drivers/staging/echo/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 /*! \page echo_can_page Line echo cancellation for voice
  32
  33 \section echo_can_page_sec_1 What does it do?
  34 This module aims to provide G.168-2002 compliant echo cancellation, to remove
  35 electrical echoes (e.g. from 2-4 wire hybrids) from voice calls.
  36
  37 \section echo_can_page_sec_2 How does it work?
  38 The heart of the echo cancellor is FIR filter. This is adapted to match the
  39 echo impulse response of the telephone line. It must be long enough to
  40 adequately cover the duration of that impulse response. The signal transmitted
  41 to the telephone line is passed through the FIR filter. Once the FIR is
  42 properly adapted, the resulting output is an estimate of the echo signal
  43 received from the line. This is subtracted from the received signal. The result
  44 is an estimate of the signal which originated at the far end of the line, free
  45 from echos of our own transmitted signal.
  46
  47 The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, and
  48 was introduced in 1960. It is the commonest form of filter adaption used in
  49 things like modem line equalisers and line echo cancellers. There it works very
  50 well.  However, it only works well for signals of constant amplitude. It works
  51 very poorly for things like speech echo cancellation, where the signal level
  52 varies widely.  This is quite easy to fix. If the signal level is normalised -
  53 similar to applying AGC - LMS can work as well for a signal of varying
  54 amplitude as it does for a modem signal. This normalised least mean squares
  55 (NLMS) algorithm is the commonest one used for speech echo cancellation. Many
  56 other algorithms exist - e.g. RLS (essentially the same as Kalman filtering),
  57 FAP, etc. Some perform significantly better than NLMS.  However, factors such
  58 as computational complexity and patents favour the use of NLMS.
  59
  60 A simple refinement to NLMS can improve its performance with speech. NLMS tends
  61 to adapt best to the strongest parts of a signal. If the signal is white noise,
  62 the NLMS algorithm works very well. However, speech has more low frequency than
  63 high frequency content. Pre-whitening (i.e. filtering the signal to flatten its
  64 spectrum) the echo signal improves the adapt rate for speech, and ensures the
  65 final residual signal is not heavily biased towards high frequencies. A very
  66 low complexity filter is adequate for this, so pre-whitening adds little to the
  67 compute requirements of the echo canceller.
  68
  69 An FIR filter adapted using pre-whitened NLMS performs well, provided certain
  70 conditions are met:
  71
  72     - The transmitted signal has poor self-correlation.
  73     - There is no signal being generated within the environment being
  74       cancelled.
  75
  76 The difficulty is that neither of these can be guaranteed.
  77
  78 If the adaption is performed while transmitting noise (or something fairly
  79 noise like, such as voice) the adaption works very well. If the adaption is
  80 performed while transmitting something highly correlative (typically narrow
  81 band energy such as signalling tones or DTMF), the adaption can go seriously
  82 wrong. The reason is there is only one solution for the adaption on a near
  83 random signal - the impulse response of the line. For a repetitive signal,
  84 there are any number of solutions which converge the adaption, and nothing
  85 guides the adaption to choose the generalised one. Allowing an untrained
  86 canceller to converge on this kind of narrowband energy probably a good thing,
  87 since at least it cancels the tones. Allowing a well converged canceller to
  88 continue converging on such energy is just a way to ruin its generalised
  89 adaption. A narrowband detector is needed, so adapation can be suspended at
  90 appropriate times.
  91
  92 The adaption process is based on trying to eliminate the received signal. When
  93 there is any signal from within the environment being cancelled it may upset
  94 the adaption process. Similarly, if the signal we are transmitting is small,
  95 noise may dominate and disturb the adaption process. If we can ensure that the
  96 adaption is only performed when we are transmitting a significant signal level,
  97 and the environment is not, things will be OK. Clearly, it is easy to tell when
  98 we are sending a significant signal. Telling, if the environment is generating
  99 a significant signal, and doing it with sufficient speed that the adaption will
 100 not have diverged too much more we stop it, is a little harder.
 101
 102 The key problem in detecting when the environment is sourcing significant
 103 energy is that we must do this very quickly. Given a reasonably long sample of
 104 the received signal, there are a number of strategies which may be used to
 105 assess whether that signal contains a strong far end component. However, by the
 106 time that assessment is complete the far end signal will have already caused
 107 major mis-convergence in the adaption process. An assessment algorithm is
 108 needed which produces a fairly accurate result from a very short burst of far
 109 end energy.
 110
 111 \section echo_can_page_sec_3 How do I use it?
 112 The echo cancellor processes both the transmit and receive streams sample by
 113 sample. The processing function is not declared inline. Unfortunately,
 114 cancellation requires many operations per sample, so the call overhead is only
 115 a minor burden.
 116 */
 117
 118 #include "fir.h"
 119 #include "oslec.h"
 120
 121 /*!
 122     G.168 echo canceller descriptor. This defines the working state for a line
 123     echo canceller.
 124 */
 125 struct oslec_state {
 126         int16_t tx, rx;
 127         int16_t clean;
 128         int16_t clean_nlp;
 129
 130         int nonupdate_dwell;
 131         int curr_pos;
 132         int taps;
 133         int log2taps;
 134         int adaption_mode;
 135
 136         int cond_met;
 137         int32_t Pstates;
 138         int16_t adapt;
 139         int32_t factor;
 140         int16_t shift;
 141
 142         /* Average levels and averaging filter states */
 143         int Ltxacc, Lrxacc, Lcleanacc, Lclean_bgacc;
 144         int Ltx, Lrx;
 145         int Lclean;
 146         int Lclean_bg;
 147         int Lbgn, Lbgn_acc, Lbgn_upper, 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, tx_2, rx_1, rx_2;
 156
 157         /* optional High Pass Filter states */
 158         int32_t xvtx[5], yvtx[5];
 159         int32_t xvrx[5], yvrx[5];
 160
 161         /* Parameters for the optional Hoth noise generator */
 162         int cng_level;
 163         int cng_rndnum;
 164         int cng_filter;
 165
 166         /* snapshot sample of coeffs used for development */
 167         int16_t *snapshot;
 168 };
 169
 170 #endif /* __ECHO_H */