Adjust speech probability in NS when echo
The average speech probability for the higher band is multiplied by the quotient of the process and analyze powers, to avoid thinking that suppressed echo is speech. In order to do this both magnitudes, alanyze and process, needed to be stored. This also was used to calculate different previous STSA estimates for analyze and process.
This CL was tested on two long team member recordings (bjornv and kwiberg) and the noisiest (5) recordings from the QA set.
BUG=webrtc:3763
R=andrew@webrtc.org, bjornv@webrtc.org
Review URL: https://webrtc-codereview.appspot.com/23799004
git-svn-id: http://webrtc.googlecode.com/svn/trunk@7437 4adac7df-926f-26a2-2b94-8c16560cd09d
diff --git a/data/audio_processing/output_data_float.pb b/data/audio_processing/output_data_float.pb
index 28ef817..79619e7 100644
--- a/data/audio_processing/output_data_float.pb
+++ b/data/audio_processing/output_data_float.pb
Binary files differ
diff --git a/webrtc/modules/audio_processing/ns/ns_core.c b/webrtc/modules/audio_processing/ns/ns_core.c
index 5d367ee..2c7c29d 100644
--- a/webrtc/modules/audio_processing/ns/ns_core.c
+++ b/webrtc/modules/audio_processing/ns/ns_core.c
@@ -21,23 +21,23 @@
// Set Feature Extraction Parameters
void WebRtcNs_set_feature_extraction_parameters(NSinst_t* inst) {
// bin size of histogram
- inst->featureExtractionParams.binSizeLrt = (float)0.1;
- inst->featureExtractionParams.binSizeSpecFlat = (float)0.05;
- inst->featureExtractionParams.binSizeSpecDiff = (float)0.1;
+ inst->featureExtractionParams.binSizeLrt = 0.1f;
+ inst->featureExtractionParams.binSizeSpecFlat = 0.05f;
+ inst->featureExtractionParams.binSizeSpecDiff = 0.1f;
// range of histogram over which lrt threshold is computed
- inst->featureExtractionParams.rangeAvgHistLrt = (float)1.0;
+ inst->featureExtractionParams.rangeAvgHistLrt = 1.f;
// scale parameters: multiply dominant peaks of the histograms by scale factor
// to obtain thresholds for prior model
inst->featureExtractionParams.factor1ModelPars =
- (float)1.20; // for lrt and spectral diff
+ 1.2f; // for lrt and spectral diff
inst->featureExtractionParams.factor2ModelPars =
- (float)0.9; // for spectral_flatness:
+ 0.9f; // for spectral_flatness:
// used when noise is flatter than speech
// peak limit for spectral flatness (varies between 0 and 1)
- inst->featureExtractionParams.thresPosSpecFlat = (float)0.6;
+ inst->featureExtractionParams.thresPosSpecFlat = 0.6f;
// limit on spacing of two highest peaks in histogram: spacing determined by
// bin size
@@ -47,21 +47,21 @@
2 * inst->featureExtractionParams.binSizeSpecDiff;
// limit on relevance of second peak:
- inst->featureExtractionParams.limitPeakWeightsSpecFlat = (float)0.5;
- inst->featureExtractionParams.limitPeakWeightsSpecDiff = (float)0.5;
+ inst->featureExtractionParams.limitPeakWeightsSpecFlat = 0.5f;
+ inst->featureExtractionParams.limitPeakWeightsSpecDiff = 0.5f;
// fluctuation limit of lrt feature
- inst->featureExtractionParams.thresFluctLrt = (float)0.05;
+ inst->featureExtractionParams.thresFluctLrt = 0.05f;
// limit on the max and min values for the feature thresholds
- inst->featureExtractionParams.maxLrt = (float)1.0;
- inst->featureExtractionParams.minLrt = (float)0.20;
+ inst->featureExtractionParams.maxLrt = 1.f;
+ inst->featureExtractionParams.minLrt = 0.2f;
- inst->featureExtractionParams.maxSpecFlat = (float)0.95;
- inst->featureExtractionParams.minSpecFlat = (float)0.10;
+ inst->featureExtractionParams.maxSpecFlat = 0.95f;
+ inst->featureExtractionParams.minSpecFlat = 0.1f;
- inst->featureExtractionParams.maxSpecDiff = (float)1.0;
- inst->featureExtractionParams.minSpecDiff = (float)0.16;
+ inst->featureExtractionParams.maxSpecDiff = 1.f;
+ inst->featureExtractionParams.minSpecDiff = 0.16f;
// criteria of weight of histogram peak to accept/reject feature
inst->featureExtractionParams.thresWeightSpecFlat =
@@ -120,8 +120,8 @@
// for quantile noise estimation
memset(inst->quantile, 0, sizeof(float) * HALF_ANAL_BLOCKL);
for (i = 0; i < SIMULT * HALF_ANAL_BLOCKL; i++) {
- inst->lquantile[i] = (float)8.0;
- inst->density[i] = (float)0.3;
+ inst->lquantile[i] = 8.f;
+ inst->density[i] = 0.3f;
}
for (i = 0; i < SIMULT; i++) {
@@ -133,61 +133,65 @@
// Wiener filter initialization
for (i = 0; i < HALF_ANAL_BLOCKL; i++) {
- inst->smooth[i] = (float)1.0;
+ inst->smooth[i] = 1.f;
}
// Set the aggressiveness: default
inst->aggrMode = 0;
// initialize variables for new method
- inst->priorSpeechProb = (float)0.5; // prior prob for speech/noise
+ inst->priorSpeechProb = 0.5f; // prior prob for speech/noise
+ // previous analyze mag spectrum
+ memset(inst->magnPrevAnalyze, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // previous process mag spectrum
+ memset(inst->magnPrevProcess, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // current noise-spectrum
+ memset(inst->noise, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // previous noise-spectrum
+ memset(inst->noisePrev, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // conservative noise spectrum estimate
+ memset(inst->magnAvgPause, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // for estimation of HB in second pass
+ memset(inst->speechProb, 0, sizeof(float) * HALF_ANAL_BLOCKL);
+ // initial average mag spectrum
+ memset(inst->initMagnEst, 0, sizeof(float) * HALF_ANAL_BLOCKL);
for (i = 0; i < HALF_ANAL_BLOCKL; i++) {
- inst->magnPrev[i] = (float)0.0; // previous mag spectrum
- inst->noisePrev[i] = (float)0.0; // previous noise-spectrum
inst->logLrtTimeAvg[i] =
LRT_FEATURE_THR; // smooth LR ratio (same as threshold)
- inst->magnAvgPause[i] = (float)0.0; // conservative noise spectrum estimate
- inst->speechProb[i] = (float)0.0; // for estimation of HB in second pass
- inst->initMagnEst[i] = (float)0.0; // initial average mag spectrum
}
// feature quantities
inst->featureData[0] =
SF_FEATURE_THR; // spectral flatness (start on threshold)
- inst->featureData[1] =
- (float)0.0; // spectral entropy: not used in this version
- inst->featureData[2] =
- (float)0.0; // spectral variance: not used in this version
+ inst->featureData[1] = 0.f; // spectral entropy: not used in this version
+ inst->featureData[2] = 0.f; // spectral variance: not used in this version
inst->featureData[3] =
LRT_FEATURE_THR; // average lrt factor (start on threshold)
inst->featureData[4] =
SF_FEATURE_THR; // spectral template diff (start on threshold)
- inst->featureData[5] = (float)0.0; // normalization for spectral-diff
+ inst->featureData[5] = 0.f; // normalization for spectral-diff
inst->featureData[6] =
- (float)0.0; // window time-average of input magnitude spectrum
+ 0.f; // window time-average of input magnitude spectrum
// histogram quantities: used to estimate/update thresholds for features
- for (i = 0; i < HIST_PAR_EST; i++) {
- inst->histLrt[i] = 0;
- inst->histSpecFlat[i] = 0;
- inst->histSpecDiff[i] = 0;
- }
+ memset(inst->histLrt, 0, sizeof(int) * HIST_PAR_EST);
+ memset(inst->histSpecFlat, 0, sizeof(int) * HIST_PAR_EST);
+ memset(inst->histSpecDiff, 0, sizeof(int) * HIST_PAR_EST);
+
inst->blockInd = -1; // frame counter
inst->priorModelPars[0] =
- LRT_FEATURE_THR; // default threshold for lrt feature
- inst->priorModelPars[1] = (float)0.5; // threshold for spectral flatness:
+ LRT_FEATURE_THR; // default threshold for lrt feature
+ inst->priorModelPars[1] = 0.5f; // threshold for spectral flatness:
// determined on-line
- inst->priorModelPars[2] = (float)1.0; // sgn_map par for spectral measure:
+ inst->priorModelPars[2] = 1.f; // sgn_map par for spectral measure:
// 1 for flatness measure
- inst->priorModelPars[3] =
- (float)0.5; // threshold for template-difference feature:
+ inst->priorModelPars[3] = 0.5f; // threshold for template-difference feature:
// determined on-line
- inst->priorModelPars[4] =
- (float)1.0; // default weighting parameter for lrt feature
- inst->priorModelPars[5] = (float)0.0; // default weighting parameter for
+ inst->priorModelPars[4] = 1.f; // default weighting parameter for lrt feature
+ inst->priorModelPars[5] = 0.f; // default weighting parameter for
// spectral flatness feature
- inst->priorModelPars[6] = (float)0.0; // default weighting parameter for
+ inst->priorModelPars[6] = 0.f; // default weighting parameter for
// spectral difference feature
inst->modelUpdatePars[0] = 2; // update flag for parameters:
@@ -221,23 +225,23 @@
inst->aggrMode = mode;
if (mode == 0) {
- inst->overdrive = (float)1.0;
- inst->denoiseBound = (float)0.5;
+ inst->overdrive = 1.f;
+ inst->denoiseBound = 0.5f;
inst->gainmap = 0;
} else if (mode == 1) {
- // inst->overdrive = (float)1.25;
- inst->overdrive = (float)1.0;
- inst->denoiseBound = (float)0.25;
+ // inst->overdrive = 1.25f;
+ inst->overdrive = 1.f;
+ inst->denoiseBound = 0.25f;
inst->gainmap = 1;
} else if (mode == 2) {
- // inst->overdrive = (float)1.25;
- inst->overdrive = (float)1.1;
- inst->denoiseBound = (float)0.125;
+ // inst->overdrive = 1.25f;
+ inst->overdrive = 1.1f;
+ inst->denoiseBound = 0.125f;
inst->gainmap = 1;
} else if (mode == 3) {
- // inst->overdrive = (float)1.30;
- inst->overdrive = (float)1.25;
- inst->denoiseBound = (float)0.09;
+ // inst->overdrive = 1.3f;
+ inst->overdrive = 1.25f;
+ inst->denoiseBound = 0.09f;
inst->gainmap = 1;
}
return 0;
@@ -264,7 +268,7 @@
for (i = 0; i < inst->magnLen; i++) {
// compute delta
if (inst->density[offset + i] > 1.0) {
- delta = FACTOR * (float)1.0 / inst->density[offset + i];
+ delta = FACTOR * 1.f / inst->density[offset + i];
} else {
delta = FACTOR;
}
@@ -275,14 +279,14 @@
QUANTILE * delta / (float)(inst->counter[s] + 1);
} else {
inst->lquantile[offset + i] -=
- ((float)1.0 - QUANTILE) * delta / (float)(inst->counter[s] + 1);
+ (1.f - QUANTILE) * delta / (float)(inst->counter[s] + 1);
}
// update density estimate
if (fabs(lmagn[i] - inst->lquantile[offset + i]) < WIDTH) {
inst->density[offset + i] =
((float)inst->counter[s] * inst->density[offset + i] +
- (float)1.0 / ((float)2.0 * WIDTH)) /
+ 1.f / (2.f * WIDTH)) /
(float)(inst->counter[s] + 1);
}
} // end loop over magnitude spectrum
@@ -371,8 +375,7 @@
avgSquareHistLrt = 0.0;
numHistLrt = 0;
for (i = 0; i < HIST_PAR_EST; i++) {
- binMid =
- ((float)i + (float)0.5) * inst->featureExtractionParams.binSizeLrt;
+ binMid = ((float)i + 0.5f) * inst->featureExtractionParams.binSizeLrt;
if (binMid <= inst->featureExtractionParams.rangeAvgHistLrt) {
avgHistLrt += inst->histLrt[i] * binMid;
numHistLrt += inst->histLrt[i];
@@ -414,8 +417,8 @@
// peaks for flatness
for (i = 0; i < HIST_PAR_EST; i++) {
- binMid = ((float)i + (float)0.5) *
- inst->featureExtractionParams.binSizeSpecFlat;
+ binMid =
+ (i + 0.5f) * inst->featureExtractionParams.binSizeSpecFlat;
if (inst->histSpecFlat[i] > maxPeak1) {
// Found new "first" peak
maxPeak2 = maxPeak1;
@@ -442,8 +445,8 @@
weightPeak2SpecDiff = 0;
// peaks for spectral difference
for (i = 0; i < HIST_PAR_EST; i++) {
- binMid = ((float)i + (float)0.5) *
- inst->featureExtractionParams.binSizeSpecDiff;
+ binMid =
+ ((float)i + 0.5f) * inst->featureExtractionParams.binSizeSpecDiff;
if (inst->histSpecDiff[i] > maxPeak1) {
// Found new "first" peak
maxPeak2 = maxPeak1;
@@ -470,7 +473,7 @@
inst->featureExtractionParams.limitPeakWeightsSpecFlat *
weightPeak1SpecFlat)) {
weightPeak1SpecFlat += weightPeak2SpecFlat;
- posPeak1SpecFlat = (float)0.5 * (posPeak1SpecFlat + posPeak2SpecFlat);
+ posPeak1SpecFlat = 0.5f * (posPeak1SpecFlat + posPeak2SpecFlat);
}
// reject if weight of peaks is not large enough, or peak value too small
if (weightPeak1SpecFlat <
@@ -502,7 +505,7 @@
inst->featureExtractionParams.limitPeakWeightsSpecDiff *
weightPeak1SpecDiff)) {
weightPeak1SpecDiff += weightPeak2SpecDiff;
- posPeak1SpecDiff = (float)0.5 * (posPeak1SpecDiff + posPeak2SpecDiff);
+ posPeak1SpecDiff = 0.5f * (posPeak1SpecDiff + posPeak2SpecDiff);
}
// get the threshold value
inst->priorModelPars[3] =
@@ -532,7 +535,7 @@
// inst->priorModelPars[5] is weight for spectral flatness
// inst->priorModelPars[6] is weight for spectral difference
featureSum = (float)(1 + useFeatureSpecFlat + useFeatureSpecDiff);
- inst->priorModelPars[4] = (float)1.0 / featureSum;
+ inst->priorModelPars[4] = 1.f / featureSum;
inst->priorModelPars[5] = ((float)useFeatureSpecFlat) / featureSum;
inst->priorModelPars[6] = ((float)useFeatureSpecDiff) / featureSum;
@@ -622,10 +625,9 @@
inst->featureData[6] += inst->signalEnergy;
avgDiffNormMagn =
- varMagn - (covMagnPause * covMagnPause) / (varPause + (float)0.0001);
+ varMagn - (covMagnPause * covMagnPause) / (varPause + 0.0001f);
// normalize and compute time-avg update of difference feature
- avgDiffNormMagn =
- (float)(avgDiffNormMagn / (inst->featureData[5] + (float)0.0001));
+ avgDiffNormMagn = (float)(avgDiffNormMagn / (inst->featureData[5] + 0.0001f));
inst->featureData[4] +=
SPECT_DIFF_TAVG * (avgDiffNormMagn - inst->featureData[4]);
}
@@ -650,9 +652,9 @@
float widthPrior, widthPrior0, widthPrior1, widthPrior2;
widthPrior0 = WIDTH_PR_MAP;
- widthPrior1 = (float)2.0 * WIDTH_PR_MAP; // width for pause region:
+ widthPrior1 = 2.f * WIDTH_PR_MAP; // width for pause region:
// lower range, so increase width in tanh map
- widthPrior2 = (float)2.0 * WIDTH_PR_MAP; // for spectral-difference measure
+ widthPrior2 = 2.f * WIDTH_PR_MAP; // for spectral-difference measure
// threshold parameters for features
threshPrior0 = inst->priorModelPars[0];
@@ -671,9 +673,9 @@
// this is the average over all frequencies of the smooth log lrt
logLrtTimeAvgKsum = 0.0;
for (i = 0; i < inst->magnLen; i++) {
- tmpFloat1 = (float)1.0 + (float)2.0 * snrLocPrior[i];
- tmpFloat2 = (float)2.0 * snrLocPrior[i] / (tmpFloat1 + (float)0.0001);
- besselTmp = (snrLocPost[i] + (float)1.0) * tmpFloat2;
+ tmpFloat1 = 1.f + 2.f * snrLocPrior[i];
+ tmpFloat2 = 2.f * snrLocPrior[i] / (tmpFloat1 + 0.0001f);
+ besselTmp = (snrLocPost[i] + 1.f) * tmpFloat2;
inst->logLrtTimeAvg[i] +=
LRT_TAVG * (besselTmp - (float)log(tmpFloat1) - inst->logLrtTimeAvg[i]);
logLrtTimeAvgKsum += inst->logLrtTimeAvg[i];
@@ -693,9 +695,9 @@
widthPrior = widthPrior1;
}
// compute indicator function: sigmoid map
- indicator0 = (float)0.5 *
- ((float)tanh(widthPrior * (logLrtTimeAvgKsum - threshPrior0)) +
- (float)1.0);
+ indicator0 =
+ 0.5f *
+ ((float)tanh(widthPrior * (logLrtTimeAvgKsum - threshPrior0)) + 1.f);
// spectral flatness feature
tmpFloat1 = inst->featureData[0];
@@ -709,9 +711,9 @@
}
// compute indicator function: sigmoid map
indicator1 =
- (float)0.5 *
+ 0.5f *
((float)tanh((float)sgnMap * widthPrior * (threshPrior1 - tmpFloat1)) +
- (float)1.0);
+ 1.f);
// for template spectrum-difference
tmpFloat1 = inst->featureData[4];
@@ -722,8 +724,7 @@
}
// compute indicator function: sigmoid map
indicator2 =
- (float)0.5 *
- ((float)tanh(widthPrior * (tmpFloat1 - threshPrior2)) + (float)1.0);
+ 0.5f * ((float)tanh(widthPrior * (tmpFloat1 - threshPrior2)) + 1.f);
// combine the indicator function with the feature weights
indPrior = weightIndPrior0 * indicator0 + weightIndPrior1 * indicator1 +
@@ -733,20 +734,19 @@
// compute the prior probability
inst->priorSpeechProb += PRIOR_UPDATE * (indPrior - inst->priorSpeechProb);
// make sure probabilities are within range: keep floor to 0.01
- if (inst->priorSpeechProb > 1.0) {
- inst->priorSpeechProb = (float)1.0;
+ if (inst->priorSpeechProb > 1.f) {
+ inst->priorSpeechProb = 1.f;
}
- if (inst->priorSpeechProb < 0.01) {
- inst->priorSpeechProb = (float)0.01;
+ if (inst->priorSpeechProb < 0.01f) {
+ inst->priorSpeechProb = 0.01f;
}
// final speech probability: combine prior model with LR factor:
- gainPrior = ((float)1.0 - inst->priorSpeechProb) /
- (inst->priorSpeechProb + (float)0.0001);
+ gainPrior = (1.f - inst->priorSpeechProb) / (inst->priorSpeechProb + 0.0001f);
for (i = 0; i < inst->magnLen; i++) {
invLrt = (float)exp(-inst->logLrtTimeAvg[i]);
invLrt = (float)gainPrior * invLrt;
- probSpeechFinal[i] = (float)1.0 / ((float)1.0 + invLrt);
+ probSpeechFinal[i] = 1.f / (1.f + invLrt);
}
}
@@ -762,6 +762,7 @@
float winData[ANAL_BLOCKL_MAX];
float magn[HALF_ANAL_BLOCKL], noise[HALF_ANAL_BLOCKL];
float snrLocPost[HALF_ANAL_BLOCKL], snrLocPrior[HALF_ANAL_BLOCKL];
+ float previousEstimateStsa[HALF_ANAL_BLOCKL];
float real[ANAL_BLOCKL_MAX], imag[HALF_ANAL_BLOCKL];
// Variables during startup
float sum_log_i = 0.0;
@@ -812,10 +813,10 @@
imag[0] = 0;
real[0] = winData[0];
- magn[0] = (float)(fabs(real[0]) + 1.0f);
+ magn[0] = fabs(real[0]) + 1.f;
imag[inst->magnLen - 1] = 0;
real[inst->magnLen - 1] = winData[1];
- magn[inst->magnLen - 1] = (float)(fabs(real[inst->magnLen - 1]) + 1.0f);
+ magn[inst->magnLen - 1] = fabs(real[inst->magnLen - 1]) + 1.f;
signalEnergy = (float)(real[0] * real[0]) +
(float)(real[inst->magnLen - 1] * real[inst->magnLen - 1]);
sumMagn = magn[0] + magn[inst->magnLen - 1];
@@ -834,7 +835,7 @@
fTmp = real[i] * real[i];
fTmp += imag[i] * imag[i];
signalEnergy += fTmp;
- magn[i] = ((float)sqrt(fTmp)) + 1.0f;
+ magn[i] = ((float)sqrt(fTmp)) + 1.f;
sumMagn += magn[i];
if (inst->blockInd < END_STARTUP_SHORT) {
if (i >= kStartBand) {
@@ -866,24 +867,24 @@
(sum_log_i_square * sum_log_magn - sum_log_i * sum_log_i_log_magn);
tmpFloat3 = tmpFloat2 / tmpFloat1;
// Constrain the estimated spectrum to be positive
- if (tmpFloat3 < 0.0f) {
- tmpFloat3 = 0.0f;
+ if (tmpFloat3 < 0.f) {
+ tmpFloat3 = 0.f;
}
inst->pinkNoiseNumerator += tmpFloat3;
tmpFloat2 = (sum_log_i * sum_log_magn);
tmpFloat2 -= ((float)(inst->magnLen - kStartBand)) * sum_log_i_log_magn;
tmpFloat3 = tmpFloat2 / tmpFloat1;
// Constrain the pink noise power to be in the interval [0, 1];
- if (tmpFloat3 < 0.0f) {
- tmpFloat3 = 0.0f;
+ if (tmpFloat3 < 0.f) {
+ tmpFloat3 = 0.f;
}
- if (tmpFloat3 > 1.0f) {
- tmpFloat3 = 1.0f;
+ if (tmpFloat3 > 1.f) {
+ tmpFloat3 = 1.f;
}
inst->pinkNoiseExp += tmpFloat3;
// Calculate frequency independent parts of parametric noise estimate.
- if (inst->pinkNoiseExp > 0.0f) {
+ if (inst->pinkNoiseExp > 0.f) {
// Use pink noise estimate
parametric_num =
exp(inst->pinkNoiseNumerator / (float)(inst->blockInd + 1));
@@ -893,7 +894,7 @@
for (i = 0; i < inst->magnLen; i++) {
// Estimate the background noise using the white and pink noise
// parameters
- if (inst->pinkNoiseExp == 0.0f) {
+ if (inst->pinkNoiseExp == 0.f) {
// Use white noise estimate
inst->parametricNoise[i] = inst->whiteNoiseLevel;
} else {
@@ -923,19 +924,18 @@
// compute DD estimate of prior SNR: needed for new method
for (i = 0; i < inst->magnLen; i++) {
// post snr
- snrLocPost[i] = (float)0.0;
+ snrLocPost[i] = 0.f;
if (magn[i] > noise[i]) {
- snrLocPost[i] = magn[i] / (noise[i] + (float)0.0001) - (float)1.0;
+ snrLocPost[i] = magn[i] / (noise[i] + 0.0001f) - 1.f;
}
// previous post snr
// previous estimate: based on previous frame with gain filter
- inst->previousEstimateStsa[i] = inst->magnPrev[i] /
- (inst->noisePrev[i] + (float)0.0001) *
- (inst->smooth[i]);
+ previousEstimateStsa[i] = inst->magnPrevAnalyze[i] /
+ (inst->noisePrev[i] + 0.0001f) * inst->smooth[i];
// DD estimate is sum of two terms: current estimate and previous estimate
// directed decision update of snrPrior
- snrLocPrior[i] = DD_PR_SNR * inst->previousEstimateStsa[i] +
- ((float)1.0 - DD_PR_SNR) * snrLocPost[i];
+ snrLocPrior[i] =
+ DD_PR_SNR * previousEstimateStsa[i] + (1.f - DD_PR_SNR) * snrLocPost[i];
// post and prior snr needed for step 2
} // end of loop over freqs
// done with step 1: dd computation of prior and post snr
@@ -968,8 +968,8 @@
inst->featureData[6] =
inst->featureData[6] / ((float)inst->modelUpdatePars[1]);
inst->featureData[5] =
- (float)0.5 * (inst->featureData[6] + inst->featureData[5]);
- inst->featureData[6] = (float)0.0;
+ 0.5f * (inst->featureData[6] + inst->featureData[5]);
+ inst->featureData[6] = 0.f;
}
}
}
@@ -979,13 +979,12 @@
gammaNoiseTmp = NOISE_UPDATE;
for (i = 0; i < inst->magnLen; i++) {
probSpeech = inst->speechProb[i];
- probNonSpeech = (float)1.0 - probSpeech;
+ probNonSpeech = 1.f - probSpeech;
// temporary noise update:
// use it for speech frames if update value is less than previous
- noiseUpdateTmp =
- gammaNoiseTmp * inst->noisePrev[i] +
- ((float)1.0 - gammaNoiseTmp) *
- (probNonSpeech * magn[i] + probSpeech * inst->noisePrev[i]);
+ noiseUpdateTmp = gammaNoiseTmp * inst->noisePrev[i] +
+ (1.f - gammaNoiseTmp) * (probNonSpeech * magn[i] +
+ probSpeech * inst->noisePrev[i]);
//
// time-constant based on speech/noise state
gammaNoiseOld = gammaNoiseTmp;
@@ -1002,10 +1001,9 @@
if (gammaNoiseTmp == gammaNoiseOld) {
noise[i] = noiseUpdateTmp;
} else {
- noise[i] =
- gammaNoiseTmp * inst->noisePrev[i] +
- ((float)1.0 - gammaNoiseTmp) *
- (probNonSpeech * magn[i] + probSpeech * inst->noisePrev[i]);
+ noise[i] = gammaNoiseTmp * inst->noisePrev[i] +
+ (1.f - gammaNoiseTmp) * (probNonSpeech * magn[i] +
+ probSpeech * inst->noisePrev[i]);
// allow for noise update downwards:
// if noise update decreases the noise, it is safe, so allow it to
// happen
@@ -1017,9 +1015,8 @@
// done with step 2: noise update
// keep track of noise spectrum for next frame
- for (i = 0; i < inst->magnLen; i++) {
- inst->noisePrev[i] = noise[i];
- }
+ memcpy(inst->noise, noise, sizeof(*noise) * inst->magnLen);
+ memcpy(inst->magnPrevAnalyze, magn, sizeof(*magn) * inst->magnLen);
return 0;
}
@@ -1034,7 +1031,7 @@
int i;
float energy1, energy2, gain, factor, factor1, factor2;
- float snrPrior, currentEstimateStsa;
+ float snrPrior, previousEstimateStsa, currentEstimateStsa;
float tmpFloat1, tmpFloat2;
float fTmp;
float fout[BLOCKL_MAX];
@@ -1050,6 +1047,7 @@
float gainMapParHB = 1.0;
float gainTimeDomainHB = 1.0;
float avgProbSpeechHB, avgProbSpeechHBTmp, avgFilterGainHB, gainModHB;
+ float sumMagnAnalyze, sumMagnProcess;
// Check that initiation has been done
if (inst->initFlag != 1) {
@@ -1121,10 +1119,10 @@
imag[0] = 0;
real[0] = winData[0];
- magn[0] = (float)(fabs(real[0]) + 1.0f);
+ magn[0] = fabs(real[0]) + 1.f;
imag[inst->magnLen - 1] = 0;
real[inst->magnLen - 1] = winData[1];
- magn[inst->magnLen - 1] = (float)(fabs(real[inst->magnLen - 1]) + 1.0f);
+ magn[inst->magnLen - 1] = fabs(real[inst->magnLen - 1]) + 1.f;
if (inst->blockInd < END_STARTUP_SHORT) {
inst->initMagnEst[0] += magn[0];
inst->initMagnEst[inst->magnLen - 1] += magn[inst->magnLen - 1];
@@ -1135,7 +1133,7 @@
// magnitude spectrum
fTmp = real[i] * real[i];
fTmp += imag[i] * imag[i];
- magn[i] = ((float)sqrt(fTmp)) + 1.0f;
+ magn[i] = ((float)sqrt(fTmp)) + 1.f;
if (inst->blockInd < END_STARTUP_SHORT) {
inst->initMagnEst[i] += magn[i];
}
@@ -1143,17 +1141,19 @@
// Compute dd update of prior snr and post snr based on new noise estimate
for (i = 0; i < inst->magnLen; i++) {
+ // previous estimate: based on previous frame with gain filter
+ previousEstimateStsa = inst->magnPrevProcess[i] /
+ (inst->noisePrev[i] + 0.0001f) * inst->smooth[i];
// post and prior snr
- currentEstimateStsa = (float)0.0;
- if (magn[i] > inst->noisePrev[i]) {
- currentEstimateStsa =
- magn[i] / (inst->noisePrev[i] + (float)0.0001) - (float)1.0;
+ currentEstimateStsa = 0.f;
+ if (magn[i] > inst->noise[i]) {
+ currentEstimateStsa = magn[i] / (inst->noise[i] + 0.0001f) - 1.f;
}
// DD estimate is sume of two terms: current estimate and previous
// estimate
// directed decision update of snrPrior
- snrPrior = DD_PR_SNR * inst->previousEstimateStsa[i] +
- ((float)1.0 - DD_PR_SNR) * currentEstimateStsa;
+ snrPrior = DD_PR_SNR * previousEstimateStsa +
+ (1.f - DD_PR_SNR) * currentEstimateStsa;
// gain filter
tmpFloat1 = inst->overdrive + snrPrior;
tmpFloat2 = (float)snrPrior / tmpFloat1;
@@ -1166,20 +1166,20 @@
theFilter[i] = inst->denoiseBound;
}
// flooring top
- if (theFilter[i] > (float)1.0) {
- theFilter[i] = 1.0;
+ if (theFilter[i] > 1.f) {
+ theFilter[i] = 1.f;
}
if (inst->blockInd < END_STARTUP_SHORT) {
theFilterTmp[i] =
(inst->initMagnEst[i] - inst->overdrive * inst->parametricNoise[i]);
- theFilterTmp[i] /= (inst->initMagnEst[i] + (float)0.0001);
+ theFilterTmp[i] /= (inst->initMagnEst[i] + 0.0001f);
// flooring bottom
if (theFilterTmp[i] < inst->denoiseBound) {
theFilterTmp[i] = inst->denoiseBound;
}
// flooring top
- if (theFilterTmp[i] > (float)1.0) {
- theFilterTmp[i] = 1.0;
+ if (theFilterTmp[i] > 1.f) {
+ theFilterTmp[i] = 1.f;
}
// Weight the two suppression filters
theFilter[i] *= (inst->blockInd);
@@ -1193,9 +1193,8 @@
imag[i] *= inst->smooth[i];
}
// keep track of magn spectrum for next frame
- for (i = 0; i < inst->magnLen; i++) {
- inst->magnPrev[i] = magn[i];
- }
+ memcpy(inst->magnPrevProcess, magn, sizeof(*magn) * inst->magnLen);
+ memcpy(inst->noisePrev, inst->noise, sizeof(inst->noise[0]) * inst->magnLen);
// back to time domain
winData[0] = real[0];
winData[1] = real[inst->magnLen - 1];
@@ -1206,26 +1205,26 @@
WebRtc_rdft(inst->anaLen, -1, winData, inst->ip, inst->wfft);
for (i = 0; i < inst->anaLen; i++) {
- real[i] = 2.0f * winData[i] / inst->anaLen; // fft scaling
+ real[i] = 2.f * winData[i] / inst->anaLen; // fft scaling
}
// scale factor: only do it after END_STARTUP_LONG time
- factor = (float)1.0;
+ factor = 1.f;
if (inst->gainmap == 1 && inst->blockInd > END_STARTUP_LONG) {
- factor1 = (float)1.0;
- factor2 = (float)1.0;
+ factor1 = 1.f;
+ factor2 = 1.f;
energy2 = 0.0;
for (i = 0; i < inst->anaLen; i++) {
energy2 += (float)real[i] * (float)real[i];
}
- gain = (float)sqrt(energy2 / (energy1 + (float)1.0));
+ gain = (float)sqrt(energy2 / (energy1 + 1.f));
// scaling for new version
if (gain > B_LIM) {
- factor1 = (float)1.0 + (float)1.3 * (gain - B_LIM);
- if (gain * factor1 > (float)1.0) {
- factor1 = (float)1.0 / gain;
+ factor1 = 1.f + 1.3f * (gain - B_LIM);
+ if (gain * factor1 > 1.f) {
+ factor1 = 1.f / gain;
}
}
if (gain < B_LIM) {
@@ -1234,12 +1233,12 @@
if (gain <= inst->denoiseBound) {
gain = inst->denoiseBound;
}
- factor2 = (float)1.0 - (float)0.3 * (B_LIM - gain);
+ factor2 = 1.f - 0.3f * (B_LIM - gain);
}
// combine both scales with speech/noise prob:
// note prior (priorSpeechProb) is not frequency dependent
factor = inst->priorSpeechProb * factor1 +
- ((float)1.0 - inst->priorSpeechProb) * factor2;
+ (1.f - inst->priorSpeechProb) * factor2;
} // out of inst->gainmap==1
// synthesis
@@ -1271,6 +1270,16 @@
avgProbSpeechHB += inst->speechProb[i];
}
avgProbSpeechHB = avgProbSpeechHB / ((float)deltaBweHB);
+ // If the speech was suppressed by a component between Analyze and
+ // Process, for example the AEC, then it should not be considered speech
+ // for high band suppression purposes.
+ sumMagnAnalyze = 0;
+ sumMagnProcess = 0;
+ for (i = 0; i < inst->magnLen; ++i) {
+ sumMagnAnalyze += inst->magnPrevAnalyze[i];
+ sumMagnProcess += inst->magnPrevProcess[i];
+ }
+ avgProbSpeechHB *= sumMagnProcess / sumMagnAnalyze;
// average filter gain from low band
// average over second half (i.e., 4->8kHz) of freq. spectrum
avgFilterGainHB = 0.0;
@@ -1278,15 +1287,13 @@
avgFilterGainHB += inst->smooth[i];
}
avgFilterGainHB = avgFilterGainHB / ((float)(deltaGainHB));
- avgProbSpeechHBTmp = (float)2.0 * avgProbSpeechHB - (float)1.0;
+ avgProbSpeechHBTmp = 2.f * avgProbSpeechHB - 1.f;
// gain based on speech prob:
- gainModHB = (float)0.5 *
- ((float)1.0 + (float)tanh(gainMapParHB * avgProbSpeechHBTmp));
+ gainModHB = 0.5f * (1.f + (float)tanh(gainMapParHB * avgProbSpeechHBTmp));
// combine gain with low band gain
- gainTimeDomainHB = (float)0.5 * gainModHB + (float)0.5 * avgFilterGainHB;
- if (avgProbSpeechHB >= (float)0.5) {
- gainTimeDomainHB =
- (float)0.25 * gainModHB + (float)0.75 * avgFilterGainHB;
+ gainTimeDomainHB = 0.5f * gainModHB + 0.5f * avgFilterGainHB;
+ if (avgProbSpeechHB >= 0.5f) {
+ gainTimeDomainHB = 0.25f * gainModHB + 0.75f * avgFilterGainHB;
}
gainTimeDomainHB = gainTimeDomainHB * decayBweHB;
// make sure gain is within flooring range
@@ -1295,8 +1302,8 @@
gainTimeDomainHB = inst->denoiseBound;
}
// flooring top
- if (gainTimeDomainHB > (float)1.0) {
- gainTimeDomainHB = 1.0;
+ if (gainTimeDomainHB > 1.f) {
+ gainTimeDomainHB = 1.f;
}
// apply gain
for (i = 0; i < inst->blockLen; i++) {
diff --git a/webrtc/modules/audio_processing/ns/ns_core.h b/webrtc/modules/audio_processing/ns/ns_core.h
index 2d36d8a..a4718fb 100644
--- a/webrtc/modules/audio_processing/ns/ns_core.h
+++ b/webrtc/modules/audio_processing/ns/ns_core.h
@@ -69,7 +69,6 @@
int counter[SIMULT];
int updates;
// parameters for Wiener filter
- float previousEstimateStsa[HALF_ANAL_BLOCKL];
float smooth[HALF_ANAL_BLOCKL];
float overdrive;
float denoiseBound;
@@ -83,8 +82,12 @@
int modelUpdatePars[4]; // parameters for updating or estimating
// thresholds/weights for prior model
float priorModelPars[7]; // parameters for prior model
+ float noise[HALF_ANAL_BLOCKL]; // noise spectrum from current frame
float noisePrev[HALF_ANAL_BLOCKL]; // noise spectrum from previous frame
- float magnPrev[HALF_ANAL_BLOCKL]; // magnitude spectrum of previous frame
+ // magnitude spectrum of previous analyze frame
+ float magnPrevAnalyze[HALF_ANAL_BLOCKL];
+ // magnitude spectrum of previous process frame
+ float magnPrevProcess[HALF_ANAL_BLOCKL];
float logLrtTimeAvg[HALF_ANAL_BLOCKL]; // log lrt factor with time-smoothing
float priorSpeechProb; // prior speech/noise probability
float featureData[7]; // data for features
