1/* SPDX-License-Identifier: GPL-2.0-only */ 2/* 3 * SpanDSP - a series of DSP components for telephony 4 * 5 * echo.c - A line echo canceller. This code is being developed 6 * against and partially complies with G168. 7 * 8 * Written by Steve Underwood <steveu@coppice.org> 9 * and David Rowe <david_at_rowetel_dot_com> 10 * 11 * Copyright (C) 2001 Steve Underwood and 2007 David Rowe 12 * 13 * All rights reserved. 14 */ 15 16#ifndef __ECHO_H 17#define __ECHO_H 18 19/* 20Line echo cancellation for voice 21 22What does it do? 23 24This module aims to provide G.168-2002 compliant echo cancellation, to remove 25electrical echoes (e.g. from 2-4 wire hybrids) from voice calls. 26 27How does it work? 28 29The heart of the echo cancellor is FIR filter. This is adapted to match the 30echo impulse response of the telephone line. It must be long enough to 31adequately cover the duration of that impulse response. The signal transmitted 32to the telephone line is passed through the FIR filter. Once the FIR is 33properly adapted, the resulting output is an estimate of the echo signal 34received from the line. This is subtracted from the received signal. The result 35is an estimate of the signal which originated at the far end of the line, free 36from echos of our own transmitted signal. 37 38The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, and 39was introduced in 1960. It is the commonest form of filter adaption used in 40things like modem line equalisers and line echo cancellers. There it works very 41well. However, it only works well for signals of constant amplitude. It works 42very poorly for things like speech echo cancellation, where the signal level 43varies widely. This is quite easy to fix. If the signal level is normalised - 44similar to applying AGC - LMS can work as well for a signal of varying 45amplitude as it does for a modem signal. This normalised least mean squares 46(NLMS) algorithm is the commonest one used for speech echo cancellation. Many 47other algorithms exist - e.g. RLS (essentially the same as Kalman filtering), 48FAP, etc. Some perform significantly better than NLMS. However, factors such 49as computational complexity and patents favour the use of NLMS. 50 51A simple refinement to NLMS can improve its performance with speech. NLMS tends 52to adapt best to the strongest parts of a signal. If the signal is white noise, 53the NLMS algorithm works very well. However, speech has more low frequency than 54high frequency content. Pre-whitening (i.e. filtering the signal to flatten its 55spectrum) the echo signal improves the adapt rate for speech, and ensures the 56final residual signal is not heavily biased towards high frequencies. A very 57low complexity filter is adequate for this, so pre-whitening adds little to the 58compute requirements of the echo canceller. 59 60An FIR filter adapted using pre-whitened NLMS performs well, provided certain 61conditions are met: 62 63 - The transmitted signal has poor self-correlation. 64 - There is no signal being generated within the environment being 65 cancelled. 66 67The difficulty is that neither of these can be guaranteed. 68 69If the adaption is performed while transmitting noise (or something fairly 70noise like, such as voice) the adaption works very well. If the adaption is 71performed while transmitting something highly correlative (typically narrow 72band energy such as signalling tones or DTMF), the adaption can go seriously 73wrong. The reason is there is only one solution for the adaption on a near 74random signal - the impulse response of the line. For a repetitive signal, 75there are any number of solutions which converge the adaption, and nothing 76guides the adaption to choose the generalised one. Allowing an untrained 77canceller to converge on this kind of narrowband energy probably a good thing, 78since at least it cancels the tones. Allowing a well converged canceller to 79continue converging on such energy is just a way to ruin its generalised 80adaption. A narrowband detector is needed, so adapation can be suspended at 81appropriate times. 82 83The adaption process is based on trying to eliminate the received signal. When 84there is any signal from within the environment being cancelled it may upset 85the adaption process. Similarly, if the signal we are transmitting is small, 86noise may dominate and disturb the adaption process. If we can ensure that the 87adaption is only performed when we are transmitting a significant signal level, 88and the environment is not, things will be OK. Clearly, it is easy to tell when 89we are sending a significant signal. Telling, if the environment is generating 90a significant signal, and doing it with sufficient speed that the adaption will 91not have diverged too much more we stop it, is a little harder. 92 93The key problem in detecting when the environment is sourcing significant 94energy is that we must do this very quickly. Given a reasonably long sample of 95the received signal, there are a number of strategies which may be used to 96assess whether that signal contains a strong far end component. However, by the 97time that assessment is complete the far end signal will have already caused 98major mis-convergence in the adaption process. An assessment algorithm is 99needed which produces a fairly accurate result from a very short burst of far 100end energy. 101 102How do I use it? 103 104The echo cancellor processes both the transmit and receive streams sample by 105sample. The processing function is not declared inline. Unfortunately, 106cancellation requires many operations per sample, so the call overhead is only 107a minor burden. 108*/ 109 110#include "fir.h" 111#include "oslec.h" 112 113/* 114 G.168 echo canceller descriptor. This defines the working state for a line 115 echo canceller. 116*/ 117struct oslec_state { 118 int16_t tx; 119 int16_t rx; 120 int16_t clean; 121 int16_t clean_nlp; 122 123 int nonupdate_dwell; 124 int curr_pos; 125 int taps; 126 int log2taps; 127 int adaption_mode; 128 129 int cond_met; 130 int32_t pstates; 131 int16_t adapt; 132 int32_t factor; 133 int16_t shift; 134 135 /* Average levels and averaging filter states */ 136 int ltxacc; 137 int lrxacc; 138 int lcleanacc; 139 int lclean_bgacc; 140 int ltx; 141 int lrx; 142 int lclean; 143 int lclean_bg; 144 int lbgn; 145 int lbgn_acc; 146 int lbgn_upper; 147 int lbgn_upper_acc; 148 149 /* foreground and background filter states */ 150 struct fir16_state_t fir_state; 151 struct fir16_state_t fir_state_bg; 152 int16_t *fir_taps16[2]; 153 154 /* DC blocking filter states */ 155 int tx_1; 156 int tx_2; 157 int rx_1; 158 int rx_2; 159 160 /* optional High Pass Filter states */ 161 int32_t xvtx[5]; 162 int32_t yvtx[5]; 163 int32_t xvrx[5]; 164 int32_t 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