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 <>
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
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 */
28#ifndef __ECHO_H
29#define __ECHO_H
32Line echo cancellation for voice
34What does it do?
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.
39How does it work?
41The heart of the echo cancellor is FIR filter. This is adapted to match the
42echo impulse response of the telephone line. It must be long enough to
43adequately cover the duration of that impulse response. The signal transmitted
44to the telephone line is passed through the FIR filter. Once the FIR is
45properly adapted, the resulting output is an estimate of the echo signal
46received from the line. This is subtracted from the received signal. The result
47is an estimate of the signal which originated at the far end of the line, free
48from echos of our own transmitted signal.
50The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, and
51was introduced in 1960. It is the commonest form of filter adaption used in
52things like modem line equalisers and line echo cancellers. There it works very
53well. However, it only works well for signals of constant amplitude. It works
54very poorly for things like speech echo cancellation, where the signal level
55varies widely. This is quite easy to fix. If the signal level is normalised -
56similar to applying AGC - LMS can work as well for a signal of varying
57amplitude as it does for a modem signal. This normalised least mean squares
58(NLMS) algorithm is the commonest one used for speech echo cancellation. Many
59other algorithms exist - e.g. RLS (essentially the same as Kalman filtering),
60FAP, etc. Some perform significantly better than NLMS. However, factors such
61as computational complexity and patents favour the use of NLMS.
63A simple refinement to NLMS can improve its performance with speech. NLMS tends
64to adapt best to the strongest parts of a signal. If the signal is white noise,
65the NLMS algorithm works very well. However, speech has more low frequency than
66high frequency content. Pre-whitening (i.e. filtering the signal to flatten its
67spectrum) the echo signal improves the adapt rate for speech, and ensures the
68final residual signal is not heavily biased towards high frequencies. A very
69low complexity filter is adequate for this, so pre-whitening adds little to the
70compute requirements of the echo canceller.
72An FIR filter adapted using pre-whitened NLMS performs well, provided certain
73conditions are met:
75    - The transmitted signal has poor self-correlation.
76    - There is no signal being generated within the environment being
77      cancelled.
79The difficulty is that neither of these can be guaranteed.
81If the adaption is performed while transmitting noise (or something fairly
82noise like, such as voice) the adaption works very well. If the adaption is
83performed while transmitting something highly correlative (typically narrow
84band energy such as signalling tones or DTMF), the adaption can go seriously
85wrong. The reason is there is only one solution for the adaption on a near
86random signal - the impulse response of the line. For a repetitive signal,
87there are any number of solutions which converge the adaption, and nothing
88guides the adaption to choose the generalised one. Allowing an untrained
89canceller to converge on this kind of narrowband energy probably a good thing,
90since at least it cancels the tones. Allowing a well converged canceller to
91continue converging on such energy is just a way to ruin its generalised
92adaption. A narrowband detector is needed, so adapation can be suspended at
93appropriate times.
95The adaption process is based on trying to eliminate the received signal. When
96there is any signal from within the environment being cancelled it may upset
97the adaption process. Similarly, if the signal we are transmitting is small,
98noise may dominate and disturb the adaption process. If we can ensure that the
99adaption is only performed when we are transmitting a significant signal level,
100and the environment is not, things will be OK. Clearly, it is easy to tell when
101we are sending a significant signal. Telling, if the environment is generating
102a significant signal, and doing it with sufficient speed that the adaption will
103not have diverged too much more we stop it, is a little harder.
105The key problem in detecting when the environment is sourcing significant
106energy is that we must do this very quickly. Given a reasonably long sample of
107the received signal, there are a number of strategies which may be used to
108assess whether that signal contains a strong far end component. However, by the
109time that assessment is complete the far end signal will have already caused
110major mis-convergence in the adaption process. An assessment algorithm is
111needed which produces a fairly accurate result from a very short burst of far
112end energy.
114How do I use it?
116The echo cancellor processes both the transmit and receive streams sample by
117sample. The processing function is not declared inline. Unfortunately,
118cancellation requires many operations per sample, so the call overhead is only
119a minor burden.
122#include "fir.h"
123#include "oslec.h"
126    G.168 echo canceller descriptor. This defines the working state for a line
127    echo canceller.
129struct oslec_state {
130    int16_t tx;
131    int16_t rx;
132    int16_t clean;
133    int16_t clean_nlp;
135    int nonupdate_dwell;
136    int curr_pos;
137    int taps;
138    int log2taps;
139    int adaption_mode;
141    int cond_met;
142    int32_t Pstates;
143    int16_t adapt;
144    int32_t factor;
145    int16_t shift;
147    /* Average levels and averaging filter states */
148    int Ltxacc;
149    int Lrxacc;
150    int Lcleanacc;
151    int Lclean_bgacc;
152    int Ltx;
153    int Lrx;
154    int Lclean;
155    int Lclean_bg;
156    int Lbgn;
157    int Lbgn_acc;
158    int Lbgn_upper;
159    int Lbgn_upper_acc;
161    /* foreground and background filter states */
162    struct fir16_state_t fir_state;
163    struct fir16_state_t fir_state_bg;
164    int16_t *fir_taps16[2];
166    /* DC blocking filter states */
167    int tx_1;
168    int tx_2;
169    int rx_1;
170    int rx_2;
172    /* optional High Pass Filter states */
173    int32_t xvtx[5];
174    int32_t yvtx[5];
175    int32_t xvrx[5];
176    int32_t yvrx[5];
178    /* Parameters for the optional Hoth noise generator */
179    int cng_level;
180    int cng_rndnum;
181    int cng_filter;
183    /* snapshot sample of coeffs used for development */
184    int16_t *snapshot;
187#endif /* __ECHO_H */

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