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webrtc/modules/audio_processing/aec/aec_core.cc
Sam Zackrisson 37242cbc41 Remove logging of metrics no longer relevant for AEC2 usage (UMA). 
The acoustic echo canceler AEC2 is being deprecated. The routing for reporting these metrics as UMA stats has outlived the metrics'usefulness.

Bug: webrtc:10563
Change-Id: Ib96693dfc43e25a0cfecad7d5d2043116ca7e6b5
Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/133573
Reviewed-by: Per Åhgren <peah@webrtc.org>
Commit-Queue: Sam Zackrisson <saza@webrtc.org>
Cr-Commit-Position: refs/heads/master@{#27699}
2019-04-23 09:14:15 +00:00

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/*
* 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.
*/
/*
* The core AEC algorithm, which is presented with time-aligned signals.
*/
#include "modules/audio_processing/aec/aec_core.h"
#include <math.h>
#include <stddef.h> // size_t
#include <stdlib.h>
#include <string.h>
#include <algorithm>
#include <cmath>
#include "rtc_base/checks.h"
extern "C" {
#include "common_audio/ring_buffer.h"
}
#include "common_audio/signal_processing/include/signal_processing_library.h"
#include "modules/audio_processing/aec/aec_common.h"
#include "modules/audio_processing/aec/aec_core_optimized_methods.h"
#include "modules/audio_processing/logging/apm_data_dumper.h"
#include "modules/audio_processing/utility/delay_estimator_wrapper.h"
#include "rtc_base/system/arch.h"
#include "system_wrappers/include/cpu_features_wrapper.h"
#include "system_wrappers/include/metrics.h"
namespace webrtc {
// Buffer size (samples)
static const size_t kBufferSizeBlocks = 250; // 1 second of audio in 16 kHz.
// Metrics
static const size_t kSubCountLen = 4;
static const size_t kCountLen = 50;
static const int kDelayMetricsAggregationWindow = 1250; // 5 seconds at 16 kHz.
// Divergence metric is based on audio level, which gets updated every
// |kSubCountLen + 1| * PART_LEN samples. Divergence metric takes the statistics
// of |kDivergentFilterFractionAggregationWindowSize| audio levels. The
// following value corresponds to 1 second at 16 kHz.
static const int kDivergentFilterFractionAggregationWindowSize = 50;
// Quantities to control H band scaling for SWB input
static const float cnScaleHband = 0.4f; // scale for comfort noise in H band.
// Initial bin for averaging nlp gain in low band
static const int freqAvgIc = PART_LEN / 2;
// Matlab code to produce table:
// win = sqrt(hanning(63)); win = [0 ; win(1:32)];
// fprintf(1, '\t%.14f, %.14f, %.14f,\n', win);
ALIGN16_BEG const float ALIGN16_END WebRtcAec_sqrtHanning[65] = {
0.00000000000000f, 0.02454122852291f, 0.04906767432742f, 0.07356456359967f,
0.09801714032956f, 0.12241067519922f, 0.14673047445536f, 0.17096188876030f,
0.19509032201613f, 0.21910124015687f, 0.24298017990326f, 0.26671275747490f,
0.29028467725446f, 0.31368174039889f, 0.33688985339222f, 0.35989503653499f,
0.38268343236509f, 0.40524131400499f, 0.42755509343028f, 0.44961132965461f,
0.47139673682600f, 0.49289819222978f, 0.51410274419322f, 0.53499761988710f,
0.55557023301960f, 0.57580819141785f, 0.59569930449243f, 0.61523159058063f,
0.63439328416365f, 0.65317284295378f, 0.67155895484702f, 0.68954054473707f,
0.70710678118655f, 0.72424708295147f, 0.74095112535496f, 0.75720884650648f,
0.77301045336274f, 0.78834642762661f, 0.80320753148064f, 0.81758481315158f,
0.83146961230255f, 0.84485356524971f, 0.85772861000027f, 0.87008699110871f,
0.88192126434835f, 0.89322430119552f, 0.90398929312344f, 0.91420975570353f,
0.92387953251129f, 0.93299279883474f, 0.94154406518302f, 0.94952818059304f,
0.95694033573221f, 0.96377606579544f, 0.97003125319454f, 0.97570213003853f,
0.98078528040323f, 0.98527764238894f, 0.98917650996478f, 0.99247953459871f,
0.99518472667220f, 0.99729045667869f, 0.99879545620517f, 0.99969881869620f,
1.00000000000000f};
// Matlab code to produce table:
// weightCurve = [0 ; 0.3 * sqrt(linspace(0,1,64))' + 0.1];
// fprintf(1, '\t%.4f, %.4f, %.4f, %.4f, %.4f, %.4f,\n', weightCurve);
ALIGN16_BEG const float ALIGN16_END WebRtcAec_weightCurve[65] = {
0.0000f, 0.1000f, 0.1378f, 0.1535f, 0.1655f, 0.1756f, 0.1845f, 0.1926f,
0.2000f, 0.2069f, 0.2134f, 0.2195f, 0.2254f, 0.2309f, 0.2363f, 0.2414f,
0.2464f, 0.2512f, 0.2558f, 0.2604f, 0.2648f, 0.2690f, 0.2732f, 0.2773f,
0.2813f, 0.2852f, 0.2890f, 0.2927f, 0.2964f, 0.3000f, 0.3035f, 0.3070f,
0.3104f, 0.3138f, 0.3171f, 0.3204f, 0.3236f, 0.3268f, 0.3299f, 0.3330f,
0.3360f, 0.3390f, 0.3420f, 0.3449f, 0.3478f, 0.3507f, 0.3535f, 0.3563f,
0.3591f, 0.3619f, 0.3646f, 0.3673f, 0.3699f, 0.3726f, 0.3752f, 0.3777f,
0.3803f, 0.3828f, 0.3854f, 0.3878f, 0.3903f, 0.3928f, 0.3952f, 0.3976f,
0.4000f};
// Matlab code to produce table:
// overDriveCurve = [sqrt(linspace(0,1,65))' + 1];
// fprintf(1, '\t%.4f, %.4f, %.4f, %.4f, %.4f, %.4f,\n', overDriveCurve);
ALIGN16_BEG const float ALIGN16_END WebRtcAec_overDriveCurve[65] = {
1.0000f, 1.1250f, 1.1768f, 1.2165f, 1.2500f, 1.2795f, 1.3062f, 1.3307f,
1.3536f, 1.3750f, 1.3953f, 1.4146f, 1.4330f, 1.4507f, 1.4677f, 1.4841f,
1.5000f, 1.5154f, 1.5303f, 1.5449f, 1.5590f, 1.5728f, 1.5863f, 1.5995f,
1.6124f, 1.6250f, 1.6374f, 1.6495f, 1.6614f, 1.6731f, 1.6847f, 1.6960f,
1.7071f, 1.7181f, 1.7289f, 1.7395f, 1.7500f, 1.7603f, 1.7706f, 1.7806f,
1.7906f, 1.8004f, 1.8101f, 1.8197f, 1.8292f, 1.8385f, 1.8478f, 1.8570f,
1.8660f, 1.8750f, 1.8839f, 1.8927f, 1.9014f, 1.9100f, 1.9186f, 1.9270f,
1.9354f, 1.9437f, 1.9520f, 1.9601f, 1.9682f, 1.9763f, 1.9843f, 1.9922f,
2.0000f};
// Delay Agnostic AEC parameters, still under development and may change.
static const float kDelayQualityThresholdMax = 0.07f;
static const float kDelayQualityThresholdMin = 0.01f;
static const int kInitialShiftOffset = 5;
#if !defined(WEBRTC_ANDROID)
static const int kDelayCorrectionStart = 1500; // 10 ms chunks
#endif
// Target suppression levels for nlp modes.
// log{0.001, 0.00001, 0.00000001}
static const float kTargetSupp[3] = {-6.9f, -11.5f, -18.4f};
// Two sets of parameters, one for the extended filter mode.
static const float kExtendedMinOverDrive[3] = {3.0f, 6.0f, 15.0f};
static const float kNormalMinOverDrive[3] = {1.0f, 2.0f, 5.0f};
const float WebRtcAec_kExtendedSmoothingCoefficients[2][2] = {{0.9f, 0.1f},
{0.92f, 0.08f}};
const float WebRtcAec_kNormalSmoothingCoefficients[2][2] = {{0.9f, 0.1f},
{0.93f, 0.07f}};
// Number of partitions forming the NLP's "preferred" bands.
enum { kPrefBandSize = 24 };
WebRtcAecFilterFar WebRtcAec_FilterFar;
WebRtcAecScaleErrorSignal WebRtcAec_ScaleErrorSignal;
WebRtcAecFilterAdaptation WebRtcAec_FilterAdaptation;
WebRtcAecOverdrive WebRtcAec_Overdrive;
WebRtcAecSuppress WebRtcAec_Suppress;
WebRtcAecComputeCoherence WebRtcAec_ComputeCoherence;
WebRtcAecUpdateCoherenceSpectra WebRtcAec_UpdateCoherenceSpectra;
WebRtcAecStoreAsComplex WebRtcAec_StoreAsComplex;
WebRtcAecPartitionDelay WebRtcAec_PartitionDelay;
WebRtcAecWindowData WebRtcAec_WindowData;
__inline static float MulRe(float aRe, float aIm, float bRe, float bIm) {
return aRe * bRe - aIm * bIm;
}
__inline static float MulIm(float aRe, float aIm, float bRe, float bIm) {
return aRe * bIm + aIm * bRe;
}
// TODO(minyue): Due to a legacy bug, |framelevel| and |averagelevel| use a
// window, of which the length is 1 unit longer than indicated. Remove "+1" when
// the code is refactored.
PowerLevel::PowerLevel()
: framelevel(kSubCountLen + 1), averagelevel(kCountLen + 1) {}
BlockBuffer::BlockBuffer() {
buffer_ = WebRtc_CreateBuffer(kBufferSizeBlocks, sizeof(float) * PART_LEN);
RTC_CHECK(buffer_);
ReInit();
}
BlockBuffer::~BlockBuffer() {
WebRtc_FreeBuffer(buffer_);
}
void BlockBuffer::ReInit() {
WebRtc_InitBuffer(buffer_);
}
void BlockBuffer::Insert(const float block[PART_LEN]) {
WebRtc_WriteBuffer(buffer_, block, 1);
}
void BlockBuffer::ExtractExtendedBlock(float extended_block[PART_LEN2]) {
float* block_ptr = NULL;
RTC_DCHECK_LT(0, AvaliableSpace());
// Extract the previous block.
WebRtc_MoveReadPtr(buffer_, -1);
size_t read_elements = WebRtc_ReadBuffer(
buffer_, reinterpret_cast<void**>(&block_ptr), &extended_block[0], 1);
if (read_elements == 0u) {
std::fill_n(&extended_block[0], PART_LEN, 0.0f);
} else if (block_ptr != &extended_block[0]) {
memcpy(&extended_block[0], block_ptr, PART_LEN * sizeof(float));
}
// Extract the current block.
read_elements =
WebRtc_ReadBuffer(buffer_, reinterpret_cast<void**>(&block_ptr),
&extended_block[PART_LEN], 1);
if (read_elements == 0u) {
std::fill_n(&extended_block[PART_LEN], PART_LEN, 0.0f);
} else if (block_ptr != &extended_block[PART_LEN]) {
memcpy(&extended_block[PART_LEN], block_ptr, PART_LEN * sizeof(float));
}
}
int BlockBuffer::AdjustSize(int buffer_size_decrease) {
return WebRtc_MoveReadPtr(buffer_, buffer_size_decrease);
}
size_t BlockBuffer::Size() {
return static_cast<int>(WebRtc_available_read(buffer_));
}
size_t BlockBuffer::AvaliableSpace() {
return WebRtc_available_write(buffer_);
}
DivergentFilterFraction::DivergentFilterFraction()
: count_(0), occurrence_(0), fraction_(-1.0) {}
void DivergentFilterFraction::Reset() {
Clear();
fraction_ = -1.0;
}
void DivergentFilterFraction::AddObservation(const PowerLevel& nearlevel,
const PowerLevel& linoutlevel,
const PowerLevel& nlpoutlevel) {
const float near_level = nearlevel.framelevel.GetLatestMean();
const float level_increase =
linoutlevel.framelevel.GetLatestMean() - near_level;
const bool output_signal_active =
nlpoutlevel.framelevel.GetLatestMean() > 40.0 * nlpoutlevel.minlevel;
// Level increase should be, in principle, negative, when the filter
// does not diverge. Here we allow some margin (0.01 * near end level) and
// numerical error (1.0). We count divergence only when the AEC output
// signal is active.
if (output_signal_active && level_increase > std::max(0.01 * near_level, 1.0))
occurrence_++;
++count_;
if (count_ == kDivergentFilterFractionAggregationWindowSize) {
fraction_ = static_cast<float>(occurrence_) /
kDivergentFilterFractionAggregationWindowSize;
Clear();
}
}
float DivergentFilterFraction::GetLatestFraction() const {
return fraction_;
}
void DivergentFilterFraction::Clear() {
count_ = 0;
occurrence_ = 0;
}
// TODO(minyue): Moving some initialization from WebRtcAec_CreateAec() to ctor.
AecCore::AecCore(int instance_index)
: data_dumper(new ApmDataDumper(instance_index)) {}
AecCore::~AecCore() {}
static int CmpFloat(const void* a, const void* b) {
const float* da = (const float*)a;
const float* db = (const float*)b;
return (*da > *db) - (*da < *db);
}
static void FilterFar(int num_partitions,
int x_fft_buf_block_pos,
float x_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float h_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float y_fft[2][PART_LEN1]) {
int i;
for (i = 0; i < num_partitions; i++) {
int j;
int xPos = (i + x_fft_buf_block_pos) * PART_LEN1;
int pos = i * PART_LEN1;
// Check for wrap
if (i + x_fft_buf_block_pos >= num_partitions) {
xPos -= num_partitions * (PART_LEN1);
}
for (j = 0; j < PART_LEN1; j++) {
y_fft[0][j] += MulRe(x_fft_buf[0][xPos + j], x_fft_buf[1][xPos + j],
h_fft_buf[0][pos + j], h_fft_buf[1][pos + j]);
y_fft[1][j] += MulIm(x_fft_buf[0][xPos + j], x_fft_buf[1][xPos + j],
h_fft_buf[0][pos + j], h_fft_buf[1][pos + j]);
}
}
}
static void ScaleErrorSignal(float mu,
float error_threshold,
float x_pow[PART_LEN1],
float ef[2][PART_LEN1]) {
int i;
float abs_ef;
for (i = 0; i < (PART_LEN1); i++) {
ef[0][i] /= (x_pow[i] + 1e-10f);
ef[1][i] /= (x_pow[i] + 1e-10f);
abs_ef = sqrtf(ef[0][i] * ef[0][i] + ef[1][i] * ef[1][i]);
if (abs_ef > error_threshold) {
abs_ef = error_threshold / (abs_ef + 1e-10f);
ef[0][i] *= abs_ef;
ef[1][i] *= abs_ef;
}
// Stepsize factor
ef[0][i] *= mu;
ef[1][i] *= mu;
}
}
static void FilterAdaptation(
const OouraFft& ooura_fft,
int num_partitions,
int x_fft_buf_block_pos,
float x_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float e_fft[2][PART_LEN1],
float h_fft_buf[2][kExtendedNumPartitions * PART_LEN1]) {
int i, j;
float fft[PART_LEN2];
for (i = 0; i < num_partitions; i++) {
int xPos = (i + x_fft_buf_block_pos) * (PART_LEN1);
int pos;
// Check for wrap
if (i + x_fft_buf_block_pos >= num_partitions) {
xPos -= num_partitions * PART_LEN1;
}
pos = i * PART_LEN1;
for (j = 0; j < PART_LEN; j++) {
fft[2 * j] = MulRe(x_fft_buf[0][xPos + j], -x_fft_buf[1][xPos + j],
e_fft[0][j], e_fft[1][j]);
fft[2 * j + 1] = MulIm(x_fft_buf[0][xPos + j], -x_fft_buf[1][xPos + j],
e_fft[0][j], e_fft[1][j]);
}
fft[1] =
MulRe(x_fft_buf[0][xPos + PART_LEN], -x_fft_buf[1][xPos + PART_LEN],
e_fft[0][PART_LEN], e_fft[1][PART_LEN]);
ooura_fft.InverseFft(fft);
memset(fft + PART_LEN, 0, sizeof(float) * PART_LEN);
// fft scaling
{
float scale = 2.0f / PART_LEN2;
for (j = 0; j < PART_LEN; j++) {
fft[j] *= scale;
}
}
ooura_fft.Fft(fft);
h_fft_buf[0][pos] += fft[0];
h_fft_buf[0][pos + PART_LEN] += fft[1];
for (j = 1; j < PART_LEN; j++) {
h_fft_buf[0][pos + j] += fft[2 * j];
h_fft_buf[1][pos + j] += fft[2 * j + 1];
}
}
}
static void Overdrive(float overdrive_scaling,
const float hNlFb,
float hNl[PART_LEN1]) {
for (int i = 0; i < PART_LEN1; ++i) {
// Weight subbands
if (hNl[i] > hNlFb) {
hNl[i] = WebRtcAec_weightCurve[i] * hNlFb +
(1 - WebRtcAec_weightCurve[i]) * hNl[i];
}
hNl[i] = powf(hNl[i], overdrive_scaling * WebRtcAec_overDriveCurve[i]);
}
}
static void Suppress(const float hNl[PART_LEN1], float efw[2][PART_LEN1]) {
for (int i = 0; i < PART_LEN1; ++i) {
// Suppress error signal
efw[0][i] *= hNl[i];
efw[1][i] *= hNl[i];
// Ooura fft returns incorrect sign on imaginary component. It matters here
// because we are making an additive change with comfort noise.
efw[1][i] *= -1;
}
}
static int PartitionDelay(
int num_partitions,
float h_fft_buf[2][kExtendedNumPartitions * PART_LEN1]) {
// Measures the energy in each filter partition and returns the partition with
// highest energy.
// TODO(bjornv): Spread computational cost by computing one partition per
// block?
float wfEnMax = 0;
int i;
int delay = 0;
for (i = 0; i < num_partitions; i++) {
int j;
int pos = i * PART_LEN1;
float wfEn = 0;
for (j = 0; j < PART_LEN1; j++) {
wfEn += h_fft_buf[0][pos + j] * h_fft_buf[0][pos + j] +
h_fft_buf[1][pos + j] * h_fft_buf[1][pos + j];
}
if (wfEn > wfEnMax) {
wfEnMax = wfEn;
delay = i;
}
}
return delay;
}
// Update metric with 10 * log10(numerator / denominator).
static void UpdateLogRatioMetric(Stats* metric,
float numerator,
float denominator) {
RTC_DCHECK(metric);
RTC_CHECK(numerator >= 0);
RTC_CHECK(denominator >= 0);
const float log_numerator = std::log10(numerator + 1e-10f);
const float log_denominator = std::log10(denominator + 1e-10f);
metric->instant = 10.0f * (log_numerator - log_denominator);
// Max.
if (metric->instant > metric->max)
metric->max = metric->instant;
// Min.
if (metric->instant < metric->min)
metric->min = metric->instant;
// Average.
metric->counter++;
// This is to protect overflow, which should almost never happen.
RTC_CHECK_NE(0, metric->counter);
metric->sum += metric->instant;
metric->average = metric->sum / metric->counter;
// Upper mean.
if (metric->instant > metric->average) {
metric->hicounter++;
// This is to protect overflow, which should almost never happen.
RTC_CHECK_NE(0, metric->hicounter);
metric->hisum += metric->instant;
metric->himean = metric->hisum / metric->hicounter;
}
}
// Threshold to protect against the ill-effects of a zero far-end.
const float WebRtcAec_kMinFarendPSD = 15;
// Updates the following smoothed Power Spectral Densities (PSD):
// - sd : near-end
// - se : residual echo
// - sx : far-end
// - sde : cross-PSD of near-end and residual echo
// - sxd : cross-PSD of near-end and far-end
//
// In addition to updating the PSDs, also the filter diverge state is
// determined.
static void UpdateCoherenceSpectra(int mult,
bool extended_filter_enabled,
float efw[2][PART_LEN1],
float dfw[2][PART_LEN1],
float xfw[2][PART_LEN1],
CoherenceState* coherence_state,
short* filter_divergence_state,
int* extreme_filter_divergence) {
// Power estimate smoothing coefficients.
const float* ptrGCoh =
extended_filter_enabled
? WebRtcAec_kExtendedSmoothingCoefficients[mult - 1]
: WebRtcAec_kNormalSmoothingCoefficients[mult - 1];
int i;
float sdSum = 0, seSum = 0;
for (i = 0; i < PART_LEN1; i++) {
coherence_state->sd[i] =
ptrGCoh[0] * coherence_state->sd[i] +
ptrGCoh[1] * (dfw[0][i] * dfw[0][i] + dfw[1][i] * dfw[1][i]);
coherence_state->se[i] =
ptrGCoh[0] * coherence_state->se[i] +
ptrGCoh[1] * (efw[0][i] * efw[0][i] + efw[1][i] * efw[1][i]);
// We threshold here to protect against the ill-effects of a zero farend.
// The threshold is not arbitrarily chosen, but balances protection and
// adverse interaction with the algorithm's tuning.
// TODO(bjornv): investigate further why this is so sensitive.
coherence_state->sx[i] =
ptrGCoh[0] * coherence_state->sx[i] +
ptrGCoh[1] *
WEBRTC_SPL_MAX(xfw[0][i] * xfw[0][i] + xfw[1][i] * xfw[1][i],
WebRtcAec_kMinFarendPSD);
coherence_state->sde[i][0] =
ptrGCoh[0] * coherence_state->sde[i][0] +
ptrGCoh[1] * (dfw[0][i] * efw[0][i] + dfw[1][i] * efw[1][i]);
coherence_state->sde[i][1] =
ptrGCoh[0] * coherence_state->sde[i][1] +
ptrGCoh[1] * (dfw[0][i] * efw[1][i] - dfw[1][i] * efw[0][i]);
coherence_state->sxd[i][0] =
ptrGCoh[0] * coherence_state->sxd[i][0] +
ptrGCoh[1] * (dfw[0][i] * xfw[0][i] + dfw[1][i] * xfw[1][i]);
coherence_state->sxd[i][1] =
ptrGCoh[0] * coherence_state->sxd[i][1] +
ptrGCoh[1] * (dfw[0][i] * xfw[1][i] - dfw[1][i] * xfw[0][i]);
sdSum += coherence_state->sd[i];
seSum += coherence_state->se[i];
}
// Divergent filter safeguard update.
*filter_divergence_state =
(*filter_divergence_state ? 1.05f : 1.0f) * seSum > sdSum;
// Signal extreme filter divergence if the error is significantly larger
// than the nearend (13 dB).
*extreme_filter_divergence = (seSum > (19.95f * sdSum));
}
// Window time domain data to be used by the fft.
__inline static void WindowData(float* x_windowed, const float* x) {
int i;
for (i = 0; i < PART_LEN; i++) {
x_windowed[i] = x[i] * WebRtcAec_sqrtHanning[i];
x_windowed[PART_LEN + i] =
x[PART_LEN + i] * WebRtcAec_sqrtHanning[PART_LEN - i];
}
}
// Puts fft output data into a complex valued array.
__inline static void StoreAsComplex(const float* data,
float data_complex[2][PART_LEN1]) {
int i;
data_complex[0][0] = data[0];
data_complex[1][0] = 0;
for (i = 1; i < PART_LEN; i++) {
data_complex[0][i] = data[2 * i];
data_complex[1][i] = data[2 * i + 1];
}
data_complex[0][PART_LEN] = data[1];
data_complex[1][PART_LEN] = 0;
}
static void ComputeCoherence(const CoherenceState* coherence_state,
float* cohde,
float* cohxd) {
// Subband coherence
for (int i = 0; i < PART_LEN1; i++) {
cohde[i] = (coherence_state->sde[i][0] * coherence_state->sde[i][0] +
coherence_state->sde[i][1] * coherence_state->sde[i][1]) /
(coherence_state->sd[i] * coherence_state->se[i] + 1e-10f);
cohxd[i] = (coherence_state->sxd[i][0] * coherence_state->sxd[i][0] +
coherence_state->sxd[i][1] * coherence_state->sxd[i][1]) /
(coherence_state->sx[i] * coherence_state->sd[i] + 1e-10f);
}
}
static void GetHighbandGain(const float* lambda, float* nlpGainHband) {
int i;
*nlpGainHband = 0.0f;
for (i = freqAvgIc; i < PART_LEN1 - 1; i++) {
*nlpGainHband += lambda[i];
}
*nlpGainHband /= static_cast<float>(PART_LEN1 - 1 - freqAvgIc);
}
static void GenerateComplexNoise(uint32_t* seed, float noise[2][PART_LEN1]) {
const float kPi2 = 6.28318530717959f;
int16_t randW16[PART_LEN];
WebRtcSpl_RandUArray(randW16, PART_LEN, seed);
noise[0][0] = 0;
noise[1][0] = 0;
for (size_t i = 1; i < PART_LEN1; i++) {
float tmp = kPi2 * randW16[i - 1] / 32768.f;
noise[0][i] = cosf(tmp);
noise[1][i] = -sinf(tmp);
}
noise[1][PART_LEN] = 0;
}
static void ComfortNoise(bool generate_high_frequency_noise,
uint32_t* seed,
float e_fft[2][PART_LEN1],
float high_frequency_comfort_noise[2][PART_LEN1],
const float* noise_spectrum,
const float* suppressor_gain) {
float complex_noise[2][PART_LEN1];
GenerateComplexNoise(seed, complex_noise);
// Shape, scale and add comfort noise.
for (int i = 1; i < PART_LEN1; ++i) {
float noise_scaling =
sqrtf(WEBRTC_SPL_MAX(1 - suppressor_gain[i] * suppressor_gain[i], 0)) *
sqrtf(noise_spectrum[i]);
e_fft[0][i] += noise_scaling * complex_noise[0][i];
e_fft[1][i] += noise_scaling * complex_noise[1][i];
}
// Form comfort noise for higher frequencies.
if (generate_high_frequency_noise) {
// Compute average noise power and nlp gain over the second half of freq
// spectrum (i.e., 4->8khz).
int start_avg_band = PART_LEN1 / 2;
float upper_bands_noise_power = 0.f;
float upper_bands_suppressor_gain = 0.f;
for (int i = start_avg_band; i < PART_LEN1; ++i) {
upper_bands_noise_power += sqrtf(noise_spectrum[i]);
upper_bands_suppressor_gain +=
sqrtf(WEBRTC_SPL_MAX(1 - suppressor_gain[i] * suppressor_gain[i], 0));
}
upper_bands_noise_power /= (PART_LEN1 - start_avg_band);
upper_bands_suppressor_gain /= (PART_LEN1 - start_avg_band);
// Shape, scale and add comfort noise.
float noise_scaling = upper_bands_suppressor_gain * upper_bands_noise_power;
high_frequency_comfort_noise[0][0] = 0;
high_frequency_comfort_noise[1][0] = 0;
for (int i = 1; i < PART_LEN1; ++i) {
high_frequency_comfort_noise[0][i] = noise_scaling * complex_noise[0][i];
high_frequency_comfort_noise[1][i] = noise_scaling * complex_noise[1][i];
}
high_frequency_comfort_noise[1][PART_LEN] = 0;
} else {
memset(high_frequency_comfort_noise, 0,
2 * PART_LEN1 * sizeof(high_frequency_comfort_noise[0][0]));
}
}
static void InitLevel(PowerLevel* level) {
const float kBigFloat = 1E17f;
level->averagelevel.Reset();
level->framelevel.Reset();
level->minlevel = kBigFloat;
}
static void InitStats(Stats* stats) {
stats->instant = kOffsetLevel;
stats->average = kOffsetLevel;
stats->max = kOffsetLevel;
stats->min = kOffsetLevel * (-1);
stats->sum = 0;
stats->hisum = 0;
stats->himean = kOffsetLevel;
stats->counter = 0;
stats->hicounter = 0;
}
static void InitMetrics(AecCore* self) {
self->stateCounter = 0;
InitLevel(&self->farlevel);
InitLevel(&self->nearlevel);
InitLevel(&self->linoutlevel);
InitLevel(&self->nlpoutlevel);
InitStats(&self->erl);
InitStats(&self->erle);
InitStats(&self->aNlp);
InitStats(&self->rerl);
self->divergent_filter_fraction.Reset();
}
static float CalculatePower(const float* in, size_t num_samples) {
size_t k;
float energy = 0.0f;
for (k = 0; k < num_samples; ++k) {
energy += in[k] * in[k];
}
return energy / num_samples;
}
static void UpdateLevel(PowerLevel* level, float power) {
level->framelevel.AddValue(power);
if (level->framelevel.EndOfBlock()) {
const float new_frame_level = level->framelevel.GetLatestMean();
if (new_frame_level > 0) {
if (new_frame_level < level->minlevel) {
level->minlevel = new_frame_level; // New minimum.
} else {
level->minlevel *= (1 + 0.001f); // Small increase.
}
}
level->averagelevel.AddValue(new_frame_level);
}
}
static void UpdateMetrics(AecCore* aec) {
const float actThresholdNoisy = 8.0f;
const float actThresholdClean = 40.0f;
const float noisyPower = 300000.0f;
float actThreshold;
if (aec->echoState) { // Check if echo is likely present
aec->stateCounter++;
}
if (aec->linoutlevel.framelevel.EndOfBlock()) {
aec->divergent_filter_fraction.AddObservation(
aec->nearlevel, aec->linoutlevel, aec->nlpoutlevel);
}
if (aec->farlevel.averagelevel.EndOfBlock()) {
if (aec->farlevel.minlevel < noisyPower) {
actThreshold = actThresholdClean;
} else {
actThreshold = actThresholdNoisy;
}
const float far_average_level = aec->farlevel.averagelevel.GetLatestMean();
// The last condition is to let estimation be made in active far-end
// segments only.
if ((aec->stateCounter > (0.5f * kCountLen * kSubCountLen)) &&
(aec->farlevel.framelevel.EndOfBlock()) &&
(far_average_level > (actThreshold * aec->farlevel.minlevel))) {
// ERL: error return loss.
const float near_average_level =
aec->nearlevel.averagelevel.GetLatestMean();
UpdateLogRatioMetric(&aec->erl, far_average_level, near_average_level);
// A_NLP: error return loss enhanced before the nonlinear suppression.
const float linout_average_level =
aec->linoutlevel.averagelevel.GetLatestMean();
UpdateLogRatioMetric(&aec->aNlp, near_average_level,
linout_average_level);
// ERLE: error return loss enhanced.
const float nlpout_average_level =
aec->nlpoutlevel.averagelevel.GetLatestMean();
UpdateLogRatioMetric(&aec->erle, near_average_level,
nlpout_average_level);
}
aec->stateCounter = 0;
}
}
static void UpdateDelayMetrics(AecCore* self) {
int i = 0;
int delay_values = 0;
int median = 0;
int lookahead = WebRtc_lookahead(self->delay_estimator);
const int kMsPerBlock = PART_LEN / (self->mult * 8);
int64_t l1_norm = 0;
if (self->num_delay_values == 0) {
// We have no new delay value data. Even though -1 is a valid |median| in
// the sense that we allow negative values, it will practically never be
// used since multiples of |kMsPerBlock| will always be returned.
// We therefore use -1 to indicate in the logs that the delay estimator was
// not able to estimate the delay.
self->delay_median = -1;
self->delay_std = -1;
self->fraction_poor_delays = -1;
return;
}
// Start value for median count down.
delay_values = self->num_delay_values >> 1;
// Get median of delay values since last update.
for (i = 0; i < kHistorySizeBlocks; i++) {
delay_values -= self->delay_histogram[i];
if (delay_values < 0) {
median = i;
break;
}
}
// Account for lookahead.
self->delay_median = (median - lookahead) * kMsPerBlock;
// Calculate the L1 norm, with median value as central moment.
for (i = 0; i < kHistorySizeBlocks; i++) {
l1_norm += abs(i - median) * self->delay_histogram[i];
}
self->delay_std = static_cast<int>((l1_norm + self->num_delay_values / 2) /
self->num_delay_values) *
kMsPerBlock;
// Determine fraction of delays that are out of bounds, that is, either
// negative (anti-causal system) or larger than the AEC filter length.
{
int num_delays_out_of_bounds = self->num_delay_values;
const int histogram_length =
sizeof(self->delay_histogram) / sizeof(self->delay_histogram[0]);
for (i = lookahead; i < lookahead + self->num_partitions; ++i) {
if (i < histogram_length)
num_delays_out_of_bounds -= self->delay_histogram[i];
}
self->fraction_poor_delays =
static_cast<float>(num_delays_out_of_bounds) / self->num_delay_values;
}
// Reset histogram.
memset(self->delay_histogram, 0, sizeof(self->delay_histogram));
self->num_delay_values = 0;
}
static void ScaledInverseFft(const OouraFft& ooura_fft,
float freq_data[2][PART_LEN1],
float time_data[PART_LEN2],
float scale,
int conjugate) {
int i;
const float normalization = scale / static_cast<float>(PART_LEN2);
const float sign = (conjugate ? -1 : 1);
time_data[0] = freq_data[0][0] * normalization;
time_data[1] = freq_data[0][PART_LEN] * normalization;
for (i = 1; i < PART_LEN; i++) {
time_data[2 * i] = freq_data[0][i] * normalization;
time_data[2 * i + 1] = sign * freq_data[1][i] * normalization;
}
ooura_fft.InverseFft(time_data);
}
static void Fft(const OouraFft& ooura_fft,
float time_data[PART_LEN2],
float freq_data[2][PART_LEN1]) {
int i;
ooura_fft.Fft(time_data);
// Reorder fft output data.
freq_data[1][0] = 0;
freq_data[1][PART_LEN] = 0;
freq_data[0][0] = time_data[0];
freq_data[0][PART_LEN] = time_data[1];
for (i = 1; i < PART_LEN; i++) {
freq_data[0][i] = time_data[2 * i];
freq_data[1][i] = time_data[2 * i + 1];
}
}
static int SignalBasedDelayCorrection(AecCore* self) {
int delay_correction = 0;
int last_delay = -2;
RTC_DCHECK(self);
#if !defined(WEBRTC_ANDROID)
// On desktops, turn on correction after |kDelayCorrectionStart| frames. This
// is to let the delay estimation get a chance to converge. Also, if the
// playout audio volume is low (or even muted) the delay estimation can return
// a very large delay, which will break the AEC if it is applied.
if (self->frame_count < kDelayCorrectionStart) {
self->data_dumper->DumpRaw("aec_da_reported_delay", 1, &last_delay);
return 0;
}
#endif
// 1. Check for non-negative delay estimate. Note that the estimates we get
// from the delay estimation are not compensated for lookahead. Hence, a
// negative |last_delay| is an invalid one.
// 2. Verify that there is a delay change. In addition, only allow a change
// if the delay is outside a certain region taking the AEC filter length
// into account.
// TODO(bjornv): Investigate if we can remove the non-zero delay change check.
// 3. Only allow delay correction if the delay estimation quality exceeds
// |delay_quality_threshold|.
// 4. Finally, verify that the proposed |delay_correction| is feasible by
// comparing with the size of the far-end buffer.
last_delay = WebRtc_last_delay(self->delay_estimator);
self->data_dumper->DumpRaw("aec_da_reported_delay", 1, &last_delay);
if ((last_delay >= 0) && (last_delay != self->previous_delay) &&
(WebRtc_last_delay_quality(self->delay_estimator) >
self->delay_quality_threshold)) {
int delay = last_delay - WebRtc_lookahead(self->delay_estimator);
// Allow for a slack in the actual delay, defined by a |lower_bound| and an
// |upper_bound|. The adaptive echo cancellation filter is currently
// |num_partitions| (of 64 samples) long. If the delay estimate is negative
// or at least 3/4 of the filter length we open up for correction.
const int lower_bound = 0;
const int upper_bound = self->num_partitions * 3 / 4;
const int do_correction = delay <= lower_bound || delay > upper_bound;
if (do_correction == 1) {
int available_read = self->farend_block_buffer_.Size();
// With |shift_offset| we gradually rely on the delay estimates. For
// positive delays we reduce the correction by |shift_offset| to lower the
// risk of pushing the AEC into a non causal state. For negative delays
// we rely on the values up to a rounding error, hence compensate by 1
// element to make sure to push the delay into the causal region.
delay_correction = -delay;
delay_correction += delay > self->shift_offset ? self->shift_offset : 1;
self->shift_offset--;
self->shift_offset = (self->shift_offset <= 1 ? 1 : self->shift_offset);
if (delay_correction > available_read - self->mult - 1) {
// There is not enough data in the buffer to perform this shift. Hence,
// we do not rely on the delay estimate and do nothing.
delay_correction = 0;
} else {
self->previous_delay = last_delay;
++self->delay_correction_count;
}
}
}
// Update the |delay_quality_threshold| once we have our first delay
// correction.
if (self->delay_correction_count > 0) {
float delay_quality = WebRtc_last_delay_quality(self->delay_estimator);
delay_quality =
(delay_quality > kDelayQualityThresholdMax ? kDelayQualityThresholdMax
: delay_quality);
self->delay_quality_threshold =
(delay_quality > self->delay_quality_threshold
? delay_quality
: self->delay_quality_threshold);
}
self->data_dumper->DumpRaw("aec_da_delay_correction", 1, &delay_correction);
return delay_correction;
}
static void RegressorPower(
int num_partitions,
int latest_added_partition,
float x_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float x_pow[PART_LEN1]) {
RTC_DCHECK_LT(latest_added_partition, num_partitions);
memset(x_pow, 0, PART_LEN1 * sizeof(x_pow[0]));
int partition = latest_added_partition;
int x_fft_buf_position = partition * PART_LEN1;
for (int i = 0; i < num_partitions; ++i) {
for (int bin = 0; bin < PART_LEN1; ++bin) {
float re = x_fft_buf[0][x_fft_buf_position];
float im = x_fft_buf[1][x_fft_buf_position];
x_pow[bin] += re * re + im * im;
++x_fft_buf_position;
}
++partition;
if (partition == num_partitions) {
partition = 0;
RTC_DCHECK_EQ(num_partitions * PART_LEN1, x_fft_buf_position);
x_fft_buf_position = 0;
}
}
}
static void EchoSubtraction(
const OouraFft& ooura_fft,
int num_partitions,
int extended_filter_enabled,
int* extreme_filter_divergence,
float filter_step_size,
float error_threshold,
float* x_fft,
int* x_fft_buf_block_pos,
float x_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float* const y,
float x_pow[PART_LEN1],
float h_fft_buf[2][kExtendedNumPartitions * PART_LEN1],
float echo_subtractor_output[PART_LEN]) {
float s_fft[2][PART_LEN1];
float e_extended[PART_LEN2];
float s_extended[PART_LEN2];
float* s;
float e[PART_LEN];
float e_fft[2][PART_LEN1];
int i;
// Update the x_fft_buf block position.
(*x_fft_buf_block_pos)--;
if ((*x_fft_buf_block_pos) == -1) {
*x_fft_buf_block_pos = num_partitions - 1;
}
// Buffer x_fft.
memcpy(x_fft_buf[0] + (*x_fft_buf_block_pos) * PART_LEN1, x_fft,
sizeof(float) * PART_LEN1);
memcpy(x_fft_buf[1] + (*x_fft_buf_block_pos) * PART_LEN1, &x_fft[PART_LEN1],
sizeof(float) * PART_LEN1);
memset(s_fft, 0, sizeof(s_fft));
// Conditionally reset the echo subtraction filter if the filter has diverged
// significantly.
if (!extended_filter_enabled && *extreme_filter_divergence) {
memset(h_fft_buf, 0,
2 * kExtendedNumPartitions * PART_LEN1 * sizeof(h_fft_buf[0][0]));
*extreme_filter_divergence = 0;
}
// Produce echo estimate s_fft.
WebRtcAec_FilterFar(num_partitions, *x_fft_buf_block_pos, x_fft_buf,
h_fft_buf, s_fft);
// Compute the time-domain echo estimate s.
ScaledInverseFft(ooura_fft, s_fft, s_extended, 2.0f, 0);
s = &s_extended[PART_LEN];
// Compute the time-domain echo prediction error.
for (i = 0; i < PART_LEN; ++i) {
e[i] = y[i] - s[i];
}
// Compute the frequency domain echo prediction error.
memset(e_extended, 0, sizeof(float) * PART_LEN);
memcpy(e_extended + PART_LEN, e, sizeof(float) * PART_LEN);
Fft(ooura_fft, e_extended, e_fft);
// Scale error signal inversely with far power.
WebRtcAec_ScaleErrorSignal(filter_step_size, error_threshold, x_pow, e_fft);
WebRtcAec_FilterAdaptation(ooura_fft, num_partitions, *x_fft_buf_block_pos,
x_fft_buf, e_fft, h_fft_buf);
memcpy(echo_subtractor_output, e, sizeof(float) * PART_LEN);
}
static void FormSuppressionGain(AecCore* aec,
float cohde[PART_LEN1],
float cohxd[PART_LEN1],
float hNl[PART_LEN1]) {
float hNlDeAvg, hNlXdAvg;
float hNlPref[kPrefBandSize];
float hNlFb = 0, hNlFbLow = 0;
const int prefBandSize = kPrefBandSize / aec->mult;
const float prefBandQuant = 0.75f, prefBandQuantLow = 0.5f;
const int minPrefBand = 4 / aec->mult;
// Power estimate smoothing coefficients.
const float* min_overdrive = aec->extended_filter_enabled
? kExtendedMinOverDrive
: kNormalMinOverDrive;
hNlXdAvg = 0;
for (int i = minPrefBand; i < prefBandSize + minPrefBand; ++i) {
hNlXdAvg += cohxd[i];
}
hNlXdAvg /= prefBandSize;
hNlXdAvg = 1 - hNlXdAvg;
hNlDeAvg = 0;
for (int i = minPrefBand; i < prefBandSize + minPrefBand; ++i) {
hNlDeAvg += cohde[i];
}
hNlDeAvg /= prefBandSize;
if (hNlXdAvg < 0.75f && hNlXdAvg < aec->hNlXdAvgMin) {
aec->hNlXdAvgMin = hNlXdAvg;
}
if (hNlDeAvg > 0.98f && hNlXdAvg > 0.9f) {
aec->stNearState = 1;
} else if (hNlDeAvg < 0.95f || hNlXdAvg < 0.8f) {
aec->stNearState = 0;
}
if (aec->hNlXdAvgMin == 1) {
aec->echoState = 0;
aec->overDrive = min_overdrive[aec->nlp_mode];
if (aec->stNearState == 1) {
memcpy(hNl, cohde, sizeof(hNl[0]) * PART_LEN1);
hNlFb = hNlDeAvg;
hNlFbLow = hNlDeAvg;
} else {
for (int i = 0; i < PART_LEN1; ++i) {
hNl[i] = 1 - cohxd[i];
hNl[i] = std::max(hNl[i], 0.f);
}
hNlFb = hNlXdAvg;
hNlFbLow = hNlXdAvg;
}
} else {
if (aec->stNearState == 1) {
aec->echoState = 0;
memcpy(hNl, cohde, sizeof(hNl[0]) * PART_LEN1);
hNlFb = hNlDeAvg;
hNlFbLow = hNlDeAvg;
} else {
aec->echoState = 1;
for (int i = 0; i < PART_LEN1; ++i) {
hNl[i] = WEBRTC_SPL_MIN(cohde[i], 1 - cohxd[i]);
hNl[i] = std::max(hNl[i], 0.f);
}
// Select an order statistic from the preferred bands.
// TODO(peah): Using quicksort now, but a selection algorithm may be
// preferred.
memcpy(hNlPref, &hNl[minPrefBand], sizeof(float) * prefBandSize);
qsort(hNlPref, prefBandSize, sizeof(float), CmpFloat);
hNlFb = hNlPref[static_cast<int>(
std::floor(prefBandQuant * (prefBandSize - 1)))];
hNlFbLow = hNlPref[static_cast<int>(
std::floor(prefBandQuantLow * (prefBandSize - 1)))];
}
}
// Track the local filter minimum to determine suppression overdrive.
if (hNlFbLow < 0.6f && hNlFbLow < aec->hNlFbLocalMin) {
aec->hNlFbLocalMin = hNlFbLow;
aec->hNlFbMin = hNlFbLow;
aec->hNlNewMin = 1;
aec->hNlMinCtr = 0;
}
aec->hNlFbLocalMin =
WEBRTC_SPL_MIN(aec->hNlFbLocalMin + 0.0008f / aec->mult, 1);
aec->hNlXdAvgMin = WEBRTC_SPL_MIN(aec->hNlXdAvgMin + 0.0006f / aec->mult, 1);
if (aec->hNlNewMin == 1) {
aec->hNlMinCtr++;
}
if (aec->hNlMinCtr == 2) {
aec->hNlNewMin = 0;
aec->hNlMinCtr = 0;
aec->overDrive = WEBRTC_SPL_MAX(
kTargetSupp[aec->nlp_mode] /
static_cast<float>(std::log(aec->hNlFbMin + 1e-10f) + 1e-10f),
min_overdrive[aec->nlp_mode]);
}
// Smooth the overdrive.
if (aec->overDrive < aec->overdrive_scaling) {
aec->overdrive_scaling =
0.99f * aec->overdrive_scaling + 0.01f * aec->overDrive;
} else {
aec->overdrive_scaling =
0.9f * aec->overdrive_scaling + 0.1f * aec->overDrive;
}
// Apply the overdrive.
WebRtcAec_Overdrive(aec->overdrive_scaling, hNlFb, hNl);
}
static void EchoSuppression(const OouraFft& ooura_fft,
AecCore* aec,
float* nearend_extended_block_lowest_band,
float farend_extended_block[PART_LEN2],
float* echo_subtractor_output,
float output[NUM_HIGH_BANDS_MAX + 1][PART_LEN]) {
float efw[2][PART_LEN1];
float xfw[2][PART_LEN1];
float dfw[2][PART_LEN1];
float comfortNoiseHband[2][PART_LEN1];
float fft[PART_LEN2];
float nlpGainHband;
int i;
size_t j;
// Coherence and non-linear filter
float cohde[PART_LEN1], cohxd[PART_LEN1];
float hNl[PART_LEN1];
// Filter energy
const int delayEstInterval = 10 * aec->mult;
float* xfw_ptr = NULL;
// Update eBuf with echo subtractor output.
memcpy(aec->eBuf + PART_LEN, echo_subtractor_output,
sizeof(float) * PART_LEN);
// Analysis filter banks for the echo suppressor.
// Windowed near-end ffts.
WindowData(fft, nearend_extended_block_lowest_band);
ooura_fft.Fft(fft);
StoreAsComplex(fft, dfw);
// Windowed echo suppressor output ffts.
WindowData(fft, aec->eBuf);
ooura_fft.Fft(fft);
StoreAsComplex(fft, efw);
// NLP
// Convert far-end partition to the frequency domain with windowing.
WindowData(fft, farend_extended_block);
Fft(ooura_fft, fft, xfw);
xfw_ptr = &xfw[0][0];
// Buffer far.
memcpy(aec->xfwBuf, xfw_ptr, sizeof(float) * 2 * PART_LEN1);
aec->delayEstCtr++;
if (aec->delayEstCtr == delayEstInterval) {
aec->delayEstCtr = 0;
aec->delayIdx = WebRtcAec_PartitionDelay(aec->num_partitions, aec->wfBuf);
}
aec->data_dumper->DumpRaw("aec_nlp_delay", 1, &aec->delayIdx);
// Use delayed far.
memcpy(xfw, aec->xfwBuf + aec->delayIdx * PART_LEN1,
sizeof(xfw[0][0]) * 2 * PART_LEN1);
WebRtcAec_UpdateCoherenceSpectra(aec->mult, aec->extended_filter_enabled == 1,
efw, dfw, xfw, &aec->coherence_state,
&aec->divergeState,
&aec->extreme_filter_divergence);
WebRtcAec_ComputeCoherence(&aec->coherence_state, cohde, cohxd);
// Select the microphone signal as output if the filter is deemed to have
// diverged.
if (aec->divergeState) {
memcpy(efw, dfw, sizeof(efw[0][0]) * 2 * PART_LEN1);
}
FormSuppressionGain(aec, cohde, cohxd, hNl);
aec->data_dumper->DumpRaw("aec_nlp_gain", PART_LEN1, hNl);
WebRtcAec_Suppress(hNl, efw);
// Add comfort noise.
ComfortNoise(aec->num_bands > 1, &aec->seed, efw, comfortNoiseHband,
aec->noisePow, hNl);
// Inverse error fft.
ScaledInverseFft(ooura_fft, efw, fft, 2.0f, 1);
// Overlap and add to obtain output.
for (i = 0; i < PART_LEN; i++) {
output[0][i] = (fft[i] * WebRtcAec_sqrtHanning[i] +
aec->outBuf[i] * WebRtcAec_sqrtHanning[PART_LEN - i]);
// Saturate output to keep it in the allowed range.
output[0][i] = WEBRTC_SPL_SAT(WEBRTC_SPL_WORD16_MAX, output[0][i],
WEBRTC_SPL_WORD16_MIN);
}
memcpy(aec->outBuf, &fft[PART_LEN], PART_LEN * sizeof(aec->outBuf[0]));
// For H band
if (aec->num_bands > 1) {
// H band gain
// average nlp over low band: average over second half of freq spectrum
// (4->8khz)
GetHighbandGain(hNl, &nlpGainHband);
// Inverse comfort_noise
ScaledInverseFft(ooura_fft, comfortNoiseHband, fft, 2.0f, 0);
// compute gain factor
for (j = 1; j < aec->num_bands; ++j) {
for (i = 0; i < PART_LEN; i++) {
output[j][i] = aec->previous_nearend_block[j][i] * nlpGainHband;
}
}
// Add some comfort noise where Hband is attenuated.
for (i = 0; i < PART_LEN; i++) {
output[1][i] += cnScaleHband * fft[i];
}
// Saturate output to keep it in the allowed range.
for (j = 1; j < aec->num_bands; ++j) {
for (i = 0; i < PART_LEN; i++) {
output[j][i] = WEBRTC_SPL_SAT(WEBRTC_SPL_WORD16_MAX, output[j][i],
WEBRTC_SPL_WORD16_MIN);
}
}
}
// Copy the current block to the old position.
memcpy(aec->eBuf, aec->eBuf + PART_LEN, sizeof(float) * PART_LEN);
memmove(aec->xfwBuf + PART_LEN1, aec->xfwBuf,
sizeof(aec->xfwBuf) - sizeof(complex_t) * PART_LEN1);
}
static void ProcessNearendBlock(
AecCore* aec,
float farend_extended_block_lowest_band[PART_LEN2],
float nearend_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN],
float output_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN]) {
size_t i;
float fft[PART_LEN2];
float nearend_extended_block_lowest_band[PART_LEN2];
float farend_fft[2][PART_LEN1];
float nearend_fft[2][PART_LEN1];
float far_spectrum = 0.0f;
float near_spectrum = 0.0f;
float abs_far_spectrum[PART_LEN1];
float abs_near_spectrum[PART_LEN1];
const float gPow[2] = {0.9f, 0.1f};
// Noise estimate constants.
const int noiseInitBlocks = 500 * aec->mult;
const float step = 0.1f;
const float ramp = 1.0002f;
const float gInitNoise[2] = {0.999f, 0.001f};
float echo_subtractor_output[PART_LEN];
aec->data_dumper->DumpWav("aec_far", PART_LEN,
&farend_extended_block_lowest_band[PART_LEN],
std::min(aec->sampFreq, 16000), 1);
aec->data_dumper->DumpWav("aec_near", PART_LEN, &nearend_block[0][0],
std::min(aec->sampFreq, 16000), 1);
if (aec->metricsMode == 1) {
// Update power levels
UpdateLevel(
&aec->farlevel,
CalculatePower(&farend_extended_block_lowest_band[PART_LEN], PART_LEN));
UpdateLevel(&aec->nearlevel,
CalculatePower(&nearend_block[0][0], PART_LEN));
}
// Convert far-end signal to the frequency domain.
memcpy(fft, farend_extended_block_lowest_band, sizeof(float) * PART_LEN2);
Fft(aec->ooura_fft, fft, farend_fft);
// Form extended nearend frame.
memcpy(&nearend_extended_block_lowest_band[0],
&aec->previous_nearend_block[0][0], sizeof(float) * PART_LEN);
memcpy(&nearend_extended_block_lowest_band[PART_LEN], &nearend_block[0][0],
sizeof(float) * PART_LEN);
// Convert near-end signal to the frequency domain.
memcpy(fft, nearend_extended_block_lowest_band, sizeof(float) * PART_LEN2);
Fft(aec->ooura_fft, fft, nearend_fft);
// Power smoothing.
if (aec->refined_adaptive_filter_enabled) {
for (i = 0; i < PART_LEN1; ++i) {
far_spectrum = farend_fft[0][i] * farend_fft[0][i] +
farend_fft[1][i] * farend_fft[1][i];
// Calculate the magnitude spectrum.
abs_far_spectrum[i] = sqrtf(far_spectrum);
}
RegressorPower(aec->num_partitions, aec->xfBufBlockPos, aec->xfBuf,
aec->xPow);
} else {
for (i = 0; i < PART_LEN1; ++i) {
far_spectrum = farend_fft[0][i] * farend_fft[0][i] +
farend_fft[1][i] * farend_fft[1][i];
aec->xPow[i] =
gPow[0] * aec->xPow[i] + gPow[1] * aec->num_partitions * far_spectrum;
// Calculate the magnitude spectrum.
abs_far_spectrum[i] = sqrtf(far_spectrum);
}
}
for (i = 0; i < PART_LEN1; ++i) {
near_spectrum = nearend_fft[0][i] * nearend_fft[0][i] +
nearend_fft[1][i] * nearend_fft[1][i];
aec->dPow[i] = gPow[0] * aec->dPow[i] + gPow[1] * near_spectrum;
// Calculate the magnitude spectrum.
abs_near_spectrum[i] = sqrtf(near_spectrum);
}
// Estimate noise power. Wait until dPow is more stable.
if (aec->noiseEstCtr > 50) {
for (i = 0; i < PART_LEN1; i++) {
if (aec->dPow[i] < aec->dMinPow[i]) {
aec->dMinPow[i] =
(aec->dPow[i] + step * (aec->dMinPow[i] - aec->dPow[i])) * ramp;
} else {
aec->dMinPow[i] *= ramp;
}
}
}
// Smooth increasing noise power from zero at the start,
// to avoid a sudden burst of comfort noise.
if (aec->noiseEstCtr < noiseInitBlocks) {
aec->noiseEstCtr++;
for (i = 0; i < PART_LEN1; i++) {
if (aec->dMinPow[i] > aec->dInitMinPow[i]) {
aec->dInitMinPow[i] = gInitNoise[0] * aec->dInitMinPow[i] +
gInitNoise[1] * aec->dMinPow[i];
} else {
aec->dInitMinPow[i] = aec->dMinPow[i];
}
}
aec->noisePow = aec->dInitMinPow;
} else {
aec->noisePow = aec->dMinPow;
}
// Block wise delay estimation used for logging
if (aec->delay_logging_enabled) {
if (WebRtc_AddFarSpectrumFloat(aec->delay_estimator_farend,
abs_far_spectrum, PART_LEN1) == 0) {
int delay_estimate = WebRtc_DelayEstimatorProcessFloat(
aec->delay_estimator, abs_near_spectrum, PART_LEN1);
if (delay_estimate >= 0) {
// Update delay estimate buffer.
aec->delay_histogram[delay_estimate]++;
aec->num_delay_values++;
}
if (aec->delay_metrics_delivered == 1 &&
aec->num_delay_values >= kDelayMetricsAggregationWindow) {
UpdateDelayMetrics(aec);
}
}
}
// Perform echo subtraction.
EchoSubtraction(
aec->ooura_fft, aec->num_partitions, aec->extended_filter_enabled,
&aec->extreme_filter_divergence, aec->filter_step_size,
aec->error_threshold, &farend_fft[0][0], &aec->xfBufBlockPos, aec->xfBuf,
&nearend_block[0][0], aec->xPow, aec->wfBuf, echo_subtractor_output);
aec->data_dumper->DumpRaw("aec_h_fft", PART_LEN1 * aec->num_partitions,
&aec->wfBuf[0][0]);
aec->data_dumper->DumpRaw("aec_h_fft", PART_LEN1 * aec->num_partitions,
&aec->wfBuf[1][0]);
aec->data_dumper->DumpWav("aec_out_linear", PART_LEN, echo_subtractor_output,
std::min(aec->sampFreq, 16000), 1);
if (aec->metricsMode == 1) {
UpdateLevel(&aec->linoutlevel,
CalculatePower(echo_subtractor_output, PART_LEN));
}
// Perform echo suppression.
EchoSuppression(aec->ooura_fft, aec, nearend_extended_block_lowest_band,
farend_extended_block_lowest_band, echo_subtractor_output,
output_block);
if (aec->metricsMode == 1) {
UpdateLevel(&aec->nlpoutlevel,
CalculatePower(&output_block[0][0], PART_LEN));
UpdateMetrics(aec);
}
// Store the nearend signal until the next frame.
for (i = 0; i < aec->num_bands; ++i) {
memcpy(&aec->previous_nearend_block[i][0], &nearend_block[i][0],
sizeof(float) * PART_LEN);
}
aec->data_dumper->DumpWav("aec_out", PART_LEN, &output_block[0][0],
std::min(aec->sampFreq, 16000), 1);
}
AecCore* WebRtcAec_CreateAec(int instance_count) {
AecCore* aec = new AecCore(instance_count);
if (!aec) {
return NULL;
}
aec->nearend_buffer_size = 0;
memset(&aec->nearend_buffer[0], 0, sizeof(aec->nearend_buffer));
// Start the output buffer with zeros to be able to produce
// a full output frame in the first frame.
aec->output_buffer_size = PART_LEN - (FRAME_LEN - PART_LEN);
memset(&aec->output_buffer[0], 0, sizeof(aec->output_buffer));
aec->delay_estimator_farend =
WebRtc_CreateDelayEstimatorFarend(PART_LEN1, kHistorySizeBlocks);
if (aec->delay_estimator_farend == NULL) {
WebRtcAec_FreeAec(aec);
return NULL;
}
// We create the delay_estimator with the same amount of maximum lookahead as
// the delay history size (kHistorySizeBlocks) for symmetry reasons.
aec->delay_estimator = WebRtc_CreateDelayEstimator(
aec->delay_estimator_farend, kHistorySizeBlocks);
if (aec->delay_estimator == NULL) {
WebRtcAec_FreeAec(aec);
return NULL;
}
#ifdef WEBRTC_ANDROID
aec->delay_agnostic_enabled = 1; // DA-AEC enabled by default.
// DA-AEC assumes the system is causal from the beginning and will self adjust
// the lookahead when shifting is required.
WebRtc_set_lookahead(aec->delay_estimator, 0);
#else
aec->delay_agnostic_enabled = 0;
WebRtc_set_lookahead(aec->delay_estimator, kLookaheadBlocks);
#endif
aec->extended_filter_enabled = 0;
aec->refined_adaptive_filter_enabled = false;
// Assembly optimization
WebRtcAec_FilterFar = FilterFar;
WebRtcAec_ScaleErrorSignal = ScaleErrorSignal;
WebRtcAec_FilterAdaptation = FilterAdaptation;
WebRtcAec_Overdrive = Overdrive;
WebRtcAec_Suppress = Suppress;
WebRtcAec_ComputeCoherence = ComputeCoherence;
WebRtcAec_UpdateCoherenceSpectra = UpdateCoherenceSpectra;
WebRtcAec_StoreAsComplex = StoreAsComplex;
WebRtcAec_PartitionDelay = PartitionDelay;
WebRtcAec_WindowData = WindowData;
#if defined(WEBRTC_ARCH_X86_FAMILY)
if (WebRtc_GetCPUInfo(kSSE2)) {
WebRtcAec_InitAec_SSE2();
}
#endif
#if defined(MIPS_FPU_LE)
WebRtcAec_InitAec_mips();
#endif
#if defined(WEBRTC_HAS_NEON)
WebRtcAec_InitAec_neon();
#endif
return aec;
}
void WebRtcAec_FreeAec(AecCore* aec) {
if (aec == NULL) {
return;
}
WebRtc_FreeDelayEstimator(aec->delay_estimator);
WebRtc_FreeDelayEstimatorFarend(aec->delay_estimator_farend);
delete aec;
}
static void SetAdaptiveFilterStepSize(AecCore* aec) {
// Extended filter adaptation parameter.
// TODO(ajm): No narrowband tuning yet.
const float kExtendedMu = 0.4f;
if (aec->refined_adaptive_filter_enabled) {
aec->filter_step_size = 0.05f;
} else {
if (aec->extended_filter_enabled) {
aec->filter_step_size = kExtendedMu;
} else {
if (aec->sampFreq == 8000) {
aec->filter_step_size = 0.6f;
} else {
aec->filter_step_size = 0.5f;
}
}
}
}
static void SetErrorThreshold(AecCore* aec) {
// Extended filter adaptation parameter.
// TODO(ajm): No narrowband tuning yet.
static const float kExtendedErrorThreshold = 1.0e-6f;
if (aec->extended_filter_enabled) {
aec->error_threshold = kExtendedErrorThreshold;
} else {
if (aec->sampFreq == 8000) {
aec->error_threshold = 2e-6f;
} else {
aec->error_threshold = 1.5e-6f;
}
}
}
int WebRtcAec_InitAec(AecCore* aec, int sampFreq) {
int i;
aec->data_dumper->InitiateNewSetOfRecordings();
aec->sampFreq = sampFreq;
SetAdaptiveFilterStepSize(aec);
SetErrorThreshold(aec);
if (sampFreq == 8000) {
aec->num_bands = 1;
} else {
aec->num_bands = (size_t)(sampFreq / 16000);
}
// Start the output buffer with zeros to be able to produce
// a full output frame in the first frame.
aec->output_buffer_size = PART_LEN - (FRAME_LEN - PART_LEN);
memset(&aec->output_buffer[0], 0, sizeof(aec->output_buffer));
aec->nearend_buffer_size = 0;
memset(&aec->nearend_buffer[0], 0, sizeof(aec->nearend_buffer));
// Initialize far-end buffer.
aec->farend_block_buffer_.ReInit();
aec->system_delay = 0;
if (WebRtc_InitDelayEstimatorFarend(aec->delay_estimator_farend) != 0) {
return -1;
}
if (WebRtc_InitDelayEstimator(aec->delay_estimator) != 0) {
return -1;
}
aec->delay_logging_enabled = 0;
aec->delay_metrics_delivered = 0;
memset(aec->delay_histogram, 0, sizeof(aec->delay_histogram));
aec->num_delay_values = 0;
aec->delay_median = -1;
aec->delay_std = -1;
aec->fraction_poor_delays = -1.0f;
aec->previous_delay = -2; // (-2): Uninitialized.
aec->delay_correction_count = 0;
aec->shift_offset = kInitialShiftOffset;
aec->delay_quality_threshold = kDelayQualityThresholdMin;
aec->num_partitions = kNormalNumPartitions;
// Update the delay estimator with filter length. We use half the
// |num_partitions| to take the echo path into account. In practice we say
// that the echo has a duration of maximum half |num_partitions|, which is not
// true, but serves as a crude measure.
WebRtc_set_allowed_offset(aec->delay_estimator, aec->num_partitions / 2);
// TODO(bjornv): I currently hard coded the enable. Once we've established
// that AECM has no performance regression, robust_validation will be enabled
// all the time and the APIs to turn it on/off will be removed. Hence, remove
// this line then.
WebRtc_enable_robust_validation(aec->delay_estimator, 1);
aec->frame_count = 0;
// Default target suppression mode.
aec->nlp_mode = 1;
// Sampling frequency multiplier w.r.t. 8 kHz.
// In case of multiple bands we process the lower band in 16 kHz, hence the
// multiplier is always 2.
if (aec->num_bands > 1) {
aec->mult = 2;
} else {
aec->mult = static_cast<int16_t>(aec->sampFreq) / 8000;
}
aec->farBufWritePos = 0;
aec->farBufReadPos = 0;
aec->inSamples = 0;
aec->outSamples = 0;
aec->knownDelay = 0;
// Initialize buffers
memset(aec->previous_nearend_block, 0, sizeof(aec->previous_nearend_block));
memset(aec->eBuf, 0, sizeof(aec->eBuf));
memset(aec->xPow, 0, sizeof(aec->xPow));
memset(aec->dPow, 0, sizeof(aec->dPow));
memset(aec->dInitMinPow, 0, sizeof(aec->dInitMinPow));
aec->noisePow = aec->dInitMinPow;
aec->noiseEstCtr = 0;
// Initial comfort noise power
for (i = 0; i < PART_LEN1; i++) {
aec->dMinPow[i] = 1.0e6f;
}
// Holds the last block written to
aec->xfBufBlockPos = 0;
// TODO(peah): Investigate need for these initializations. Deleting them
// doesn't change the output at all and yields 0.4% overall speedup.
memset(aec->xfBuf, 0, sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);
memset(aec->wfBuf, 0, sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);
memset(aec->coherence_state.sde, 0, sizeof(complex_t) * PART_LEN1);
memset(aec->coherence_state.sxd, 0, sizeof(complex_t) * PART_LEN1);
memset(aec->xfwBuf, 0,
sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);
memset(aec->coherence_state.se, 0, sizeof(float) * PART_LEN1);
// To prevent numerical instability in the first block.
for (i = 0; i < PART_LEN1; i++) {
aec->coherence_state.sd[i] = 1;
}
for (i = 0; i < PART_LEN1; i++) {
aec->coherence_state.sx[i] = 1;
}
memset(aec->hNs, 0, sizeof(aec->hNs));
memset(aec->outBuf, 0, sizeof(float) * PART_LEN);
aec->hNlFbMin = 1;
aec->hNlFbLocalMin = 1;
aec->hNlXdAvgMin = 1;
aec->hNlNewMin = 0;
aec->hNlMinCtr = 0;
aec->overDrive = 2;
aec->overdrive_scaling = 2;
aec->delayIdx = 0;
aec->stNearState = 0;
aec->echoState = 0;
aec->divergeState = 0;
aec->seed = 777;
aec->delayEstCtr = 0;
aec->extreme_filter_divergence = 0;
// Metrics disabled by default
aec->metricsMode = 0;
InitMetrics(aec);
return 0;
}
void WebRtcAec_BufferFarendBlock(AecCore* aec, const float* farend) {
// Check if the buffer is full, and in that case flush the oldest data.
if (aec->farend_block_buffer_.AvaliableSpace() < 1) {
aec->farend_block_buffer_.AdjustSize(1);
}
aec->farend_block_buffer_.Insert(farend);
}
int WebRtcAec_AdjustFarendBufferSizeAndSystemDelay(AecCore* aec,
int buffer_size_decrease) {
int achieved_buffer_size_decrease =
aec->farend_block_buffer_.AdjustSize(buffer_size_decrease);
aec->system_delay -= achieved_buffer_size_decrease * PART_LEN;
return achieved_buffer_size_decrease;
}
void FormNearendBlock(
size_t nearend_start_index,
size_t num_bands,
const float* const* nearend_frame,
size_t num_samples_from_nearend_frame,
const float nearend_buffer[NUM_HIGH_BANDS_MAX + 1]
[PART_LEN - (FRAME_LEN - PART_LEN)],
float nearend_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN]) {
RTC_DCHECK_LE(num_samples_from_nearend_frame, PART_LEN);
const int num_samples_from_buffer = PART_LEN - num_samples_from_nearend_frame;
if (num_samples_from_buffer > 0) {
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&nearend_block[i][0], &nearend_buffer[i][0],
num_samples_from_buffer * sizeof(float));
}
}
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&nearend_block[i][num_samples_from_buffer],
&nearend_frame[i][nearend_start_index],
num_samples_from_nearend_frame * sizeof(float));
}
}
void BufferNearendFrame(
size_t nearend_start_index,
size_t num_bands,
const float* const* nearend_frame,
size_t num_samples_to_buffer,
float nearend_buffer[NUM_HIGH_BANDS_MAX + 1]
[PART_LEN - (FRAME_LEN - PART_LEN)]) {
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&nearend_buffer[i][0],
&nearend_frame[i][nearend_start_index + FRAME_LEN -
num_samples_to_buffer],
num_samples_to_buffer * sizeof(float));
}
}
void BufferOutputBlock(
size_t num_bands,
const float output_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN],
size_t* output_buffer_size,
float output_buffer[NUM_HIGH_BANDS_MAX + 1][2 * PART_LEN]) {
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&output_buffer[i][*output_buffer_size], &output_block[i][0],
PART_LEN * sizeof(float));
}
(*output_buffer_size) += PART_LEN;
}
void FormOutputFrame(size_t output_start_index,
size_t num_bands,
size_t* output_buffer_size,
float output_buffer[NUM_HIGH_BANDS_MAX + 1][2 * PART_LEN],
float* const* output_frame) {
RTC_DCHECK_LE(FRAME_LEN, *output_buffer_size);
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&output_frame[i][output_start_index], &output_buffer[i][0],
FRAME_LEN * sizeof(float));
}
(*output_buffer_size) -= FRAME_LEN;
if (*output_buffer_size > 0) {
RTC_DCHECK_GE(2 * PART_LEN - FRAME_LEN, (*output_buffer_size));
for (size_t i = 0; i < num_bands; ++i) {
memcpy(&output_buffer[i][0], &output_buffer[i][FRAME_LEN],
(*output_buffer_size) * sizeof(float));
}
}
}
void WebRtcAec_ProcessFrames(AecCore* aec,
const float* const* nearend,
size_t num_bands,
size_t num_samples,
int knownDelay,
float* const* out) {
RTC_DCHECK(num_samples == 80 || num_samples == 160);
aec->frame_count++;
// For each frame the process is as follows:
// 1) If the system_delay indicates on being too small for processing a
// frame we stuff the buffer with enough data for 10 ms.
// 2 a) Adjust the buffer to the system delay, by moving the read pointer.
// b) Apply signal based delay correction, if we have detected poor AEC
// performance.
// 3) TODO(bjornv): Investigate if we need to add this:
// If we can't move read pointer due to buffer size limitations we
// flush/stuff the buffer.
// 4) Process as many partitions as possible.
// 5) Update the |system_delay| with respect to a full frame of FRAME_LEN
// samples. Even though we will have data left to process (we work with
// partitions) we consider updating a whole frame, since that's the
// amount of data we input and output in audio_processing.
// 6) Update the outputs.
// The AEC has two different delay estimation algorithms built in. The
// first relies on delay input values from the user and the amount of
// shifted buffer elements is controlled by |knownDelay|. This delay will
// give a guess on how much we need to shift far-end buffers to align with
// the near-end signal. The other delay estimation algorithm uses the
// far- and near-end signals to find the offset between them. This one
// (called "signal delay") is then used to fine tune the alignment, or
// simply compensate for errors in the system based one.
// Note that the two algorithms operate independently. Currently, we only
// allow one algorithm to be turned on.
RTC_DCHECK_EQ(aec->num_bands, num_bands);
for (size_t j = 0; j < num_samples; j += FRAME_LEN) {
// 1) At most we process |aec->mult|+1 partitions in 10 ms. Make sure we
// have enough far-end data for that by stuffing the buffer if the
// |system_delay| indicates others.
if (aec->system_delay < FRAME_LEN) {
// We don't have enough data so we rewind 10 ms.
WebRtcAec_AdjustFarendBufferSizeAndSystemDelay(aec, -(aec->mult + 1));
}
if (!aec->delay_agnostic_enabled) {
// 2 a) Compensate for a possible change in the system delay.
// TODO(bjornv): Investigate how we should round the delay difference;
// right now we know that incoming |knownDelay| is underestimated when
// it's less than |aec->knownDelay|. We therefore, round (-32) in that
// direction. In the other direction, we don't have this situation, but
// might flush one partition too little. This can cause non-causality,
// which should be investigated. Maybe, allow for a non-symmetric
// rounding, like -16.
int move_elements = (aec->knownDelay - knownDelay - 32) / PART_LEN;
int moved_elements = aec->farend_block_buffer_.AdjustSize(move_elements);
aec->knownDelay -= moved_elements * PART_LEN;
} else {
// 2 b) Apply signal based delay correction.
int move_elements = SignalBasedDelayCorrection(aec);
int moved_elements = aec->farend_block_buffer_.AdjustSize(move_elements);
int far_near_buffer_diff =
aec->farend_block_buffer_.Size() -
(aec->nearend_buffer_size + FRAME_LEN) / PART_LEN;
WebRtc_SoftResetDelayEstimator(aec->delay_estimator, moved_elements);
WebRtc_SoftResetDelayEstimatorFarend(aec->delay_estimator_farend,
moved_elements);
// If we rely on reported system delay values only, a buffer underrun here
// can never occur since we've taken care of that in 1) above. Here, we
// apply signal based delay correction and can therefore end up with
// buffer underruns since the delay estimation can be wrong. We therefore
// stuff the buffer with enough elements if needed.
if (far_near_buffer_diff < 0) {
WebRtcAec_AdjustFarendBufferSizeAndSystemDelay(aec,
far_near_buffer_diff);
}
}
static_assert(
16 == (FRAME_LEN - PART_LEN),
"These constants need to be properly related for this code to work");
float output_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN];
float nearend_block[NUM_HIGH_BANDS_MAX + 1][PART_LEN];
float farend_extended_block_lowest_band[PART_LEN2];
// Form and process a block of nearend samples, buffer the output block of
// samples.
aec->farend_block_buffer_.ExtractExtendedBlock(
farend_extended_block_lowest_band);
FormNearendBlock(j, num_bands, nearend, PART_LEN - aec->nearend_buffer_size,
aec->nearend_buffer, nearend_block);
ProcessNearendBlock(aec, farend_extended_block_lowest_band, nearend_block,
output_block);
BufferOutputBlock(num_bands, output_block, &aec->output_buffer_size,
aec->output_buffer);
if ((FRAME_LEN - PART_LEN + aec->nearend_buffer_size) == PART_LEN) {
// When possible (every fourth frame) form and process a second block of
// nearend samples, buffer the output block of samples.
aec->farend_block_buffer_.ExtractExtendedBlock(
farend_extended_block_lowest_band);
FormNearendBlock(j + FRAME_LEN - PART_LEN, num_bands, nearend, PART_LEN,
aec->nearend_buffer, nearend_block);
ProcessNearendBlock(aec, farend_extended_block_lowest_band, nearend_block,
output_block);
BufferOutputBlock(num_bands, output_block, &aec->output_buffer_size,
aec->output_buffer);
// Reset the buffer size as there are no samples left in the nearend input
// to buffer.
aec->nearend_buffer_size = 0;
} else {
// Buffer the remaining samples in the nearend input.
aec->nearend_buffer_size += FRAME_LEN - PART_LEN;
BufferNearendFrame(j, num_bands, nearend, aec->nearend_buffer_size,
aec->nearend_buffer);
}
// 5) Update system delay with respect to the entire frame.
aec->system_delay -= FRAME_LEN;
// 6) Form the output frame.
FormOutputFrame(j, num_bands, &aec->output_buffer_size, aec->output_buffer,
out);
}
}
int WebRtcAec_GetDelayMetricsCore(AecCore* self,
int* median,
int* std,
float* fraction_poor_delays) {
RTC_DCHECK(self);
RTC_DCHECK(median);
RTC_DCHECK(std);
if (self->delay_logging_enabled == 0) {
// Logging disabled.
return -1;
}
if (self->delay_metrics_delivered == 0) {
UpdateDelayMetrics(self);
self->delay_metrics_delivered = 1;
}
*median = self->delay_median;
*std = self->delay_std;
*fraction_poor_delays = self->fraction_poor_delays;
return 0;
}
int WebRtcAec_echo_state(AecCore* self) {
return self->echoState;
}
void WebRtcAec_GetEchoStats(AecCore* self,
Stats* erl,
Stats* erle,
Stats* a_nlp,
float* divergent_filter_fraction) {
RTC_DCHECK(erl);
RTC_DCHECK(erle);
RTC_DCHECK(a_nlp);
*erl = self->erl;
*erle = self->erle;
*a_nlp = self->aNlp;
*divergent_filter_fraction =
self->divergent_filter_fraction.GetLatestFraction();
}
void WebRtcAec_SetConfigCore(AecCore* self,
int nlp_mode,
int metrics_mode,
int delay_logging) {
RTC_DCHECK_GE(nlp_mode, 0);
RTC_DCHECK_LT(nlp_mode, 3);
self->nlp_mode = nlp_mode;
self->metricsMode = metrics_mode;
if (self->metricsMode) {
InitMetrics(self);
}
// Turn on delay logging if it is either set explicitly or if delay agnostic
// AEC is enabled (which requires delay estimates).
self->delay_logging_enabled = delay_logging || self->delay_agnostic_enabled;
if (self->delay_logging_enabled) {
memset(self->delay_histogram, 0, sizeof(self->delay_histogram));
}
}
void WebRtcAec_enable_delay_agnostic(AecCore* self, int enable) {
self->delay_agnostic_enabled = enable;
}
int WebRtcAec_delay_agnostic_enabled(AecCore* self) {
return self->delay_agnostic_enabled;
}
void WebRtcAec_enable_refined_adaptive_filter(AecCore* self, bool enable) {
self->refined_adaptive_filter_enabled = enable;
SetAdaptiveFilterStepSize(self);
SetErrorThreshold(self);
}
bool WebRtcAec_refined_adaptive_filter_enabled(const AecCore* self) {
return self->refined_adaptive_filter_enabled;
}
void WebRtcAec_enable_extended_filter(AecCore* self, int enable) {
self->extended_filter_enabled = enable;
SetAdaptiveFilterStepSize(self);
SetErrorThreshold(self);
self->num_partitions = enable ? kExtendedNumPartitions : kNormalNumPartitions;
// Update the delay estimator with filter length. See InitAEC() for details.
WebRtc_set_allowed_offset(self->delay_estimator, self->num_partitions / 2);
}
int WebRtcAec_extended_filter_enabled(AecCore* self) {
return self->extended_filter_enabled;
}
int WebRtcAec_system_delay(AecCore* self) {
return self->system_delay;
}
void WebRtcAec_SetSystemDelay(AecCore* self, int delay) {
RTC_DCHECK_GE(delay, 0);
self->system_delay = delay;
}
} // namespace webrtc
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