webrtc/modules/audio_coding/neteq/background_noise.cc
Henrik Lundin 6719017d19 NetEq: Remove background noise fill during long expansions
NetEq was (up until this CL) capable of fading over to generating a
constant background noise when voice expansion had lasted too long.
However, the code has for a really long time only ever used the "off"
mode, which meant that long expansions are faded down to complete
silence (only zeros), i.e., background noise fill was not used.
Removing the other two modes ("on" and "fade") simplifies the code.

Bug: webrtc:9180
Change-Id: Ia2d46960208f3d75c9659ad3f027c52e5ecfb6b0
Reviewed-on: https://webrtc-review.googlesource.com/71485
Commit-Queue: Henrik Lundin <henrik.lundin@webrtc.org>
Reviewed-by: Minyue Li <minyue@webrtc.org>
Cr-Commit-Position: refs/heads/master@{#22969}
2018-04-23 06:59:46 +00:00

253 lines
10 KiB
C++

/*
* Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/audio_coding/neteq/background_noise.h"
#include <assert.h>
#include <string.h> // memcpy
#include <algorithm> // min, max
#include "common_audio/signal_processing/include/signal_processing_library.h"
#include "modules/audio_coding/neteq/audio_multi_vector.h"
#include "modules/audio_coding/neteq/cross_correlation.h"
#include "modules/audio_coding/neteq/post_decode_vad.h"
namespace webrtc {
// static
const size_t BackgroundNoise::kMaxLpcOrder;
BackgroundNoise::BackgroundNoise(size_t num_channels)
: num_channels_(num_channels),
channel_parameters_(new ChannelParameters[num_channels_]) {
Reset();
}
BackgroundNoise::~BackgroundNoise() {}
void BackgroundNoise::Reset() {
initialized_ = false;
for (size_t channel = 0; channel < num_channels_; ++channel) {
channel_parameters_[channel].Reset();
}
}
void BackgroundNoise::Update(const AudioMultiVector& input,
const PostDecodeVad& vad) {
if (vad.running() && vad.active_speech()) {
// Do not update the background noise parameters if we know that the signal
// is active speech.
return;
}
int32_t auto_correlation[kMaxLpcOrder + 1];
int16_t fiter_output[kMaxLpcOrder + kResidualLength];
int16_t reflection_coefficients[kMaxLpcOrder];
int16_t lpc_coefficients[kMaxLpcOrder + 1];
for (size_t channel_ix = 0; channel_ix < num_channels_; ++channel_ix) {
ChannelParameters& parameters = channel_parameters_[channel_ix];
int16_t temp_signal_array[kVecLen + kMaxLpcOrder] = {0};
int16_t* temp_signal = &temp_signal_array[kMaxLpcOrder];
input[channel_ix].CopyTo(kVecLen, input.Size() - kVecLen, temp_signal);
int32_t sample_energy = CalculateAutoCorrelation(temp_signal, kVecLen,
auto_correlation);
if ((!vad.running() &&
sample_energy < parameters.energy_update_threshold) ||
(vad.running() && !vad.active_speech())) {
// Generate LPC coefficients.
if (auto_correlation[0] > 0) {
// Regardless of whether the filter is actually updated or not,
// update energy threshold levels, since we have in fact observed
// a low energy signal.
if (sample_energy < parameters.energy_update_threshold) {
// Never go under 1.0 in average sample energy.
parameters.energy_update_threshold = std::max(sample_energy, 1);
parameters.low_energy_update_threshold = 0;
}
// Only update BGN if filter is stable, i.e., if return value from
// Levinson-Durbin function is 1.
if (WebRtcSpl_LevinsonDurbin(auto_correlation, lpc_coefficients,
reflection_coefficients,
kMaxLpcOrder) != 1) {
return;
}
} else {
// Center value in auto-correlation is not positive. Do not update.
return;
}
// Generate the CNG gain factor by looking at the energy of the residual.
WebRtcSpl_FilterMAFastQ12(temp_signal + kVecLen - kResidualLength,
fiter_output, lpc_coefficients,
kMaxLpcOrder + 1, kResidualLength);
int32_t residual_energy = WebRtcSpl_DotProductWithScale(fiter_output,
fiter_output,
kResidualLength,
0);
// Check spectral flatness.
// Comparing the residual variance with the input signal variance tells
// if the spectrum is flat or not.
// If 5 * residual_energy >= 16 * sample_energy, the spectrum is flat
// enough. Also ensure that the energy is non-zero.
if ((sample_energy > 0) &&
(int64_t{5} * residual_energy >= int64_t{16} * sample_energy)) {
// Spectrum is flat enough; save filter parameters.
// |temp_signal| + |kVecLen| - |kMaxLpcOrder| points at the first of the
// |kMaxLpcOrder| samples in the residual signal, which will form the
// filter state for the next noise generation.
SaveParameters(channel_ix, lpc_coefficients,
temp_signal + kVecLen - kMaxLpcOrder, sample_energy,
residual_energy);
}
} else {
// Will only happen if post-decode VAD is disabled and |sample_energy| is
// not low enough. Increase the threshold for update so that it increases
// by a factor 4 in 4 seconds.
IncrementEnergyThreshold(channel_ix, sample_energy);
}
}
return;
}
int32_t BackgroundNoise::Energy(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].energy;
}
void BackgroundNoise::SetMuteFactor(size_t channel, int16_t value) {
assert(channel < num_channels_);
channel_parameters_[channel].mute_factor = value;
}
int16_t BackgroundNoise::MuteFactor(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].mute_factor;
}
const int16_t* BackgroundNoise::Filter(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].filter;
}
const int16_t* BackgroundNoise::FilterState(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].filter_state;
}
void BackgroundNoise::SetFilterState(size_t channel, const int16_t* input,
size_t length) {
assert(channel < num_channels_);
length = std::min(length, kMaxLpcOrder);
memcpy(channel_parameters_[channel].filter_state, input,
length * sizeof(int16_t));
}
int16_t BackgroundNoise::Scale(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].scale;
}
int16_t BackgroundNoise::ScaleShift(size_t channel) const {
assert(channel < num_channels_);
return channel_parameters_[channel].scale_shift;
}
int32_t BackgroundNoise::CalculateAutoCorrelation(
const int16_t* signal, size_t length, int32_t* auto_correlation) const {
static const int kCorrelationStep = -1;
const int correlation_scale =
CrossCorrelationWithAutoShift(signal, signal, length, kMaxLpcOrder + 1,
kCorrelationStep, auto_correlation);
// Number of shifts to normalize energy to energy/sample.
int energy_sample_shift = kLogVecLen - correlation_scale;
return auto_correlation[0] >> energy_sample_shift;
}
void BackgroundNoise::IncrementEnergyThreshold(size_t channel,
int32_t sample_energy) {
// TODO(hlundin): Simplify the below threshold update. What this code
// does is simply "threshold += (increment * threshold) >> 16", but due
// to the limited-width operations, it is not exactly the same. The
// difference should be inaudible, but bit-exactness would not be
// maintained.
assert(channel < num_channels_);
ChannelParameters& parameters = channel_parameters_[channel];
int32_t temp_energy =
(kThresholdIncrement * parameters.low_energy_update_threshold) >> 16;
temp_energy += kThresholdIncrement *
(parameters.energy_update_threshold & 0xFF);
temp_energy += (kThresholdIncrement *
((parameters.energy_update_threshold>>8) & 0xFF)) << 8;
parameters.low_energy_update_threshold += temp_energy;
parameters.energy_update_threshold += kThresholdIncrement *
(parameters.energy_update_threshold>>16);
parameters.energy_update_threshold +=
parameters.low_energy_update_threshold >> 16;
parameters.low_energy_update_threshold =
parameters.low_energy_update_threshold & 0x0FFFF;
// Update maximum energy.
// Decrease by a factor 1/1024 each time.
parameters.max_energy = parameters.max_energy -
(parameters.max_energy >> 10);
if (sample_energy > parameters.max_energy) {
parameters.max_energy = sample_energy;
}
// Set |energy_update_threshold| to no less than 60 dB lower than
// |max_energy_|. Adding 524288 assures proper rounding.
int32_t energy_update_threshold = (parameters.max_energy + 524288) >> 20;
if (energy_update_threshold > parameters.energy_update_threshold) {
parameters.energy_update_threshold = energy_update_threshold;
}
}
void BackgroundNoise::SaveParameters(size_t channel,
const int16_t* lpc_coefficients,
const int16_t* filter_state,
int32_t sample_energy,
int32_t residual_energy) {
assert(channel < num_channels_);
ChannelParameters& parameters = channel_parameters_[channel];
memcpy(parameters.filter, lpc_coefficients,
(kMaxLpcOrder+1) * sizeof(int16_t));
memcpy(parameters.filter_state, filter_state,
kMaxLpcOrder * sizeof(int16_t));
// Save energy level and update energy threshold levels.
// Never get under 1.0 in average sample energy.
parameters.energy = std::max(sample_energy, 1);
parameters.energy_update_threshold = parameters.energy;
parameters.low_energy_update_threshold = 0;
// Normalize residual_energy to 29 or 30 bits before sqrt.
int16_t norm_shift = WebRtcSpl_NormW32(residual_energy) - 1;
if (norm_shift & 0x1) {
norm_shift -= 1; // Even number of shifts required.
}
residual_energy = WEBRTC_SPL_SHIFT_W32(residual_energy, norm_shift);
// Calculate scale and shift factor.
parameters.scale = static_cast<int16_t>(WebRtcSpl_SqrtFloor(residual_energy));
// Add 13 to the |scale_shift_|, since the random numbers table is in
// Q13.
// TODO(hlundin): Move the "13" to where the |scale_shift_| is used?
parameters.scale_shift =
static_cast<int16_t>(13 + ((kLogResidualLength + norm_shift) / 2));
initialized_ = true;
}
} // namespace webrtc