| #!/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): |
| |
| 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 (120, 160, 240, 320, 360, 480, 640, 720, 960): |
| |
| print('\n--- mdct rot twiddles {:3d} ---'.format(n)) |
| |
| kv = 2 * np.pi * (np.arange(n // 4) + 1/8) / 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 ' ') |
| |
| |
| def mdct_scaling(): |
| |
| print('\n--- mdct scaling ---') |
| ns = np.array([ [ 60, 120, 180, 240, 360], [ 80, 160, 240, 320, 480] ]) |
| print_table(np.sqrt(2 / ns[0])) |
| print_table(np.sqrt(2 / ns[1])) |
| |
| |
| 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 ---') |
| print_table(np.sin((np.arange(8) + 0.5) * (np.pi / 17))) |
| print_table(np.sin((np.arange(8)) * (np.pi / 17))) |
| |
| |
| 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 ] |
| |
| 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 ]: |
| |
| 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() |
| mdct_scaling() |
| |
| inv_table() |
| sns_ge_table() |
| tns_lag_window() |
| tns_quantization_table() |
| quant_iq_table() |
| |
| ltpf_resampler_table() |
| ltpf_interpolate_table() |
| |
| print('') |
