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Break out WebRtcNs_ComputeDdUpdate function in ns_core

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This is done in order to make the code more readible and maintainable.
It generates bit-exact output.

BUG=webrtc:3811
R=bjornv@webrtc.org, kwiberg@webrtc.org

Review URL: https://webrtc-codereview.appspot.com/31739004

git-svn-id: http://webrtc.googlecode.com/svn/trunk@7514 4adac7df-926f-26a2-2b94-8c16560cd09d
This commit is contained in:
aluebs@webrtc.org
2014-10-23 19:54:33 +00:00
parent f8ea0d5518
commit 7e19a11a71
1 changed files with 30 additions and 22 deletions
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52
webrtc/modules/audio_processing/ns/ns_core.c
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@@ -1014,6 +1014,35 @@ static void Windowing(const float* window,
} }
} }
// Estimate prior SNR decision-directed and compute DD based Wiener Filter.
// Input:
// * |magn| is the signal magnitude spectrum estimate.
// Output:
// * |theFilter| is the frequency response of the computed Wiener filter.
static void ComputeDdBasedWienerFilter(const NSinst_t* const self,
const float* magn,
float* theFilter) {
int i;
float snrPrior, previousEstimateStsa, currentEstimateStsa;
for (i = 0; i < self->magnLen; i++) {
// Previous estimate: based on previous frame with gain filter.
previousEstimateStsa = self->magnPrevProcess[i] /
(self->noisePrev[i] + 0.0001f) * self->smooth[i];
// Post and prior SNR.
currentEstimateStsa = 0.f;
if (magn[i] > self->noise[i]) {
currentEstimateStsa = magn[i] / (self->noise[i] + 0.0001f) - 1.f;
}
// DD estimate is sum of two terms: current estimate and previous estimate.
// Directed decision update of |snrPrior|.
snrPrior = DD_PR_SNR * previousEstimateStsa +
(1.f - DD_PR_SNR) * currentEstimateStsa;
// Gain filter.
theFilter[i] = snrPrior / (self->overdrive + snrPrior);
} // End of loop over frequencies.
}
int WebRtcNs_AnalyzeCore(NSinst_t* inst, float* speechFrame) { int WebRtcNs_AnalyzeCore(NSinst_t* inst, float* speechFrame) {
int i; int i;
const int kStartBand = 5; // Skip first frequency bins during estimation. const int kStartBand = 5; // Skip first frequency bins during estimation.
@@ -1172,8 +1201,6 @@ int WebRtcNs_ProcessCore(NSinst_t* inst,
int i; int i;
float energy1, energy2, gain, factor, factor1, factor2; float energy1, energy2, gain, factor, factor1, factor2;
float snrPrior, previousEstimateStsa, currentEstimateStsa;
float tmpFloat1, tmpFloat2;
float fout[BLOCKL_MAX]; float fout[BLOCKL_MAX];
float winData[ANAL_BLOCKL_MAX]; float winData[ANAL_BLOCKL_MAX];
float magn[HALF_ANAL_BLOCKL]; float magn[HALF_ANAL_BLOCKL];
@@ -1245,26 +1272,7 @@ int WebRtcNs_ProcessCore(NSinst_t* inst,
} }
} }
// Compute dd update of prior snr and post snr based on new noise estimate ComputeDdBasedWienerFilter(inst, magn, theFilter);
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 = 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 * previousEstimateStsa +
(1.f - DD_PR_SNR) * currentEstimateStsa;
// gain filter
tmpFloat1 = inst->overdrive + snrPrior;
tmpFloat2 = (float)snrPrior / tmpFloat1;
theFilter[i] = (float)tmpFloat2;
} // end of loop over freqs
for (i = 0; i < inst->magnLen; i++) { for (i = 0; i < inst->magnLen; i++) {
// flooring bottom // flooring bottom