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Diffstat (limited to 'drivers/staging/echo/echo.h')
| -rw-r--r-- | drivers/staging/echo/echo.h | 175 |
1 files changed, 0 insertions, 175 deletions
diff --git a/drivers/staging/echo/echo.h b/drivers/staging/echo/echo.h deleted file mode 100644 index 754e66d30c1..00000000000 --- a/drivers/staging/echo/echo.h +++ /dev/null @@ -1,175 +0,0 @@ -/* - * 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, 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, Lrxacc, Lcleanacc, Lclean_bgacc; - int Ltx, Lrx; - int Lclean; - int Lclean_bg; - int Lbgn, Lbgn_acc, Lbgn_upper, 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, tx_2, rx_1, rx_2; - - /* optional High Pass Filter states */ - int32_t xvtx[5], yvtx[5]; - int32_t xvrx[5], 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 */ |