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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
39How does it work?
40
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.
49
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.
62
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.
71
72An FIR filter adapted using pre-whitened NLMS performs well, provided certain
73conditions are met:
74
75 - The transmitted signal has poor self-correlation.
76 - There is no signal being generated within the environment being
77 cancelled.
78
79The difficulty is that neither of these can be guaranteed.
80
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.
94
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.
104
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.
113
114How do I use it?
115
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.
120*/
121
122#include "fir.h"
123#include "oslec.h"
124
125/*
126 G.168 echo canceller descriptor. This defines the working state for a line
127 echo canceller.
128*/
129struct oslec_state {
130 int16_t tx;
131 int16_t 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;
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;
160
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];
165
166 /* DC blocking filter states */
167 int tx_1;
168 int tx_2;
169 int rx_1;
170 int rx_2;
171
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];
177
178 /* Parameters for the optional Hoth noise generator */
179 int cng_level;
180 int cng_rndnum;
181 int cng_filter;
182
183 /* snapshot sample of coeffs used for development */
184 int16_t *snapshot;
185};
186
187#endif /* __ECHO_H */
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
39How does it work?
40
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.
49
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.
62
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.
71
72An FIR filter adapted using pre-whitened NLMS performs well, provided certain
73conditions are met:
74
75 - The transmitted signal has poor self-correlation.
76 - There is no signal being generated within the environment being
77 cancelled.
78
79The difficulty is that neither of these can be guaranteed.
80
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.
94
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.
104
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.
113
114How do I use it?
115
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.
120*/
121
122#include "fir.h"
123#include "oslec.h"
124
125/*
126 G.168 echo canceller descriptor. This defines the working state for a line
127 echo canceller.
128*/
129struct oslec_state {
130 int16_t tx;
131 int16_t 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;
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;
160
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];
165
166 /* DC blocking filter states */
167 int tx_1;
168 int tx_2;
169 int rx_1;
170 int rx_2;
171
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];
177
178 /* Parameters for the optional Hoth noise generator */
179 int cng_level;
180 int cng_rndnum;
181 int cng_filter;
182
183 /* snapshot sample of coeffs used for development */
184 int16_t *snapshot;
185};
186
187#endif /* __ECHO_H */