AEC部分核心源码

AEC部分核心源码

由于该是在别人的github里边下载到的，先把代码贴上：

/** Copyright (c) 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.AEC核心算法与对齐信号一起呈现*/#include "webrtc/modules/audio_processing/aec/aec_core.h"#ifdef WEBRTC_AEC_DEBUG_DUMP#include <stdio.h>#endif#include <assert.h>#include <math.h>#include <stddef.h> // size_t#include <stdlib.h>#include <string.h>#include "webrtc/common_audio/ring_buffer.h"#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"#include "webrtc/modules/audio_processing/aec/aec_common.h"#include "webrtc/modules/audio_processing/aec/aec_core_internal.h"#include "webrtc/modules/audio_processing/aec/aec_rdft.h"#include "webrtc/modules/audio_processing/logging/aec_logging.h"#include "webrtc/modules/audio_processing/utility/delay_estimator_wrapper.h"#include "webrtc/system_wrappers/include/cpu_features_wrapper.h"#include "webrtc/typedefs.h"// Buffer size (samples)static const size_t kBufSizePartitions = 250; // 1秒16hz的音频，1 second of audio in 16 kHz.// Metrics：指标static const int subCountLen = 4;//子计数长度static const int countLen = 50;//延迟指标聚合窗口static const int kDelayMetricsAggregationWindow = 1250; // 5 seconds at 16 kHz.// Quantities to control H band scaling for SWB input--用于控制SWB输入的H波段缩放的数量static const int flagHbandCn = 1; // 用于在H波段添加舒适噪声的标志static const float cnScaleHband =(float)0.4; //H波段舒适噪音的标度//初始bin，用于平均低频段的nlp增益static const int freqAvgIc = PART_LEN / 2;// matlab代码生成表：// win = sqrt（hanning（63））; win= [0; win（1:32）];// fprintf（1，'\ t％.14f，％.14f，％.14f，\ n'，win）;//添加汉明窗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,0.1950903213f, 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代码生成表：// weightCurve = [0 ; 0.3 * sqrt(linspace(0,1,64))' + 0.1];// fprintf(1, '\t%.4f, %.4f, %.4f, %.4f, %.4f, %.4f,\n', weightCurve) //曲线权重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代码生成表：// overDriveCurve = [sqrt(linspace(0,1,65))' + 1];// fprintf(1, '\t%.4f, %.4f, %.4f, %.4f, %.4f, %.4f,\n', overDriveCurve);//超驱动曲线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};// 延迟不可知AEC参数仍在开发中，可能会更改。static const float kDelayQualityThresholdMax = 0.07f;static const float kDelayQualityThresholdMin = 0.01f;static const int kInitialShiftOffset = 5;//初始移位偏移#if !defined(WEBRTC_ANDROID)static const int kDelayCorrectionStart = 1500; // 10 ms 块 延迟校正开始数据#endif// nlp模式的目标抑制级别。// log{0.001, 0.00001, 0.00000001}//目标抑制数组static const float kTargetSupp[3] = {-6.9f, -11.5f, -18.4f};// 两套参数，一组用于扩展滤波器模式。static const float kExtendedMinOverDrive[3] = {3.0f, 6.0f, 15.0f};//扩展模式使用static const float kNormalMinOverDrive[3] = {1.0f, 2.0f, 5.0f};//普通参数//扩展平滑系数设置const float WebRtcAec_kExtendedSmoothingCoefficients[2][2] = {{0.9f, 0.1f},{0.92f, 0.08f}};//扩展平滑系数//正常平滑系数设置const float WebRtcAec_kNormalSmoothingCoefficients[2][2] = {{0.9f, 0.1f},{0.93f, 0.07f}};//正常平滑系数// 构成NLP“首选”频段的分区数。enum {kPrefBandSize = 24//首选区段大小};#ifdef WEBRTC_AEC_DEBUG_DUMP//扩展使用上边的实例countextern int webrtc_aec_instance_count;#endifWebRtcAecFilterFar WebRtcAec_FilterFar;//FIR过滤器WebRtcAecScaleErrorSignal WebRtcAec_ScaleErrorSignal;//误差信号e(n)WebRtcAecFilterAdaptation WebRtcAec_FilterAdaptation;//自适应滤波器WebRtcAecOverdriveAndSuppress WebRtcAec_OverdriveAndSuppress;//过载和抑制WebRtcAecComfortNoise WebRtcAec_ComfortNoise;//舒适噪音WebRtcAecSubBandCoherence WebRtcAec_SubbandCoherence;//子带相干性__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;}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);}//远端过滤器的方法static void FilterFar(AecCore* aec, float yf[2][PART_LEN1]) {int i;for (i = 0; i < aec->num_partitions; i++) {int j;//BufBlockPos：缓冲区的位置int xPos = (i + aec->xfBufBlockPos) * PART_LEN1;int pos = i * PART_LEN1;// Check for wrapif (i + aec->xfBufBlockPos >= aec->num_partitions) {xPos -= aec->num_partitions * (PART_LEN1);}for (j = 0; j < PART_LEN1; j++) {yf[0][j] += MulRe(aec->xfBuf[0][xPos + j],aec->xfBuf[1][xPos + j],aec->wfBuf[0][pos + j],aec->wfBuf[1][pos + j]);yf[1][j] += MulIm(aec->xfBuf[0][xPos + j],aec->xfBuf[1][xPos + j],aec->wfBuf[0][pos + j],aec->wfBuf[1][pos + j]);}}}//误差估计error信号static void ScaleErrorSignal(AecCore* aec, float ef[2][PART_LEN1]) {const float mu = aec->extended_filter_enabled ? kExtendedMu : aec->normal_mu;//error_threshold:误差信号阈值const float error_threshold = aec->extended_filter_enabled? kExtendedErrorThreshold: aec->normal_error_threshold;int i;float abs_ef;for (i = 0; i < (PART_LEN1); i++) {ef[0][i] /= (aec->xPow[i] + 1e-10f);ef[1][i] /= (aec->xPow[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;}// 步长因子//const float mu = aec->extended_filter_enabled ? kExtendedMu : aec->normal_mu;ef[0][i] *= mu;ef[1][i] *= mu;}}//无时间限制的滤波器自适应。// TODO（andrew）：考虑使用低复杂度模式。//无限自适应过滤器// static void FilterAdaptationUnconstrained(AecCore* aec, float *fft,// float ef[2][PART_LEN1]) {// int i, j;// for (i = 0; i < aec->num_partitions; i++) {// int xPos = (i + aec->xfBufBlockPos)*(PART_LEN1);// int pos;// // Check for wrap// if (i + aec->xfBufBlockPos >= aec->num_partitions) {//xPos -= aec->num_partitions * PART_LEN1;// }//// pos = i * PART_LEN1;//// for (j = 0; j < PART_LEN1; j++) {//aec->wfBuf[0][pos + j] += MulRe(aec->xfBuf[0][xPos + j],// -aec->xfBuf[1][xPos + j],// ef[0][j], ef[1][j]);//aec->wfBuf[1][pos + j] += MulIm(aec->xfBuf[0][xPos + j],// -aec->xfBuf[1][xPos + j],// ef[0][j], ef[1][j]);// }// }//}//自适应滤波器处理逻辑static void FilterAdaptation(AecCore* aec, float* fft, float ef[2][PART_LEN1]) {int i, j;for (i = 0; i < aec->num_partitions; i++) {int xPos = (i + aec->xfBufBlockPos) * (PART_LEN1);int pos;// Check for wrapif (i + aec->xfBufBlockPos >= aec->num_partitions) {xPos -= aec->num_partitions * PART_LEN1;}pos = i * PART_LEN1;for (j = 0; j < PART_LEN; j++) {fft[2 * j] = MulRe(aec->xfBuf[0][xPos + j],-aec->xfBuf[1][xPos + j],ef[0][j],ef[1][j]);fft[2 * j + 1] = MulIm(aec->xfBuf[0][xPos + j],-aec->xfBuf[1][xPos + j],ef[0][j],ef[1][j]);}fft[1] = MulRe(aec->xfBuf[0][xPos + PART_LEN],-aec->xfBuf[1][xPos + PART_LEN],ef[0][PART_LEN],ef[1][PART_LEN]);//fft的逆变换aec_rdft_inverse_128(fft);memset(fft + PART_LEN, 0, sizeof(float) * PART_LEN);// fft缩放{float scale = 2.0f / PART_LEN2;for (j = 0; j < PART_LEN; j++) {fft[j] *= scale;}}aec_rdft_forward_128(fft);aec->wfBuf[0][pos] += fft[0];aec->wfBuf[0][pos + PART_LEN] += fft[1];for (j = 1; j < PART_LEN; j++) {aec->wfBuf[0][pos + j] += fft[2 * j];aec->wfBuf[1][pos + j] += fft[2 * j + 1];}}}//过载抑制static void OverdriveAndSuppress(AecCore* aec,float hNl[PART_LEN1],const float hNlFb,float efw[2][PART_LEN1]) {int i;for (i = 0; i < PART_LEN1; i++) {// Weight subbandsif (hNl[i] > hNlFb) {hNl[i] = WebRtcAec_weightCurve[i] * hNlFb +(1 - WebRtcAec_weightCurve[i]) * hNl[i];}hNl[i] = powf(hNl[i], aec->overDriveSm * WebRtcAec_overDriveCurve[i]);// 抑制错误信号efw[0][i] *= hNl[i];efw[1][i] *= hNl[i];// Ooura fft 在虚部返回不正确的信号. It matters here// because we are making an additive change with comfort noise.efw[1][i] *= -1;}}//延迟分区static int PartitionDelay(const AecCore* aec) {//测量每个过滤器分区中的能量，并使用//最高能量。// TODO（bjornv）：通过在每个分区上计算一个分区来分散计算成本//阻止？float wfEnMax = 0;int i;int delay = 0;for (i = 0; i < aec->num_partitions; i++) {int j;int pos = i * PART_LEN1;float wfEn = 0;for (j = 0; j < PART_LEN1; j++) {wfEn += aec->wfBuf[0][pos + j] * aec->wfBuf[0][pos + j] +aec->wfBuf[1][pos + j] * aec->wfBuf[1][pos + j];}if (wfEn > wfEnMax) {wfEnMax = wfEn;delay = i;}}return delay;}//阈值，以防止零远端的不良影响。const float WebRtcAec_kMinFarendPSD = 15;// 更新以下平滑的功率谱密度（PSD）：// - sd : near-end--近端// - se : residual echo--残留回波// - sx : far-end--远端// - sde : cross-PSD of near-end and residual echo--近端和残留回波的交叉PSD// - sxd : cross-PSD of near-end and far-end--近端和远端的交叉PSD//// 除了更新PSD，还确定滤波器的发散状态//采取行动后。static void SmoothedPSD(AecCore* aec,float efw[2][PART_LEN1],float dfw[2][PART_LEN1],float xfw[2][PART_LEN1]) {//功率估计平滑系数。const float* ptrGCoh = aec->extended_filter_enabled? WebRtcAec_kExtendedSmoothingCoefficients[aec->mult - 1]: WebRtcAec_kNormalSmoothingCoefficients[aec->mult - 1];int i;float sdSum = 0, seSum = 0;for (i = 0; i < PART_LEN1; i++) {aec->sd[i] = ptrGCoh[0] * aec->sd[i] +ptrGCoh[1] * (dfw[0][i] * dfw[0][i] + dfw[1][i] * dfw[1][i]);aec->se[i] = ptrGCoh[0] * aec->se[i] +ptrGCoh[1] * (efw[0][i] * efw[0][i] + efw[1][i] * efw[1][i]);//我们在此处设置阈值，以防止零费用3的不利影响。//阈值不是任意选择的，但可以平衡保护和//与算法调整之间的不利相互作用。// TODO（bjornv）：进一步研究为什么它如此敏感。aec->sx[i] =ptrGCoh[0] * aec->sx[i] +ptrGCoh[1] * WEBRTC_SPL_MAX(xfw[0][i] * xfw[0][i] + xfw[1][i] * xfw[1][i],WebRtcAec_kMinFarendPSD);aec->sde[i][0] =ptrGCoh[0] * aec->sde[i][0] +ptrGCoh[1] * (dfw[0][i] * efw[0][i] + dfw[1][i] * efw[1][i]);aec->sde[i][1] =ptrGCoh[0] * aec->sde[i][1] +ptrGCoh[1] * (dfw[0][i] * efw[1][i] - dfw[1][i] * efw[0][i]);aec->sxd[i][0] =ptrGCoh[0] * aec->sxd[i][0] +ptrGCoh[1] * (dfw[0][i] * xfw[0][i] + dfw[1][i] * xfw[1][i]);aec->sxd[i][1] =ptrGCoh[0] * aec->sxd[i][1] +ptrGCoh[1] * (dfw[0][i] * xfw[1][i] - dfw[1][i] * xfw[0][i]);sdSum += aec->sd[i];seSum += aec->se[i];}// 发散过滤器防护 .aec->divergeState = (aec->divergeState ? 1.05f : 1.0f) * seSum > sdSum;if (aec->divergeState)memcpy(efw, dfw, sizeof(efw[0][0]) * 2 * PART_LEN1);// 如果误差远大于近端（13 dB），则复位。if (!aec->extended_filter_enabled && seSum > (19.95f * sdSum))memset(aec->wfBuf, 0, sizeof(aec->wfBuf));}// fft要使用的窗口时域数据。__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];}}// 将fft输出数据放入一个复数值数组中。__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;}//子带相干性static void SubbandCoherence(AecCore* aec,float efw[2][PART_LEN1],float xfw[2][PART_LEN1],float* fft,float* cohde,float* cohxd) {float dfw[2][PART_LEN1];int i;if (aec->delayEstCtr == 0)aec->delayIdx = PartitionDelay(aec);// 使用远端延迟memcpy(xfw,aec->xfwBuf + aec->delayIdx * PART_LEN1,sizeof(xfw[0][0]) * 2 * PART_LEN1);// 窗口的近端 fftWindowData(fft, aec->dBuf);aec_rdft_forward_128(fft);StoreAsComplex(fft, dfw);// 窗口的误差 fftWindowData(fft, aec->eBuf);aec_rdft_forward_128(fft);StoreAsComplex(fft, efw);SmoothedPSD(aec, efw, dfw, xfw);// 子带相干性for (i = 0; i < PART_LEN1; i++) {cohde[i] =(aec->sde[i][0] * aec->sde[i][0] + aec->sde[i][1] * aec->sde[i][1]) /(aec->sd[i] * aec->se[i] + 1e-10f);cohxd[i] =(aec->sxd[i][0] * aec->sxd[i][0] + aec->sxd[i][1] * aec->sxd[i][1]) /(aec->sx[i] * aec->sd[i] + 1e-10f);}}//获取高频带增益static void GetHighbandGain(const float* lambda, float* nlpGainHband) {int i;nlpGainHband[0] = (float)0.0;for (i = freqAvgIc; i < PART_LEN1 - 1; i++) {nlpGainHband[0] += lambda[i];}nlpGainHband[0] /= (float)(PART_LEN1 - 1 - freqAvgIc);}//舒适噪音产生static void ComfortNoise(AecCore* aec,float efw[2][PART_LEN1],complex_t* comfortNoiseHband,const float* noisePow,const float* lambda) {int i, num;float rand[PART_LEN];float noise, noiseAvg, tmp, tmpAvg;int16_t randW16[PART_LEN];complex_t u[PART_LEN1];const float pi2 = 6.28318530717959f;// 在[0 1]上生成统一的随机数组WebRtcSpl_RandUArray(randW16, PART_LEN, &aec->seed);for (i = 0; i < PART_LEN; i++) {rand[i] = ((float)randW16[i]) / 32768;}//抑制低频噪声u[0][0] = 0;u[0][1] = 0;for (i = 1; i < PART_LEN1; i++) {tmp = pi2 * rand[i - 1];noise = sqrtf(noisePow[i]);u[i][0] = noise * cosf(tmp);u[i][1] = -noise * sinf(tmp);}u[PART_LEN][1] = 0;for (i = 0; i < PART_LEN1; i++) {// 这是与背景噪声功率匹配的适当权重tmp = sqrtf(WEBRTC_SPL_MAX(1 - lambda[i] * lambda[i], 0));// tmp = 1 - lambda[i];efw[0][i] += tmp * u[i][0];efw[1][i] += tmp * u[i][1];}//用于H波段舒适噪音// TODO：不要两次计算噪声和“ tmp”。 使用以前的结果。noiseAvg = 0.0;tmpAvg = 0.0;num = 0;if (aec->num_bands > 1 && flagHbandCn == 1) {//平均噪音等级//平均频率频谱的后半部分（即4-> 8khz）// TODO：我们不需要num。 我们知道要累加多少元素。for (i = PART_LEN1 >> 1; i < PART_LEN1; i++) {num++;noiseAvg += sqrtf(noisePow[i]);}noiseAvg /= (float)num;//平均nlp比例//平均频率频谱的后半部分（即4-> 8khz）// TODO：我们不需要num。 我们知道要累加多少元素。num = 0;for (i = PART_LEN1 >> 1; i < PART_LEN1; i++) {num++;tmpAvg += sqrtf(WEBRTC_SPL_MAX(1 - lambda[i] * lambda[i], 0));}tmpAvg /= (float)num;//对H波段使用平均噪声// TODO：我们这里可能应该有一个新的随机向量。//拒绝低频噪声。u[0][0] = 0;u[0][1] = 0;for (i = 1; i < PART_LEN1; i++) {tmp = pi2 * rand[i - 1];//对H波段使用平均噪声u[i][0] = noiseAvg * (float)cos(tmp);u[i][1] = -noiseAvg * (float)sin(tmp);}u[PART_LEN][1] = 0;for (i = 0; i < PART_LEN1; i++) {// Use average NLP weight for H bandcomfortNoiseHband[i][0] = tmpAvg * u[i][0];comfortNoiseHband[i][1] = tmpAvg * u[i][1];}}}//初始化levelstatic void InitLevel(PowerLevel* level) {const float kBigFloat = 1E17f;level->averagelevel = 0;level->framelevel = 0;level->minlevel = kBigFloat;level->frsum = 0;level->sfrsum = 0;level->frcounter = 0;level->sfrcounter = 0;}//初始化数据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);}static void UpdateLevel(PowerLevel* level, float in[2][PART_LEN1]) {//在频域中进行能量计算。 FFT在//由于重叠，PART_LEN2个样本的一部分，但是我们只需要能量//一半的数据（最后的PART_LEN样本）。 Parseval的关系状态//能量根据//// \ sum_ {n = 0} ^ {N-1} | x（n）| ^ 2 = 1 / N * \ sum_ {n = 0} ^ {N-1} | X（n）| ^ 2// =能源，////其中N = PART_LEN2。因为我们只对计算能量感兴趣//对于最后的PART_LEN样本，我们通过计算ENERGY和//除以2//// \ sum_ {n = N / 2} ^ {N-1} | x（n）| ^ 2〜=能源/ 2////由于我们处理的是实值时域信号，因此我们只存储频率// bins [0，PART_LEN]，这是| in |由组成。为了计算能量，我们//需要添加缺少部分的贡献// [PART_LEN + 1，PART_LEN2-1]。在相移之前，这些值是相同的//使用[1，PART_LEN-1]中的值，因此将这些值乘以2。//是下面的for循环中的值，但是乘以2和除法//被2取消。// TODO（bjornv）：研究在其他地方重复使用的能源计算//放置在代码中。int k = 1;// Imaginary parts are zero at end points and left out of the calculation.float energy = (in[0][0] * in[0][0]) / 2;energy += (in[0][PART_LEN] * in[0][PART_LEN]) / 2;for (k = 1; k < PART_LEN; k++) {energy += (in[0][k] * in[0][k] + in[1][k] * in[1][k]);}energy /= PART_LEN2;level->sfrsum += energy;level->sfrcounter++;if (level->sfrcounter > subCountLen) {level->framelevel = level->sfrsum / (subCountLen * PART_LEN);level->sfrsum = 0;level->sfrcounter = 0;if (level->framelevel > 0) {if (level->framelevel < level->minlevel) {level->minlevel = level->framelevel; // New minimum.} else {level->minlevel *= (1 + 0.001f); // Small increase.}}level->frcounter++;level->frsum += level->framelevel;if (level->frcounter > countLen) {level->averagelevel = level->frsum / countLen;level->frsum = 0;level->frcounter = 0;}}}static void UpdateMetrics(AecCore* aec) {float dtmp, dtmp2;const float actThresholdNoisy = 8.0f;const float actThresholdClean = 40.0f;const float safety = 0.99995f;const float noisyPower = 300000.0f;float actThreshold;float echo, suppressedEcho;if (aec->echoState) {// 检查是否可能存在回声aec->stateCounter++;}if (aec->farlevel.frcounter == 0) {if (aec->farlevel.minlevel < noisyPower) {actThreshold = actThresholdClean;} else {actThreshold = actThresholdNoisy;}if ((aec->stateCounter > (0.5f * countLen * subCountLen)) &&(aec->farlevel.sfrcounter == 0)// 仅在活动的远端进行估计&&(aec->farlevel.averagelevel >(actThreshold * aec->farlevel.minlevel))) {// 减去噪声功率echo = aec->nearlevel.averagelevel - safety * aec->nearlevel.minlevel;// ERLdtmp = 10 * (float)log10(aec->farlevel.averagelevel /aec->nearlevel.averagelevel +1e-10f);dtmp2 = 10 * (float)log10(aec->farlevel.averagelevel / echo + 1e-10f);aec->erl.instant = dtmp;if (dtmp > aec->erl.max) {aec->erl.max = dtmp;}if (dtmp < aec->erl.min) {aec->erl.min = dtmp;}aec->erl.counter++;aec->erl.sum += dtmp;aec->erl.average = aec->erl.sum / aec->erl.counter;// 上均值if (dtmp > aec->erl.average) {aec->erl.hicounter++;aec->erl.hisum += dtmp;aec->erl.himean = aec->erl.hisum / aec->erl.hicounter;}// A_NLPdtmp = 10 * (float)log10(aec->nearlevel.averagelevel /(2 * aec->linoutlevel.averagelevel) +1e-10f);// subtract noise powersuppressedEcho = 2 * (aec->linoutlevel.averagelevel -safety * aec->linoutlevel.minlevel);dtmp2 = 10 * (float)log10(echo / suppressedEcho + 1e-10f);aec->aNlp.instant = dtmp2;if (dtmp > aec->aNlp.max) {aec->aNlp.max = dtmp;}if (dtmp < aec->aNlp.min) {aec->aNlp.min = dtmp;}aec->aNlp.counter++;aec->aNlp.sum += dtmp;aec->aNlp.average = aec->aNlp.sum / aec->aNlp.counter;// 上均值if (dtmp > aec->aNlp.average) {aec->aNlp.hicounter++;aec->aNlp.hisum += dtmp;aec->aNlp.himean = aec->aNlp.hisum / aec->aNlp.hicounter;}// ERLE// subtract noise powersuppressedEcho = 2 * (aec->nlpoutlevel.averagelevel -safety * aec->nlpoutlevel.minlevel);dtmp = 10 * (float)log10(aec->nearlevel.averagelevel /(2 * aec->nlpoutlevel.averagelevel) +1e-10f);dtmp2 = 10 * (float)log10(echo / suppressedEcho + 1e-10f);dtmp = dtmp2;aec->erle.instant = dtmp;if (dtmp > aec->erle.max) {aec->erle.max = dtmp;}if (dtmp < aec->erle.min) {aec->erle.min = dtmp;}aec->erle.counter++;aec->erle.sum += dtmp;aec->erle.average = aec->erle.sum / aec->erle.counter;// Upper meanif (dtmp > aec->erle.average) {aec->erle.hicounter++;aec->erle.hisum += dtmp;aec->erle.himean = aec->erle.hisum / aec->erle.hicounter;}}aec->stateCounter = 0;}}//初始化指标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) {//我们没有新的延迟值数据。 即使-1是有效的|中位数| 在//从某种意义上说，我们允许使用负值，但实际上永远不会//因为| kMsPerBlock |的倍数而使用 将始终返回。//因此，我们使用-1在日志中指出延迟估算器为//无法估算延迟。self->delay_median = -1;self->delay_std = -1;self->fraction_poor_delays = -1;return;}// 中位数倒计时的起始值。delay_values = self->num_delay_values >> 1;// 获取自上次更新以来的延迟值的中位数。for (i = 0; i < kHistorySizeBlocks; i++) {delay_values -= self->delay_histogram[i];if (delay_values < 0) {median = i;break;}}// 提前考虑。self->delay_median = (median - lookahead) * kMsPerBlock;//计算L1范数，以中位数为中心矩。for (i = 0; i < kHistorySizeBlocks; i++) {l1_norm += abs(i - median) * self->delay_histogram[i];}self->delay_std = (int)((l1_norm + self->num_delay_values / 2) /self->num_delay_values) * kMsPerBlock;// 确定超出范围的延迟比例，即//负数（反因果系统）或大于AEC过滤器长度。{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 = (float)num_delays_out_of_bounds /self->num_delay_values;}// 重写 histogram.memset(self->delay_histogram, 0, sizeof(self->delay_histogram));self->num_delay_values = 0;return;}//时间频率static void TimeToFrequency(float time_data[PART_LEN2],float freq_data[2][PART_LEN1],int window) {int i = 0;// TODO(bjornv): Should we have a different function/wrapper for windowed FFT?if (window) {for (i = 0; i < PART_LEN; i++) {time_data[i] *= WebRtcAec_sqrtHanning[i];time_data[PART_LEN + i] *= WebRtcAec_sqrtHanning[PART_LEN - i];}}aec_rdft_forward_128(time_data);// Reorder.重新排序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];}}//无需系统延迟更新即可移动远读Ptrstatic int MoveFarReadPtrWithoutSystemDelayUpdate(AecCore* self, int elements) {WebRtc_MoveReadPtr(self->far_buf_windowed, elements);#ifdef WEBRTC_AEC_DEBUG_DUMPWebRtc_MoveReadPtr(self->far_time_buf, elements);#endifreturn WebRtc_MoveReadPtr(self->far_buf, elements);}//基于信号的延迟校正static int SignalBasedDelayCorrection(AecCore* self) {int delay_correction = 0;int last_delay = -2;assert(self != NULL);#if !defined(WEBRTC_ANDROID)//在桌面上，在| kDelayCorrectionStart |之后打开校正 框架。 这个//是为了让延迟估计有收敛的机会。 另外，如果//播放的音频音量很小（甚至静音），延迟估计可以返回//非常大的延迟，如果应用了AEC，则会中断AEC。if (self->frame_count < kDelayCorrectionStart) {return 0;}#endif// 1.检查非负延迟估计。 请注意，我们得到的估算值//延迟估计不会补偿超前。 因此，//否| last_delay | 是无效的。// 2.确认存在延迟更改。 此外，仅允许更改//如果延迟超出某个区域，则采用AEC滤波器长度//考虑在内。// TODO（bjornv）：研究是否可以删除非零延迟更改检查。// 3.仅当延迟估计质量超过时才允许延迟校正// | delay_quality_threshold |。// 4.最后，验证建议的| delay_correction | 是可行的//与远端缓冲区的大小进行比较。last_delay = WebRtc_last_delay(self->delay_estimator);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);//允许实际延迟，由| lower_bound |定义 和// | upper_bound |。 自适应回声消除滤波器目前// | num_partitions | （共64个样本）长。 如果延迟估计为负//或至少打开过滤器长度的3/4进行校正。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 = (int)WebRtc_available_read(self->far_buf);// 具有| shift_offset | 我们逐渐依赖延迟估算。 对于//正延迟，我们通过| shift_offset |减少校正 降低//有将AEC置于非因果状态的风险。 对于负面的延迟//我们依靠值直至舍入误差，因此补偿1//元素，以确保将延迟推入因果区域。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) {// 缓冲区中没有足够的数据来执行此移位。 因此，//我们不依赖延迟估计，并且什么也不做。delay_correction = 0;} else {self->previous_delay = last_delay;++self->delay_correction_count;}}}//更新| delay_quality_threshold | 一旦我们有第一次延迟//更正。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);}return delay_correction;}//NLP非线性处理过程：static void NonLinearProcessing(AecCore* aec,float* output,float* const* outputH) {float efw[2][PART_LEN1], xfw[2][PART_LEN1];complex_t comfortNoiseHband[PART_LEN1];float fft[PART_LEN2];float scale, dtmp;float nlpGainHband;//nlp增益子带int i;size_t j;/*计算相关性*/// 相干和非线性滤波器//conde:表示近端和误差信号的相关性，conde越大回声就越小//cohxd:远端与近端信号相关性，cohxd值越大回声就越大float cohde[PART_LEN1], cohxd[PART_LEN1];//hNlDeAvg 表示参考信号与mic接收信号的不相关性；hNlXdAvg 表示aec输出信号与mic接收信号的相关性。/*主要用于更新hNlXdAvg的最小值hNlXdAvgMin。数值0.75控制了该更新的频率，如果或者数值越大，表面hNlXdAvgMin的更新频率越快，对残留回声也会越敏感*/float hNlDeAvg, hNlXdAvg;float hNl[PART_LEN1];//首选子带大小float hNlPref[kPrefBandSize];float hNlFb = 0, hNlFbLow = 0;//prefBandQuant：首选子带数量const float prefBandQuant = 0.75f, prefBandQuantLow = 0.5f;const int prefBandSize = kPrefBandSize / aec->mult;const int minPrefBand = 4 / aec->mult;// 功率估计平滑系数。const float* min_overdrive = aec->extended_filter_enabled? kExtendedMinOverDrive: kNormalMinOverDrive;// Filter energyconst int delayEstInterval = 10 * aec->mult;float* xfw_ptr = NULL;aec->delayEstCtr++;if (aec->delayEstCtr == delayEstInterval) {aec->delayEstCtr = 0;}// 初始化H波段的舒适噪音memset(comfortNoiseHband, 0, sizeof(comfortNoiseHband));nlpGainHband = (float)0.0;dtmp = (float)0.0;// 我们应该至少在| far_buf |中存储至少一个元素。assert(WebRtc_available_read(aec->far_buf_windowed) > 0);// NLPWebRtc_ReadBuffer(aec->far_buf_windowed, (void**)&xfw_ptr, &xfw[0][0], 1);// TODO（bjornv）：研究是否可以重用| far_buf_windowed | 代替// | xfwBuf |。//远端缓冲远容量。memcpy(aec->xfwBuf, xfw_ptr, sizeof(float) * 2 * PART_LEN1);//自带相关性WebRtcAec_SubbandCoherence(aec, efw, xfw, fft, cohde, cohxd);hNlXdAvg = 0;for (i = minPrefBand; i < prefBandSize + minPrefBand; i++) {hNlXdAvg += cohxd[i];}hNlXdAvg /= prefBandSize;hNlXdAvg = 1 - hNlXdAvg;hNlDeAvg = 0;for (i = minPrefBand; i < prefBandSize + minPrefBand; i++) {hNlDeAvg += cohde[i];}hNlDeAvg /= prefBandSize;/*主要用于更新hNlXdAvg的最小值hNlXdAvgMin。数值0.75控制了该更新的频率，如果或者数值越大，表面hNlXdAvgMin的更新频率越快，对残留回声也会越敏感*/if (hNlXdAvg < 0.75f && hNlXdAvg < aec->hNlXdAvgMin) {aec->hNlXdAvgMin = hNlXdAvg;}if (hNlDeAvg > 0.98f && hNlXdAvg > 0.9f) {/*aec输出信号与mic接收信号相关性大，同时参考信号与mic接收信号的不相关性较大，说明此时只有近端信号，或者残留信号非常弱*/aec->stNearState = 1;//在只存在近端语音的情况下设置近端状态为1} else if (hNlDeAvg < 0.95f || hNlXdAvg < 0.8f) {/*aec输出信号与mic接收信号相关性较小，或者参考信号与mic接收信号的不相关性较小（相关性较大），说明此时存在残留回声需要抑制*/aec->stNearState = 0;//在存在远端回声则设置状态为0}if (aec->hNlXdAvgMin == 1) {aec->echoState = 0;aec->overDrive = min_overdrive[aec->nlp_mode];if (aec->stNearState == 1) {memcpy(hNl, cohde, sizeof(hNl));hNlFb = hNlDeAvg;hNlFbLow = hNlDeAvg;} else {for (i = 0; i < PART_LEN1; i++) {hNl[i] = 1 - cohxd[i];}hNlFb = hNlXdAvg;hNlFbLow = hNlXdAvg;}} else {if (aec->stNearState == 1) {aec->echoState = 0;memcpy(hNl, cohde, sizeof(hNl));hNlFb = hNlDeAvg;hNlFbLow = hNlDeAvg;} else {aec->echoState = 1;for (i = 0; i < PART_LEN1; i++) {hNl[i] = WEBRTC_SPL_MIN(cohde[i], 1 - cohxd[i]);}//从首选频段中选择顺序统计信息。// TODO：现在使用quicksort，但是选择算法可能是首选。memcpy(hNlPref, &hNl[minPrefBand], sizeof(float) * prefBandSize);qsort(hNlPref, prefBandSize, sizeof(float), CmpFloat);hNlFb = hNlPref[(int)floor(prefBandQuant * (prefBandSize - 1))];hNlFbLow = hNlPref[(int)floor(prefBandQuantLow * (prefBandSize - 1))];}}/*检测一段时间内是否出现了更小的hNlFbMin，hNlFbMin用来更新overd的抑制程度。数值0.6用来控制参数更新频率，该数值越大hNlFbMin更新越频繁，对于残留回声会越敏感*/// 跟踪本地滤波器最小值以确定抑制过载。if (hNlFbLow < 0.6f && hNlFbLow < aec->hNlFbLocalMin) {aec->hNlFbLocalMin = hNlFbLow;aec->hNlFbMin = hNlFbLow;aec->hNlNewMin = 1;aec->hNlMinCtr = 0;}/*以下两个参数以固定的步长更新，为的是hNlXdAvgMin与hNlFbMin不会陷入死锁状态无法更新。当然这里的步长因子也可以控制上述两个数值的更新频率，一般是步长因子越大更新越频繁*/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++;}/*hNlMinCtr == 2表明hNlFbMin只在当前帧更新，而下一帧不更新。也即，当前帧找到最小数值需要连续满足hnlMinCtr - 1帧，防止误触发*/if (aec->hNlMinCtr == 2) {aec->hNlNewMin = 0;aec->hNlMinCtr = 0;/*kTargetSupp[aec->nlp_mode]用来设置当前帧抑制多少dB*/aec->overDrive =WEBRTC_SPL_MAX(kTargetSupp[aec->nlp_mode] /((float)log(aec->hNlFbMin + 1e-10f) + 1e-10f),min_overdrive[aec->nlp_mode]);}//平滑过载。if (aec->overDrive < aec->overDriveSm) {aec->overDriveSm = 0.99f * aec->overDriveSm + 0.01f * aec->overDrive;} else {aec->overDriveSm = 0.9f * aec->overDriveSm + 0.1f * aec->overDrive;}WebRtcAec_OverdriveAndSuppress(aec, hNl, hNlFb, efw);// Add comfort noise.WebRtcAec_ComfortNoise(aec, efw, comfortNoiseHband, aec->noisePow, hNl);// TODO(bjornv): 研究在以下情况下如何考虑以下窗口//需要。if (aec->metricsMode == 1) {// 注意，我们在时域| eBuf |中将比例缩放为2。//另外，在转换前将时域信号加窗，//平均损失一半的能量。 我们先考虑仅在UpdateMetrics（）中缩放。UpdateLevel(&aec->nlpoutlevel, efw);}// 逆 error fft.fft[0] = efw[0][0];fft[1] = efw[0][PART_LEN];for (i = 1; i < PART_LEN; i++) {fft[2 * i] = efw[0][i];// Ooura fft要求更信号。fft[2 * i + 1] = -efw[1][i];}aec_rdft_inverse_128(fft);// 重叠并相加以获得输出。scale = 2.0f / PART_LEN2;for (i = 0; i < PART_LEN; i++) {fft[i] *= scale; // fft scalingfft[i] = fft[i] * WebRtcAec_sqrtHanning[i] + aec->outBuf[i];fft[PART_LEN + i] *= scale; // fft scalingaec->outBuf[i] = fft[PART_LEN + i] * WebRtcAec_sqrtHanning[PART_LEN - i];// 饱和输出以使其保持在允许范围内。output[i] = WEBRTC_SPL_SAT(WEBRTC_SPL_WORD16_MAX, fft[i], WEBRTC_SPL_WORD16_MIN);}// For H bandif (aec->num_bands > 1) {// H波段增益//低频段的平均nlp：频率频谱后半段的平均值//（4-> 8khz）GetHighbandGain(hNl, &nlpGainHband);// 逆舒适噪音if (flagHbandCn == 1) {fft[0] = comfortNoiseHband[0][0];fft[1] = comfortNoiseHband[PART_LEN][0];for (i = 1; i < PART_LEN; i++) {fft[2 * i] = comfortNoiseHband[i][0];fft[2 * i + 1] = comfortNoiseHband[i][1];}aec_rdft_inverse_128(fft);scale = 2.0f / PART_LEN2;}// 计算增益因子for (j = 0; j < aec->num_bands - 1; ++j) {for (i = 0; i < PART_LEN; i++) {dtmp = aec->dBufH[j][i];dtmp = dtmp * nlpGainHband; // 可变增益// 在Hband衰减的地方添加一些舒适噪音if (flagHbandCn == 1 && j == 0) {fft[i] *= scale; // fft scalingdtmp += cnScaleHband * fft[i];}// 饱和输出以使其保持在允许范围内。outputH[j][i] = WEBRTC_SPL_SAT(WEBRTC_SPL_WORD16_MAX, dtmp, WEBRTC_SPL_WORD16_MIN);}}}//将当前块复制到旧位置。memcpy(aec->dBuf, aec->dBuf + PART_LEN, sizeof(float) * PART_LEN);memcpy(aec->eBuf, aec->eBuf + PART_LEN, sizeof(float) * PART_LEN);// 将当前块复制到H波段的旧位置for (j = 0; j < aec->num_bands - 1; ++j) {memcpy(aec->dBufH[j], aec->dBufH[j] + PART_LEN, sizeof(float) * PART_LEN);}memmove(aec->xfwBuf + PART_LEN1,aec->xfwBuf,sizeof(aec->xfwBuf) - sizeof(complex_t) * PART_LEN1);}static void ProcessBlock(AecCore* aec) {size_t i;float y[PART_LEN], e[PART_LEN];float scale;float fft[PART_LEN2];float xf[2][PART_LEN1], yf[2][PART_LEN1], ef[2][PART_LEN1];float df[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};// 噪声估计常数。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 nearend[PART_LEN];float* nearend_ptr = NULL;float output[PART_LEN];float outputH[NUM_HIGH_BANDS_MAX][PART_LEN];float* outputH_ptr[NUM_HIGH_BANDS_MAX];for (i = 0; i < NUM_HIGH_BANDS_MAX; ++i) {outputH_ptr[i] = outputH[i];}float* xf_ptr = NULL;// 连接旧的和新的近端块。for (i = 0; i < aec->num_bands - 1; ++i) {WebRtc_ReadBuffer(aec->nearFrBufH[i],(void**)&nearend_ptr,nearend,PART_LEN);memcpy(aec->dBufH[i] + PART_LEN, nearend_ptr, sizeof(nearend));}WebRtc_ReadBuffer(aec->nearFrBuf, (void**)&nearend_ptr, nearend, PART_LEN);memcpy(aec->dBuf + PART_LEN, nearend_ptr, sizeof(nearend));// ---------- Ooura fft ----------#ifdef WEBRTC_AEC_DEBUG_DUMP{float farend[PART_LEN];float* farend_ptr = NULL;WebRtc_ReadBuffer(aec->far_time_buf, (void**)&farend_ptr, farend, 1);RTC_AEC_DEBUG_WAV_WRITE(aec->farFile, farend_ptr, PART_LEN);RTC_AEC_DEBUG_WAV_WRITE(aec->nearFile, nearend_ptr, PART_LEN);}#endif//我们应该至少在| far_buf |中存储至少一个元素。assert(WebRtc_available_read(aec->far_buf) > 0);WebRtc_ReadBuffer(aec->far_buf, (void**)&xf_ptr, &xf[0][0], 1);// Near fftmemcpy(fft, aec->dBuf, sizeof(float) * PART_LEN2);TimeToFrequency(fft, df, 0);// 功率平滑for (i = 0; i < PART_LEN1; i++) {far_spectrum = (xf_ptr[i] * xf_ptr[i]) +(xf_ptr[PART_LEN1 + i] * xf_ptr[PART_LEN1 + i]);aec->xPow[i] =gPow[0] * aec->xPow[i] + gPow[1] * aec->num_partitions * far_spectrum;// 计算绝对 spectraabs_far_spectrum[i] = sqrtf(far_spectrum);near_spectrum = df[0][i] * df[0][i] + df[1][i] * df[1][i];aec->dPow[i] = gPow[0] * aec->dPow[i] + gPow[1] * near_spectrum;//计算绝对 spectraabs_near_spectrum[i] = sqrtf(near_spectrum);}// E刺激噪音。 等待直到dPow更稳定。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;}}}// 从一开始就平稳地增加噪声功率，从零开始，//避免突然产生的舒适噪音。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;}// 用于记录的逐块延迟估计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) {// 更新延迟估计缓冲区.aec->delay_histogram[delay_estimate]++;aec->num_delay_values++;}if (aec->delay_metrics_delivered == 1 &&aec->num_delay_values >= kDelayMetricsAggregationWindow) {UpdateDelayMetrics(aec);}}}//更新xfBuf块的位置。aec->xfBufBlockPos--;if (aec->xfBufBlockPos == -1) {aec->xfBufBlockPos = aec->num_partitions - 1;}// Buffer xfmemcpy(aec->xfBuf[0] + aec->xfBufBlockPos * PART_LEN1,xf_ptr,sizeof(float) * PART_LEN1);memcpy(aec->xfBuf[1] + aec->xfBufBlockPos * PART_LEN1,&xf_ptr[PART_LEN1],sizeof(float) * PART_LEN1);memset(yf, 0, sizeof(yf));// Filter farWebRtcAec_FilterFar(aec, yf);//逆fft以获得回波估计和误差。fft[0] = yf[0][0];fft[1] = yf[0][PART_LEN];for (i = 1; i < PART_LEN; i++) {fft[2 * i] = yf[0][i];fft[2 * i + 1] = yf[1][i];}aec_rdft_inverse_128(fft);scale = 2.0f / PART_LEN2;for (i = 0; i < PART_LEN; i++) {y[i] = fft[PART_LEN + i] * scale; // fft scaling}for (i = 0; i < PART_LEN; i++) {e[i] = nearend_ptr[i] - y[i];}// Error fftmemcpy(aec->eBuf + PART_LEN, e, sizeof(float) * PART_LEN);memset(fft, 0, sizeof(float) * PART_LEN);memcpy(fft + PART_LEN, e, sizeof(float) * PART_LEN);// TODO(bjornv): Change to use TimeToFrequency().aec_rdft_forward_128(fft);ef[1][0] = 0;ef[1][PART_LEN] = 0;ef[0][0] = fft[0];ef[0][PART_LEN] = fft[1];for (i = 1; i < PART_LEN; i++) {ef[0][i] = fft[2 * i];ef[1][i] = fft[2 * i + 1];}RTC_AEC_DEBUG_RAW_WRITE(aec->e_fft_file,&ef[0][0],sizeof(ef[0][0]) * PART_LEN1 * 2);if (aec->metricsMode == 1) {//请注意，在转换之前，ftf中的前PART_LEN个样本是//零。 因此，在UpdateLevel（）中缩放为2不应为//执行。 该缩放是在UpdateMetrics（）中进行的。UpdateLevel(&aec->linoutlevel, ef);}// 与远功率成反比地缩放误差信号。WebRtcAec_ScaleErrorSignal(aec, ef);WebRtcAec_FilterAdaptation(aec, fft, ef);NonLinearProcessing(aec, output, outputH_ptr);if (aec->metricsMode == 1) {//更新功率水平和回声指标UpdateLevel(&aec->farlevel, (float(*)[PART_LEN1])xf_ptr);UpdateLevel(&aec->nearlevel, df);UpdateMetrics(aec);}// 存储输出块。WebRtc_WriteBuffer(aec->outFrBuf, output, PART_LEN);// 对于高频段for (i = 0; i < aec->num_bands - 1; ++i) {WebRtc_WriteBuffer(aec->outFrBufH[i], outputH[i], PART_LEN);}RTC_AEC_DEBUG_WAV_WRITE(aec->outLinearFile, e, PART_LEN);RTC_AEC_DEBUG_WAV_WRITE(aec->outFile, output, PART_LEN);}AecCore* WebRtcAec_CreateAec() {int i;AecCore* aec = malloc(sizeof(AecCore));if (!aec) {return NULL;}aec->nearFrBuf = WebRtc_CreateBuffer(FRAME_LEN + PART_LEN, sizeof(float));if (!aec->nearFrBuf) {WebRtcAec_FreeAec(aec);return NULL;}aec->outFrBuf = WebRtc_CreateBuffer(FRAME_LEN + PART_LEN, sizeof(float));if (!aec->outFrBuf) {WebRtcAec_FreeAec(aec);return NULL;}for (i = 0; i < NUM_HIGH_BANDS_MAX; ++i) {aec->nearFrBufH[i] = WebRtc_CreateBuffer(FRAME_LEN + PART_LEN,sizeof(float));if (!aec->nearFrBufH[i]) {WebRtcAec_FreeAec(aec);return NULL;}aec->outFrBufH[i] = WebRtc_CreateBuffer(FRAME_LEN + PART_LEN,sizeof(float));if (!aec->outFrBufH[i]) {WebRtcAec_FreeAec(aec);return NULL;}}// Create far-end buffers.aec->far_buf =WebRtc_CreateBuffer(kBufSizePartitions, sizeof(float) * 2 * PART_LEN1);if (!aec->far_buf) {WebRtcAec_FreeAec(aec);return NULL;}aec->far_buf_windowed =WebRtc_CreateBuffer(kBufSizePartitions, sizeof(float) * 2 * PART_LEN1);if (!aec->far_buf_windowed) {WebRtcAec_FreeAec(aec);return NULL;}#ifdef WEBRTC_AEC_DEBUG_DUMPaec->instance_index = webrtc_aec_instance_count;aec->far_time_buf =WebRtc_CreateBuffer(kBufSizePartitions, sizeof(float) * PART_LEN);if (!aec->far_time_buf) {WebRtcAec_FreeAec(aec);return NULL;}aec->farFile = aec->nearFile = aec->outFile = aec->outLinearFile = NULL;aec->debug_dump_count = 0;#endifaec->delay_estimator_farend =WebRtc_CreateDelayEstimatorFarend(PART_LEN1, kHistorySizeBlocks);if (aec->delay_estimator_farend == NULL) {WebRtcAec_FreeAec(aec);return NULL;}//我们创建与最大提前量相同的delay_estimator//由于对称性原因，延迟历史记录大小（kHistorySizeBlocks）。aec->delay_estimator = WebRtc_CreateDelayEstimator(aec->delay_estimator_farend, kHistorySizeBlocks);if (aec->delay_estimator == NULL) {WebRtcAec_FreeAec(aec);return NULL;}#ifdef WEBRTC_ANDROIDaec->delay_agnostic_enabled = 1; //默认启用DA-AEC。// DA-AEC假设系统从一开始就是因果关系，并且会自我调整//需要移位时的前瞻。WebRtc_set_lookahead(aec->delay_estimator, 0);#elseaec->delay_agnostic_enabled = 0;WebRtc_set_lookahead(aec->delay_estimator, kLookaheadBlocks);#endifaec->extended_filter_enabled = 0;// 装配优化WebRtcAec_FilterFar = FilterFar;WebRtcAec_ScaleErrorSignal = ScaleErrorSignal;WebRtcAec_FilterAdaptation = FilterAdaptation;WebRtcAec_OverdriveAndSuppress = OverdriveAndSuppress;WebRtcAec_ComfortNoise = ComfortNoise;WebRtcAec_SubbandCoherence = SubbandCoherence;#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();#elif defined(WEBRTC_DETECT_NEON)if ((WebRtc_GetCPUFeaturesARM() & kCPUFeatureNEON) != 0) {WebRtcAec_InitAec_neon();}#endifaec_rdft_init();return aec;}void WebRtcAec_FreeAec(AecCore* aec) {int i;if (aec == NULL) {return;}WebRtc_FreeBuffer(aec->nearFrBuf);WebRtc_FreeBuffer(aec->outFrBuf);for (i = 0; i < NUM_HIGH_BANDS_MAX; ++i) {WebRtc_FreeBuffer(aec->nearFrBufH[i]);WebRtc_FreeBuffer(aec->outFrBufH[i]);}WebRtc_FreeBuffer(aec->far_buf);WebRtc_FreeBuffer(aec->far_buf_windowed);#ifdef WEBRTC_AEC_DEBUG_DUMPWebRtc_FreeBuffer(aec->far_time_buf);#endifRTC_AEC_DEBUG_WAV_CLOSE(aec->farFile);RTC_AEC_DEBUG_WAV_CLOSE(aec->nearFile);RTC_AEC_DEBUG_WAV_CLOSE(aec->outFile);RTC_AEC_DEBUG_WAV_CLOSE(aec->outLinearFile);RTC_AEC_DEBUG_RAW_CLOSE(aec->e_fft_file);WebRtc_FreeDelayEstimator(aec->delay_estimator);WebRtc_FreeDelayEstimatorFarend(aec->delay_estimator_farend);free(aec);}int WebRtcAec_InitAec(AecCore* aec, int sampFreq) {int i;aec->sampFreq = sampFreq;if (sampFreq == 8000) {aec->normal_mu = 0.6f;aec->normal_error_threshold = 2e-6f;aec->num_bands = 1;} else {aec->normal_mu = 0.5f;aec->normal_error_threshold = 1.5e-6f;aec->num_bands = (size_t)(sampFreq / 16000);}WebRtc_InitBuffer(aec->nearFrBuf);WebRtc_InitBuffer(aec->outFrBuf);for (i = 0; i < NUM_HIGH_BANDS_MAX; ++i) {WebRtc_InitBuffer(aec->nearFrBufH[i]);WebRtc_InitBuffer(aec->outFrBufH[i]);}// 初始化 far-end buffers.WebRtc_InitBuffer(aec->far_buf);WebRtc_InitBuffer(aec->far_buf_windowed);#ifdef WEBRTC_AEC_DEBUG_DUMPWebRtc_InitBuffer(aec->far_time_buf);{int process_rate = sampFreq > 16000 ? 16000 : sampFreq;RTC_AEC_DEBUG_WAV_REOPEN("aec_far", aec->instance_index,aec->debug_dump_count, process_rate,&aec->farFile );RTC_AEC_DEBUG_WAV_REOPEN("aec_near", aec->instance_index,aec->debug_dump_count, process_rate,&aec->nearFile);RTC_AEC_DEBUG_WAV_REOPEN("aec_out", aec->instance_index,aec->debug_dump_count, process_rate,&aec->outFile );RTC_AEC_DEBUG_WAV_REOPEN("aec_out_linear", aec->instance_index,aec->debug_dump_count, process_rate,&aec->outLinearFile);}RTC_AEC_DEBUG_RAW_OPEN("aec_e_fft",aec->debug_dump_count,&aec->e_fft_file);++aec->debug_dump_count;#endifaec->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->signal_delay_correction = 0;aec->previous_delay = -2; // (-2): Uninitialized.aec->delay_correction_count = 0;aec->shift_offset = kInitialShiftOffset;aec->delay_quality_threshold = kDelayQualityThresholdMin;aec->num_partitions = kNormalNumPartitions;//使用滤波器长度更新延迟估算器。 我们用一半| num_partitions | 考虑回声路径。 实际上我们说//回声的持续时间最大为一半| num_partitions |，而不是//是，但仅作为粗略的度量。WebRtc_set_allowed_offset(aec->delay_estimator, aec->num_partitions / 2);//TODO（bjornv）：我目前对启用代码进行了硬编码。 一旦建立// AECM没有性能下降，将启用robust_validation//一直删除，并将其打开/关闭的API将被删除。 因此，删除//这行。WebRtc_enable_robust_validation(aec->delay_estimator, 1);aec->frame_count = 0;// 默认目标抑制模式。aec->nlp_mode = 1;// 采样倍频器w.r.t. 8 kHz。//如果有多个频段，我们以16 kHz的频率处理较低频段，因此//乘数始终为2。if (aec->num_bands > 1) {aec->mult = 2;} else {aec->mult = (short)aec->sampFreq / 8000;}aec->farBufWritePos = 0;aec->farBufReadPos = 0;aec->inSamples = 0;aec->outSamples = 0;aec->knownDelay = 0;// Initialize buffersmemset(aec->dBuf, 0, sizeof(aec->dBuf));memset(aec->eBuf, 0, sizeof(aec->eBuf));// For H bandsfor (i = 0; i < NUM_HIGH_BANDS_MAX; ++i) {memset(aec->dBufH[i], 0, sizeof(aec->dBufH[i]));}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;// 初始化舒适噪音for (i = 0; i < PART_LEN1; i++) {aec->dMinPow[i] = 1.0e6f;}//保存写入的最后一个块aec->xfBufBlockPos = 0;// TODO: 研究对这些初始化的需求。 删除它们不会//完全改变输出，并产生0.4％的整体加速比。memset(aec->xfBuf, 0, sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);memset(aec->wfBuf, 0, sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);memset(aec->sde, 0, sizeof(complex_t) * PART_LEN1);memset(aec->sxd, 0, sizeof(complex_t) * PART_LEN1);memset(aec->xfwBuf, 0, sizeof(complex_t) * kExtendedNumPartitions * PART_LEN1);memset(aec->se, 0, sizeof(float) * PART_LEN1);// 为了防止第一个程序段中的数值不稳定。for (i = 0; i < PART_LEN1; i++) {aec->sd[i] = 1;}for (i = 0; i < PART_LEN1; i++) {aec->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->overDriveSm = 2;aec->delayIdx = 0;aec->stNearState = 0;aec->echoState = 0;aec->divergeState = 0;aec->seed = 777;aec->delayEstCtr = 0;// 默认禁用指标aec->metricsMode = 0;InitMetrics(aec);return 0;}void WebRtcAec_BufferFarendPartition(AecCore* aec, const float* farend) {float fft[PART_LEN2];float xf[2][PART_LEN1];// 检查缓冲区是否已满，并在这种情况下刷新最早的数据。if (WebRtc_available_write(aec->far_buf) < 1) {WebRtcAec_MoveFarReadPtr(aec, 1);}// 无需窗口即可将远端分区转换到频域。memcpy(fft, farend, sizeof(float) * PART_LEN2);TimeToFrequency(fft, xf, 0);WebRtc_WriteBuffer(aec->far_buf, &xf[0][0], 1);//通过加窗将远端分区转换到频域。memcpy(fft, farend, sizeof(float) * PART_LEN2);TimeToFrequency(fft, xf, 1);WebRtc_WriteBuffer(aec->far_buf_windowed, &xf[0][0], 1);}int WebRtcAec_MoveFarReadPtr(AecCore* aec, int elements) {int elements_moved = MoveFarReadPtrWithoutSystemDelayUpdate(aec, elements);aec->system_delay -= elements_moved * PART_LEN;return elements_moved;}void WebRtcAec_ProcessFrames(AecCore* aec,const float* const* nearend,size_t num_bands,size_t num_samples,int knownDelay,float* const* out) {size_t i, j;int out_elements = 0;aec->frame_count++;//对于每个帧，过程如下：// 1）如果system_delay指示太小而无法处理//帧，我们用足够的数据填充缓冲区10毫秒。// 2 a）通过移动读取指针将缓冲区调整为系统延迟。// b）如果我们检测到不良的AEC，则应用基于信号的延迟校正//性能。// 3）TODO（bjornv）：研究是否需要添加以下内容：//如果由于缓冲区大小限制而无法移动读取指针//刷新/填充缓冲区。// 4）处理尽可能多的分区。// 5）更新| system_delay |关于FRAME_LEN的整个帧//样本。即使我们还有待处理的数据（我们与//分区），我们考虑更新整个框架，因为//我们在audio_processing中输入和输出的数据量。// 6）更新输出。// AEC内置了两种不同的延迟估计算法。//首先依赖于用户的延迟输入值和//移位的缓冲元素由| knownDelay |控制。此延迟将//猜测要转移多少远端缓冲区才能与之对齐//近端信号。另一种延迟估算算法使用//远端和近端信号以查找它们之间的偏移。这个//（称为“信号延迟”）然后用于微调对齐方式，或者//简单地补偿基于系统的错误。//请注意，这两种算法是独立运行的。目前，我们只//允许打开一种算法。assert(aec->num_bands == num_bands);for (j = 0; j < num_samples; j+= FRAME_LEN) {// TODO（bjornv）：将近端缓冲区处理更改为与//远端，即具有near_pre_buf。//缓冲近端帧。WebRtc_WriteBuffer(aec->nearFrBuf, &nearend[0][j], FRAME_LEN);// For H bandfor (i = 1; i < num_bands; ++i) {WebRtc_WriteBuffer(aec->nearFrBufH[i - 1], &nearend[i][j], FRAME_LEN);}//1）最多我们在10毫秒内处理| aec-> mult | +1分区。 确保我们//通过填充缓冲区（如果// | system_delay | 表示其他。if (aec->system_delay < FRAME_LEN) {// We don't have enough data so we rewind 10 ms.WebRtcAec_MoveFarReadPtr(aec, -(aec->mult + 1));}if (!aec->delay_agnostic_enabled) {// 2 a）补偿系统延迟的可能变化。// TODO（bjornv）：研究如何舍入延迟差；//现在，我们知道传入的| knownDelay | 被低估了//小于| aec-> knownDelay |。 因此，我们将（-32）舍入为//方向。 另一方面，我们没有这种情况，但是//可能会冲洗一个分区太少。 这可能会导致非因果关系，//应该对其进行调查。 也许允许非对称//取整，例如-16。int move_elements = (aec->knownDelay - knownDelay - 32) / PART_LEN;int moved_elements =MoveFarReadPtrWithoutSystemDelayUpdate(aec, move_elements);aec->knownDelay -= moved_elements * PART_LEN;} else {//2 b）应用基于信号的延迟校正。int move_elements = SignalBasedDelayCorrection（aec）;int moved_elements =MoveFarReadPtrWithoutSystemDelayUpdate(aec, move_elements);int far_near_buffer_diff = WebRtc_available_read(aec->far_buf) -WebRtc_available_read(aec->nearFrBuf) / PART_LEN;WebRtc_SoftResetDelayEstimator(aec->delay_estimator, moved_elements);WebRtc_SoftResetDelayEstimatorFarend(aec->delay_estimator_farend,moved_elements);aec->signal_delay_correction += moved_elements;// 如果仅依靠报告的系统延迟值，则此处的缓冲区不足//永远不会发生，因为我们已经在上面的1）中进行了处理。 在这里，我们//应用基于信号的延迟校正，因此最终可以//缓冲区欠载，因为延迟估计可能是错误的。 因此，我们//如果需要，用足够的元素填充缓冲区。if (far_near_buffer_diff < 0) {WebRtcAec_MoveFarReadPtr(aec, far_near_buffer_diff);}}//4）处理尽可能多的块。while (WebRtc_available_read(aec->nearFrBuf) >= PART_LEN) {ProcessBlock(aec);}// 5）更新整个帧的系统延迟。aec->system_delay -= FRAME_LEN;//6）更新输出帧。//如果输出少于一帧，则填充out缓冲区。//这只应发生在第一帧。out_elements = (int)WebRtc_available_read(aec->outFrBuf);if (out_elements < FRAME_LEN) {WebRtc_MoveReadPtr(aec->outFrBuf, out_elements - FRAME_LEN);for (i = 0; i < num_bands - 1; ++i) {WebRtc_MoveReadPtr(aec->outFrBufH[i], out_elements - FRAME_LEN);}}// 获取输出帧。WebRtc_ReadBuffer(aec->outFrBuf, NULL, &out[0][j], FRAME_LEN);//适用于H波段。for (i = 1; i < num_bands; ++i) {WebRtc_ReadBuffer(aec->outFrBufH[i - 1], NULL, &out[i][j], FRAME_LEN);}}}int WebRtcAec_GetDelayMetricsCore(AecCore* self, int* median, int* std,float* fraction_poor_delays) {assert(self != NULL);assert(median != NULL);assert(std != NULL);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) {assert(erl != NULL);assert(erle != NULL);assert(a_nlp != NULL);*erl = self->erl;*erle = self->erle;*a_nlp = self->aNlp;}#ifdef WEBRTC_AEC_DEBUG_DUMPvoid* WebRtcAec_far_time_buf(AecCore* self) {return self->far_time_buf; }#endifvoid WebRtcAec_SetConfigCore(AecCore* self,int nlp_mode,int metrics_mode,int delay_logging) {assert(nlp_mode >= 0 && nlp_mode < 3);self->nlp_mode = nlp_mode;self->metricsMode = metrics_mode;if (self->metricsMode) {InitMetrics(self);}// 如果延迟日志记录是明确设置的或与延迟无关的，请打开//启用AEC（需要延迟估计）。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_extended_filter(AecCore* self, int enable) {self->extended_filter_enabled = enable;self->num_partitions = enable ? kExtendedNumPartitions : kNormalNumPartitions;// 用滤波器长度更新延迟估计器。 有关详细信息，请参见InitAEC（）。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) {assert(delay >= 0);self->system_delay = delay;}

由于webrtc回声消除部分的算法已经更新到AEC3了，想着先把AEC部分看明白，想获取webrtc的modules的完整代码可以在下边链接找：

github链接

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