, -1.26621006e-02, -1.15169871e-02,
-9.98882386e-03, -8.17444895e-03, -6.17308037e-03, -4.08266886e-03,
-1.99647619e-03, -2.25189837e-18, 1.83165284e-03, 3.43586351e-03,
4.76401273e-03, 5.78237521e-03, 6.47239234e-03, 6.83034270e-03,
6.86645399e-03, 6.60352025e-03, 6.07510531e-03, 5.32342672e-03,
4.39702277e-03, 3.34830927e-03, 2.23113197e-03, 1.09841545e-03,
1.86962369e-18, -1.01925433e-03, -1.92103305e-03, -2.67475555e-03,
-3.25835851e-03, -3.65866558e-03, -3.87135231e-03, -3.90053247e-03,
-3.75800672e-03, -3.46222687e-03, -3.03703830e-03, -2.51026925e-03,
-1.91223839e-03, -1.27425140e-03, -6.27153730e-04, -1.42248266e-18,
5.81108062e-04, 1.09397426e-03, 1.52102188e-03, 1.84975249e-03,
2.07294546e-03, 2.18860625e-03, 2.19968245e-03, 2.11357591e-03,
1.94148675e-03, 1.69763080e-03, 1.39837473e-03, 1.06133447e-03,
7.04480871e-04, 3.45293760e-04, 9.74695082e-19, -3.17074654e-04,
-5.94017766e-04, -8.21692190e-04, -9.93941563e-04, -1.10764097e-03,
-1.16260169e-03, -1.16134522e-03, -1.10876706e-03, -1.01171451e-03,
-8.78505232e-04, -7.18414023e-04, -5.41155231e-04, -3.56385965e-04,
-1.73252713e-04, -5.81880142e-19, 1.56344667e-04, 2.90200217e-04,
3.97571380e-04, 4.76098424e-04, 5.25022155e-04, 5.45072918e-04,
5.38295523e-04, 5.07824294e-04, 4.57623849e-04, 3.92211738e-04,
3.16378650e-04, 2.34920777e-04, 1.52397076e-04, 7.29218021e-05,
2.81006480e-19, -6.35893034e-05, -1.15860365e-04, -1.55639427e-04,
-1.82538332e-04, -1.96888686e-04, -1.99643819e-04, -1.92256960e-04,
-1.76544567e-04, -1.54543830e-04, -1.28372848e-04, -1.00101113e-04,
-7.16366399e-05, -4.46345894e-05, -2.04305583e-05
])
LTPF_HI = np.array([
6.69885837e-03, 3.96711478e-02, 1.06999186e-01, 2.09880463e-01,
3.35690625e-01, 4.59220930e-01, 5.50075002e-01, 5.83527575e-01,
5.50075002e-01, 4.59220930e-01, 3.35690625e-01, 2.09880463e-01,
1.06999186e-01, 3.96711478e-02, 6.69885837e-03
])
def print_table(t, m=4):
for (i, v) in enumerate(t):
print('{:14.8e},'.format(v), end = '\n' if i%m == m-1 else ' ')
if len(t) % 4:
print('')
def mdct_fft_twiddles():
for n in (10, 20, 30, 40, 60, 80, 90, 120, 160, 180, 240, 480):
print('\n--- fft bf2 twiddles {:3d} ---'.format(n))
kv = -2 * np.pi * np.arange(n // 2) / n
for (i, k) in enumerate(kv):
print('{{ {:14.7e}, {:14.7e} }},'.format(np.cos(k), np.sin(k)),
end = '\n' if i%2 == 1 else ' ')
for n in (15, 45):
print('\n--- fft bf3 twiddles {:3d} ---'.format(n))
kv = -2 * np.pi * np.arange(n) / n
for k in kv:
print(('{{ {{ {:14.7e}, {:14.7e} }},' +
' {{ {:14.7e}, {:14.7e} }} }},').format(
np.cos(k), np.sin(k), np.cos(2*k), np.sin(2*k)))
def mdct_rot_twiddles():
for n in (40, 80, 120, 160, 240, 320, 360, 480, 640, 720, 960, 1920):
print('\n--- mdct rot twiddles {:3d} ---'.format(n))
kv = 2 * np.pi * (np.arange(n // 4) + 1/8) / n
scale = np.sqrt( np.sqrt( 4 / n ) )
for (i, k) in enumerate(kv):
print('{{ {:14.7e}, {:14.7e} }},'.format(
np.cos(k) * scale, np.sin(k) * scale),
end = '\n' if i%2 == 1 else ' ')
def tns_lag_window():
print('\n--- tns lag window ---')
print_table(np.exp(-0.5 * (0.02 * np.pi * np.arange(9)) ** 2))
def tns_quantization_table():
print('\n--- tns quantization table ---')
xe = np.sin((np.arange(8) + 0.5) * (np.pi / 17))
xe = np.rint(xe * 2**15).astype(np.int16)
xd = np.sin(np.arange(9) * (np.pi / 17))
xd = np.rint(xd * 2**15).astype(np.int16)
for x in (xe, xd):
print()
for (i, xi) in enumerate(x):
print('0x{:04x}p-15,'.format(xi), end = '\n' if i%4 == 4-1 else ' ')
if len(x) % 4:
print()
def quant_iq_table():
print('\n--- quantization iq table ---')
print_table(10 ** (np.arange(65) / 28))
def sns_ge_table():
g_tilt_table = [ 14, 18, 22, 26, 30, 34 ]
for (sr, g_tilt) in enumerate(g_tilt_table):
print('\n--- sns ge table, sr:{} ---'.format(sr))
print_table(10 ** ((np.arange(64) * g_tilt) / 630))
def inv_table():
print('\n--- inv table ---')
print_table(np.append(np.zeros(1), 1 / np.arange(1, 28)))
def ltpf_resampler_table():
for sr in [ 8, 16, 32, 24, 48, 96 ]:
r = 192 // sr
k = 64 if r & (r-1) else 192
p = (192 // k) * (k // sr)
q = p * (0.5 if sr == 8 else 1)
print('\n--- LTPF resampler {:d} KHz to 12.8 KHz ---'.format(sr))
h = np.rint(np.append(LTPF_H12K8, 0.) * q * 2**15).astype(int)
h = h.reshape((len(h) // p, p)).T
h = np.flip(h, axis=0)
print('... Gain:', np.max(np.sum(np.abs(h), axis=1)) / 32768.)
for i in range(0, len(h), 192 // k):
for j in range(0, len(h[i]), 10):
print('{:5d}, {:5d}, {:5d}, {:5d}, {:5d}, '
'{:5d}, {:5d}, {:5d}, {:5d}, {:5d},'.format(
h[i][j+0], h[i][j+1], h[i][j+2], h[i][j+3], h[i][j+4],
h[i][j+5], h[i][j+6], h[i][j+7], h[i][j+8], h[i][j+9]))
def ltpf_interpolate_table():
print('\n--- LTPF interpolation ---')
h = np.rint(np.append(LTPF_HI, 0.) * 2**15).astype(int)
h = h.reshape(len(h) // 4, 4).T
h = np.flip(h, axis=0)
print('... Gain:', np.max(np.sum(np.abs(h), axis=1)) / 32768.)
for i in range(len(h)):
print('{:5d}, {:5d}, {:5d}, {:5d}'.format(
h[i][0], h[i][1], h[i][2], h[i][3]))
if __name__ == '__main__':
mdct_fft_twiddles()
mdct_rot_twiddles()
inv_table()
sns_ge_table()
tns_lag_window()
tns_quantization_table()
quant_iq_table()
ltpf_resampler_table()
ltpf_interpolate_table()
print('')