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