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Diffstat (limited to 'drivers/misc/echo/echo.h')
| -rw-r--r-- | drivers/misc/echo/echo.h | 187 |
1 files changed, 187 insertions, 0 deletions
diff --git a/drivers/misc/echo/echo.h b/drivers/misc/echo/echo.h new file mode 100644 index 00000000000..9b08c63e636 --- /dev/null +++ b/drivers/misc/echo/echo.h @@ -0,0 +1,187 @@ +/* + * 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. + * + * This program is free software; you can redistribute it and/or modify + * it under the terms of the GNU General Public License version 2, as + * published by the Free Software Foundation. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program; if not, write to the Free Software + * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. + */ + +#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 remove +electrical 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 the +echo impulse response of the telephone line. It must be long enough to +adequately cover the duration of that impulse response. The signal transmitted +to the telephone line is passed through the FIR filter. Once the FIR is +properly adapted, the resulting output is an estimate of the echo signal +received from the line. This is subtracted from the received signal. The result +is an estimate of the signal which originated at the far end of the line, free +from echos of our own transmitted signal. + +The least mean squares (LMS) algorithm is attributed to Widrow and Hoff, and +was introduced in 1960. It is the commonest form of filter adaption used in +things like modem line equalisers and line echo cancellers. There it works very +well. However, it only works well for signals of constant amplitude. It works +very poorly for things like speech echo cancellation, where the signal level +varies 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 varying +amplitude as it does for a modem signal. This normalised least mean squares +(NLMS) algorithm is the commonest one used for speech echo cancellation. Many +other algorithms exist - e.g. RLS (essentially the same as Kalman filtering), +FAP, etc. Some perform significantly better than NLMS. However, factors such +as computational complexity and patents favour the use of NLMS. + +A simple refinement to NLMS can improve its performance with speech. NLMS tends +to 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 than +high frequency content. Pre-whitening (i.e. filtering the signal to flatten its +spectrum) the echo signal improves the adapt rate for speech, and ensures the +final residual signal is not heavily biased towards high frequencies. A very +low complexity filter is adequate for this, so pre-whitening adds little to the +compute requirements of the echo canceller. + +An FIR filter adapted using pre-whitened NLMS performs well, provided certain +conditions 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 fairly +noise like, such as voice) the adaption works very well. If the adaption is +performed while transmitting something highly correlative (typically narrow +band energy such as signalling tones or DTMF), the adaption can go seriously +wrong. The reason is there is only one solution for the adaption on a near +random signal - the impulse response of the line. For a repetitive signal, +there are any number of solutions which converge the adaption, and nothing +guides the adaption to choose the generalised one. Allowing an untrained +canceller to converge on this kind of narrowband energy probably a good thing, +since at least it cancels the tones. Allowing a well converged canceller to +continue converging on such energy is just a way to ruin its generalised +adaption. A narrowband detector is needed, so adapation can be suspended at +appropriate times. + +The adaption process is based on trying to eliminate the received signal. When +there is any signal from within the environment being cancelled it may upset +the adaption process. Similarly, if the signal we are transmitting is small, +noise may dominate and disturb the adaption process. If we can ensure that the +adaption 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 when +we are sending a significant signal. Telling, if the environment is generating +a significant signal, and doing it with sufficient speed that the adaption will +not have diverged too much more we stop it, is a little harder. + +The key problem in detecting when the environment is sourcing significant +energy is that we must do this very quickly. Given a reasonably long sample of +the received signal, there are a number of strategies which may be used to +assess whether that signal contains a strong far end component. However, by the +time that assessment is complete the far end signal will have already caused +major mis-convergence in the adaption process. An assessment algorithm is +needed which produces a fairly accurate result from a very short burst of far +end energy. + +How do I use it? + +The echo cancellor processes both the transmit and receive streams sample by +sample. The processing function is not declared inline. Unfortunately, +cancellation requires many operations per sample, so the call overhead is only +a 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 */ |