/* SPDX-License-Identifier: GPL-2.0-only *//* * SpanDSP - a series of DSP components for telephony * * echo.c - A line echo canceller.  This code is being developed *          against and partially complies with G168. * * Written by Steve Underwood <steveu@coppice.org> *         and David Rowe <david_at_rowetel_dot_com> * * Copyright (C) 2001 Steve Underwood and 2007 David Rowe * * All rights reserved. */ #ifndef __ECHO_H#define __ECHO_H /*Line echo cancellation for voice What does it do? This module aims to provide G.168-2002 compliant echo cancellation, to removeelectrical echoes (e.g. from 2-4 wire hybrids) from voice calls. How does it work? The heart of the echo cancellor is FIR filter. This is adapted to match theecho impulse response of the telephone line. It must be long enough toadequately cover the duration of that impulse response. The signal transmittedto the telephone line is passed through the FIR filter. Once the FIR isproperly adapted, the resulting output is an estimate of the echo signalreceived from the line. This is subtracted from the received signal. The resultis an estimate of the signal which originated at the far end of the line, freefrom echos of our own transmitted signal. The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, andwas introduced in 1960. It is the commonest form of filter adaption used inthings like modem line equalisers and line echo cancellers. There it works verywell.  However, it only works well for signals of constant amplitude. It worksvery poorly for things like speech echo cancellation, where the signal levelvaries widely.  This is quite easy to fix. If the signal level is normalised -similar to applying AGC - LMS can work as well for a signal of varyingamplitude as it does for a modem signal. This normalised least mean squares(NLMS) algorithm is the commonest one used for speech echo cancellation. Manyother algorithms exist - e.g. RLS (essentially the same as Kalman filtering),FAP, etc. Some perform significantly better than NLMS.  However, factors suchas computational complexity and patents favour the use of NLMS. A simple refinement to NLMS can improve its performance with speech. NLMS tendsto adapt best to the strongest parts of a signal. If the signal is white noise,the NLMS algorithm works very well. However, speech has more low frequency thanhigh frequency content. Pre-whitening (i.e. filtering the signal to flatten itsspectrum) the echo signal improves the adapt rate for speech, and ensures thefinal residual signal is not heavily biased towards high frequencies. A verylow complexity filter is adequate for this, so pre-whitening adds little to thecompute requirements of the echo canceller. An FIR filter adapted using pre-whitened NLMS performs well, provided certainconditions are met:     - The transmitted signal has poor self-correlation.    - There is no signal being generated within the environment being      cancelled. The difficulty is that neither of these can be guaranteed. If the adaption is performed while transmitting noise (or something fairlynoise like, such as voice) the adaption works very well. If the adaption isperformed while transmitting something highly correlative (typically narrowband energy such as signalling tones or DTMF), the adaption can go seriouslywrong. The reason is there is only one solution for the adaption on a nearrandom signal - the impulse response of the line. For a repetitive signal,there are any number of solutions which converge the adaption, and nothingguides the adaption to choose the generalised one. Allowing an untrainedcanceller to converge on this kind of narrowband energy probably a good thing,since at least it cancels the tones. Allowing a well converged canceller tocontinue converging on such energy is just a way to ruin its generalisedadaption. A narrowband detector is needed, so adapation can be suspended atappropriate times. The adaption process is based on trying to eliminate the received signal. Whenthere is any signal from within the environment being cancelled it may upsetthe adaption process. Similarly, if the signal we are transmitting is small,noise may dominate and disturb the adaption process. If we can ensure that theadaption is only performed when we are transmitting a significant signal level,and the environment is not, things will be OK. Clearly, it is easy to tell whenwe are sending a significant signal. Telling, if the environment is generatinga significant signal, and doing it with sufficient speed that the adaption willnot have diverged too much more we stop it, is a little harder. The key problem in detecting when the environment is sourcing significantenergy is that we must do this very quickly. Given a reasonably long sample ofthe received signal, there are a number of strategies which may be used toassess whether that signal contains a strong far end component. However, by thetime that assessment is complete the far end signal will have already causedmajor mis-convergence in the adaption process. An assessment algorithm isneeded which produces a fairly accurate result from a very short burst of farend energy. How do I use it? The echo cancellor processes both the transmit and receive streams sample bysample. The processing function is not declared inline. Unfortunately,cancellation requires many operations per sample, so the call overhead is onlya minor burden.*/ #include "fir.h"#include "oslec.h" /*    G.168 echo canceller descriptor. This defines the working state for a line    echo canceller.*/struct oslec_state {	int16_t tx;	int16_t rx;	int16_t clean;	int16_t clean_nlp; 	int nonupdate_dwell;	int curr_pos;	int taps;	int log2taps;	int adaption_mode; 	int cond_met;	int32_t pstates;	int16_t adapt;	int32_t factor;	int16_t shift; 	/* Average levels and averaging filter states */	int ltxacc;	int lrxacc;	int lcleanacc;	int lclean_bgacc;	int ltx;	int lrx;	int lclean;	int lclean_bg;	int lbgn;	int lbgn_acc;	int lbgn_upper;	int lbgn_upper_acc; 	/* foreground and background filter states */	struct fir16_state_t fir_state;	struct fir16_state_t fir_state_bg;	int16_t *fir_taps16[2]; 	/* DC blocking filter states */	int tx_1;	int tx_2;	int rx_1;	int rx_2; 	/* optional High Pass Filter states */	int32_t xvtx[5];	int32_t yvtx[5];	int32_t xvrx[5];	int32_t yvrx[5]; 	/* Parameters for the optional Hoth noise generator */	int cng_level;	int cng_rndnum;	int cng_filter; 	/* snapshot sample of coeffs used for development */	int16_t *snapshot;}; #endif /* __ECHO_H */