Adding upstream version 4.19.249.upstream/4.19.249

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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 000000000..9b08c63e6
--- /dev/null
+++ b/drivers/misc/echo/echo.h
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+/*
+ * 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 */