1/* 2 * SpanDSP - a series of DSP components for telephony 3 * 4 * echo.c - A line echo canceller. This code is being developed 5 * against and partially complies with G168. 6 * 7 * Written by Steve Underwood <steveu@coppice.org> 8 * and David Rowe <david_at_rowetel_dot_com> 9 * 10 * Copyright (C) 2001 Steve Underwood and 2007 David Rowe 11 * 12 * All rights reserved. 13 * 14 * This program is free software; you can redistribute it and/or modify 15 * it under the terms of the GNU General Public License version 2, as 16 * published by the Free Software Foundation. 17 * 18 * This program is distributed in the hope that it will be useful, 19 * but WITHOUT ANY WARRANTY; without even the implied warranty of 20 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 21 * GNU General Public License for more details. 22 * 23 * You should have received a copy of the GNU General Public License 24 * along with this program; if not, write to the Free Software 25 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. 26 */ 27 28#ifndef __ECHO_H 29#define __ECHO_H 30 31/* 32Line echo cancellation for voice 33 34What does it do? 35 36This module aims to provide G.168-2002 compliant echo cancellation, to remove 37electrical echoes (e.g. from 2-4 wire hybrids) from voice calls. 38 39 40How does it work? 41 42The heart of the echo cancellor is FIR filter. This is adapted to match the 43echo impulse response of the telephone line. It must be long enough to 44adequately cover the duration of that impulse response. The signal transmitted 45to the telephone line is passed through the FIR filter. Once the FIR is 46properly adapted, the resulting output is an estimate of the echo signal 47received from the line. This is subtracted from the received signal. The result 48is an estimate of the signal which originated at the far end of the line, free 49from echos of our own transmitted signal. 50 51The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, and 52was introduced in 1960. It is the commonest form of filter adaption used in 53things like modem line equalisers and line echo cancellers. There it works very 54well. However, it only works well for signals of constant amplitude. It works 55very poorly for things like speech echo cancellation, where the signal level 56varies widely. This is quite easy to fix. If the signal level is normalised - 57similar to applying AGC - LMS can work as well for a signal of varying 58amplitude as it does for a modem signal. This normalised least mean squares 59(NLMS) algorithm is the commonest one used for speech echo cancellation. Many 60other algorithms exist - e.g. RLS (essentially the same as Kalman filtering), 61FAP, etc. Some perform significantly better than NLMS. However, factors such 62as computational complexity and patents favour the use of NLMS. 63 64A simple refinement to NLMS can improve its performance with speech. NLMS tends 65to adapt best to the strongest parts of a signal. If the signal is white noise, 66the NLMS algorithm works very well. However, speech has more low frequency than 67high frequency content. Pre-whitening (i.e. filtering the signal to flatten its 68spectrum) the echo signal improves the adapt rate for speech, and ensures the 69final residual signal is not heavily biased towards high frequencies. A very 70low complexity filter is adequate for this, so pre-whitening adds little to the 71compute requirements of the echo canceller. 72 73An FIR filter adapted using pre-whitened NLMS performs well, provided certain 74conditions are met: 75 76 - The transmitted signal has poor self-correlation. 77 - There is no signal being generated within the environment being 78 cancelled. 79 80The difficulty is that neither of these can be guaranteed. 81 82If the adaption is performed while transmitting noise (or something fairly 83noise like, such as voice) the adaption works very well. If the adaption is 84performed while transmitting something highly correlative (typically narrow 85band energy such as signalling tones or DTMF), the adaption can go seriously 86wrong. The reason is there is only one solution for the adaption on a near 87random signal - the impulse response of the line. For a repetitive signal, 88there are any number of solutions which converge the adaption, and nothing 89guides the adaption to choose the generalised one. Allowing an untrained 90canceller to converge on this kind of narrowband energy probably a good thing, 91since at least it cancels the tones. Allowing a well converged canceller to 92continue converging on such energy is just a way to ruin its generalised 93adaption. A narrowband detector is needed, so adapation can be suspended at 94appropriate times. 95 96The adaption process is based on trying to eliminate the received signal. When 97there is any signal from within the environment being cancelled it may upset 98the adaption process. Similarly, if the signal we are transmitting is small, 99noise may dominate and disturb the adaption process. If we can ensure that the 100adaption is only performed when we are transmitting a significant signal level, 101and the environment is not, things will be OK. Clearly, it is easy to tell when 102we are sending a significant signal. Telling, if the environment is generating 103a significant signal, and doing it with sufficient speed that the adaption will 104not have diverged too much more we stop it, is a little harder. 105 106The key problem in detecting when the environment is sourcing significant 107energy is that we must do this very quickly. Given a reasonably long sample of 108the received signal, there are a number of strategies which may be used to 109assess whether that signal contains a strong far end component. However, by the 110time that assessment is complete the far end signal will have already caused 111major mis-convergence in the adaption process. An assessment algorithm is 112needed which produces a fairly accurate result from a very short burst of far 113end energy. 114 115How do I use it? 116 117The echo cancellor processes both the transmit and receive streams sample by 118sample. The processing function is not declared inline. Unfortunately, 119cancellation requires many operations per sample, so the call overhead is only 120a minor burden. 121*/ 122 123#include "fir.h" 124#include "oslec.h" 125 126/* 127 G.168 echo canceller descriptor. This defines the working state for a line 128 echo canceller. 129*/ 130struct oslec_state { 131 int16_t tx, rx; 132 int16_t clean; 133 int16_t clean_nlp; 134 135 int nonupdate_dwell; 136 int curr_pos; 137 int taps; 138 int log2taps; 139 int adaption_mode; 140 141 int cond_met; 142 int32_t Pstates; 143 int16_t adapt; 144 int32_t factor; 145 int16_t shift; 146 147 /* Average levels and averaging filter states */ 148 int Ltxacc, Lrxacc, Lcleanacc, Lclean_bgacc; 149 int Ltx, Lrx; 150 int Lclean; 151 int Lclean_bg; 152 int Lbgn, Lbgn_acc, Lbgn_upper, Lbgn_upper_acc; 153 154 /* foreground and background filter states */ 155 struct fir16_state_t fir_state; 156 struct fir16_state_t fir_state_bg; 157 int16_t *fir_taps16[2]; 158 159 /* DC blocking filter states */ 160 int tx_1, tx_2, rx_1, rx_2; 161 162 /* optional High Pass Filter states */ 163 int32_t xvtx[5], yvtx[5]; 164 int32_t xvrx[5], yvrx[5]; 165 166 /* Parameters for the optional Hoth noise generator */ 167 int cng_level; 168 int cng_rndnum; 169 int cng_filter; 170 171 /* snapshot sample of coeffs used for development */ 172 int16_t *snapshot; 173}; 174 175#endif /* __ECHO_H */ 176