VAD refactoring: Code style changes of local function.
Changes applied to local GmmProbability(): * Replaced shift macros with shift operations * Indentation and braces * Removed redundant code * Removed unnecessary type casts * Name changes * Adjusted comments Tested with vad_unittests and audioproc_unittest BUG=None TEST=None Review URL: https://webrtc-codereview.appspot.com/475002 git-svn-id: http://webrtc.googlecode.com/svn/trunk@1991 4adac7df-926f-26a2-2b94-8c16560cd09d
This commit is contained in:
bjornv@webrtc.org
parent
cac787842c
commit
2273f325b2
@ -91,409 +91,385 @@ static const int16_t kLocalThresholdVAG[3] = { 94, 94, 94 };
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static const int16_t kGlobalThresholdVAG[3] = { 1100, 1050, 1100 };
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// Calculates the probabilities for both speech and background noise using
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// Gaussian Mixture Models. A hypothesis-test is performed to decide which type
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// of signal is most probable.
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// Gaussian Mixture Models (GMM). A hypothesis-test is performed to decide which
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// type of signal is most probable.
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//
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// - inst [i/o] : Pointer to VAD instance
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// - self [i/o] : Pointer to VAD instance
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// - feature_vector [i] : Feature vector = log10(energy in frequency band)
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// - total_power [i] : Total power in audio frame.
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// - frame_length [i] : Number of input samples
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//
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// - returns : the VAD decision (0 - noise, 1 - speech).
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static int16_t GmmProbability(VadInstT *inst, int16_t *feature_vector,
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int16_t total_power, int frame_length)
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{
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int n, k;
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int16_t backval;
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int16_t h0, h1;
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int16_t ratvec, xval;
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int16_t vadflag;
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int16_t shifts0, shifts1;
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int16_t tmp16, tmp16_1, tmp16_2;
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int16_t diff, nr, pos;
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int16_t nmk, nmk2, nmk3, smk, smk2, nsk, ssk;
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int16_t delt, ndelt;
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int16_t maxspe, maxmu;
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int16_t deltaN[kTableSize], deltaS[kTableSize];
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int16_t ngprvec[kTableSize], sgprvec[kTableSize];
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int32_t h0test, h1test;
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int32_t tmp32_1, tmp32_2;
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int32_t dotVal;
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int32_t nmid, smid;
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int32_t probn[kNumGaussians], probs[kNumGaussians];
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int16_t *nmean1ptr, *nmean2ptr, *smean1ptr, *smean2ptr, *nstd1ptr, *nstd2ptr,
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*sstd1ptr, *sstd2ptr;
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int16_t overhead1, overhead2, individualTest, totalTest;
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static int16_t GmmProbability(VadInstT* self, int16_t* feature_vector,
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int16_t total_power, int frame_length) {
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int n, k;
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int16_t backval;
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int16_t h0, h1;
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int16_t ratvec, xval;
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int16_t vadflag = 0;
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int16_t shifts0, shifts1;
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int16_t tmp16, tmp16_1, tmp16_2;
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int16_t diff;
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int nr, pos;
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int16_t nmk, nmk2, nmk3, smk, smk2, nsk, ssk;
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int16_t delt, ndelt;
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int16_t maxspe, maxmu;
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int16_t deltaN[kTableSize], deltaS[kTableSize];
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int16_t ngprvec[kTableSize], sgprvec[kTableSize];
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int32_t h0test, h1test;
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int32_t tmp32_1, tmp32_2;
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int32_t dotVal;
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int32_t nmid, smid;
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int32_t probn[kNumGaussians], probs[kNumGaussians];
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int16_t *nmean1ptr, *nmean2ptr, *smean1ptr, *smean2ptr;
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int16_t *nstd1ptr, *nstd2ptr, *sstd1ptr, *sstd2ptr;
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int16_t overhead1, overhead2, individualTest, totalTest;
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// Set the thresholds to different values based on frame length
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if (frame_length == 80)
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{
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// 80 input samples
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overhead1 = inst->over_hang_max_1[0];
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overhead2 = inst->over_hang_max_2[0];
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individualTest = inst->individual[0];
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totalTest = inst->total[0];
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} else if (frame_length == 160)
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{
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// 160 input samples
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overhead1 = inst->over_hang_max_1[1];
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overhead2 = inst->over_hang_max_2[1];
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individualTest = inst->individual[1];
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totalTest = inst->total[1];
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} else
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{
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// 240 input samples
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overhead1 = inst->over_hang_max_1[2];
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overhead2 = inst->over_hang_max_2[2];
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individualTest = inst->individual[2];
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totalTest = inst->total[2];
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// Set various thresholds based on frame lengths (80, 160 or 240 samples).
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if (frame_length == 80) {
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overhead1 = self->over_hang_max_1[0];
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overhead2 = self->over_hang_max_2[0];
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individualTest = self->individual[0];
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totalTest = self->total[0];
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} else if (frame_length == 160) {
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overhead1 = self->over_hang_max_1[1];
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overhead2 = self->over_hang_max_2[1];
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individualTest = self->individual[1];
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totalTest = self->total[1];
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} else {
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overhead1 = self->over_hang_max_1[2];
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overhead2 = self->over_hang_max_2[2];
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individualTest = self->individual[2];
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totalTest = self->total[2];
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}
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if (total_power > kMinEnergy) {
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// We have a signal present.
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// Set pointers to the Gaussian parameters.
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nmean1ptr = &self->noise_means[0];
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nmean2ptr = &self->noise_means[kNumChannels];
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smean1ptr = &self->speech_means[0];
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smean2ptr = &self->speech_means[kNumChannels];
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nstd1ptr = &self->noise_stds[0];
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nstd2ptr = &self->noise_stds[kNumChannels];
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sstd1ptr = &self->speech_stds[0];
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sstd2ptr = &self->speech_stds[kNumChannels];
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dotVal = 0;
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for (n = 0; n < kNumChannels; n++) {
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// Perform for all channels.
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pos = (n << 1);
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xval = feature_vector[n];
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// Probability for Noise, Q7 * Q20 = Q27.
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean1ptr++, *nstd1ptr++,
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&deltaN[pos]);
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probn[0] = kNoiseDataWeights[n] * tmp32_1;
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean2ptr++, *nstd2ptr++,
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&deltaN[pos + 1]);
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probn[1] = kNoiseDataWeights[n + kNumChannels] * tmp32_1;
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h0test = probn[0] + probn[1]; // Q27
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h0 = (int16_t) (h0test >> 12); // Q15
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// Probability for Speech.
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean1ptr++, *sstd1ptr++,
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&deltaS[pos]);
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probs[0] = kSpeechDataWeights[n] * tmp32_1;
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean2ptr++, *sstd2ptr++,
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&deltaS[pos + 1]);
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probs[1] = kSpeechDataWeights[n + kNumChannels] * tmp32_1;
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h1test = probs[0] + probs[1]; // Q27
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h1 = (int16_t) (h1test >> 12); // Q15
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// Calculate the log likelihood ratio. Approximate log2(H1/H0) with
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// |shifts0| - |shifts1|.
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shifts0 = WebRtcSpl_NormW32(h0test);
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shifts1 = WebRtcSpl_NormW32(h1test);
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if ((h0test > 0) && (h1test > 0)) {
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ratvec = shifts0 - shifts1;
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} else if (h1test > 0) {
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ratvec = 31 - shifts1;
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} else if (h0test > 0) {
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ratvec = shifts0 - 31;
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} else {
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ratvec = 0;
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}
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// VAD decision with spectrum weighting.
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dotVal += WEBRTC_SPL_MUL_16_16(ratvec, kSpectrumWeight[n]);
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// Individual channel test.
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if ((ratvec << 2) > individualTest) {
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vadflag = 1;
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}
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// Probabilities used when updating model.
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if (h0 > 0) {
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tmp32_1 = probn[0] & 0xFFFFF000; // Q27
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tmp32_2 = (tmp32_1 << 2); // Q29
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ngprvec[pos] = (int16_t) WebRtcSpl_DivW32W16(tmp32_2, h0); // Q14
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ngprvec[pos + 1] = 16384 - ngprvec[pos];
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} else {
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ngprvec[pos] = 16384;
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ngprvec[pos + 1] = 0;
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}
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// Probabilities used when updating model.
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if (h1 > 0) {
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tmp32_1 = probs[0] & 0xFFFFF000;
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tmp32_2 = (tmp32_1 << 2);
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sgprvec[pos] = (int16_t) WebRtcSpl_DivW32W16(tmp32_2, h1);
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sgprvec[pos + 1] = 16384 - sgprvec[pos];
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} else {
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sgprvec[pos] = 0;
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sgprvec[pos + 1] = 0;
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}
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}
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if (total_power > kMinEnergy)
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{ // If signal present at all
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// Set pointers to the gaussian parameters
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nmean1ptr = &inst->noise_means[0];
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nmean2ptr = &inst->noise_means[kNumChannels];
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smean1ptr = &inst->speech_means[0];
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smean2ptr = &inst->speech_means[kNumChannels];
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nstd1ptr = &inst->noise_stds[0];
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nstd2ptr = &inst->noise_stds[kNumChannels];
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sstd1ptr = &inst->speech_stds[0];
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sstd2ptr = &inst->speech_stds[kNumChannels];
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vadflag = 0;
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dotVal = 0;
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for (n = 0; n < kNumChannels; n++)
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{ // For all channels
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pos = WEBRTC_SPL_LSHIFT_W16(n, 1);
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xval = feature_vector[n];
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// Probability for Noise, Q7 * Q20 = Q27
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean1ptr++, *nstd1ptr++,
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&deltaN[pos]);
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probn[0] = (int32_t)(kNoiseDataWeights[n] * tmp32_1);
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean2ptr++, *nstd2ptr++,
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&deltaN[pos + 1]);
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probn[1] = (int32_t)(kNoiseDataWeights[n + kNumChannels] * tmp32_1);
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h0test = probn[0] + probn[1]; // Q27
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h0 = (int16_t)WEBRTC_SPL_RSHIFT_W32(h0test, 12); // Q15
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// Probability for Speech
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean1ptr++, *sstd1ptr++,
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&deltaS[pos]);
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probs[0] = (int32_t)(kSpeechDataWeights[n] * tmp32_1);
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tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean2ptr++, *sstd2ptr++,
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&deltaS[pos + 1]);
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probs[1] = (int32_t)(kSpeechDataWeights[n + kNumChannels] * tmp32_1);
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h1test = probs[0] + probs[1]; // Q27
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h1 = (int16_t)WEBRTC_SPL_RSHIFT_W32(h1test, 12); // Q15
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// Get likelihood ratio. Approximate log2(H1/H0) with shifts0 - shifts1
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shifts0 = WebRtcSpl_NormW32(h0test);
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shifts1 = WebRtcSpl_NormW32(h1test);
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if ((h0test > 0) && (h1test > 0))
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{
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ratvec = shifts0 - shifts1;
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} else if (h1test > 0)
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{
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ratvec = 31 - shifts1;
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} else if (h0test > 0)
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{
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ratvec = shifts0 - 31;
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} else
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{
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ratvec = 0;
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}
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// VAD decision with spectrum weighting
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dotVal += WEBRTC_SPL_MUL_16_16(ratvec, kSpectrumWeight[n]);
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// Individual channel test
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if ((ratvec << 2) > individualTest)
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{
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vadflag = 1;
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}
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// Probabilities used when updating model
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if (h0 > 0)
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{
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tmp32_1 = probn[0] & 0xFFFFF000; // Q27
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tmp32_2 = WEBRTC_SPL_LSHIFT_W32(tmp32_1, 2); // Q29
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ngprvec[pos] = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, h0);
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ngprvec[pos + 1] = 16384 - ngprvec[pos];
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} else
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{
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ngprvec[pos] = 16384;
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ngprvec[pos + 1] = 0;
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}
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// Probabilities used when updating model
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if (h1 > 0)
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{
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tmp32_1 = probs[0] & 0xFFFFF000;
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tmp32_2 = WEBRTC_SPL_LSHIFT_W32(tmp32_1, 2);
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sgprvec[pos] = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, h1);
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sgprvec[pos + 1] = 16384 - sgprvec[pos];
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} else
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{
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sgprvec[pos] = 0;
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sgprvec[pos + 1] = 0;
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}
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}
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// Overall test
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if (dotVal >= totalTest)
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{
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vadflag |= 1;
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}
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// Set pointers to the means and standard deviations.
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nmean1ptr = &inst->noise_means[0];
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smean1ptr = &inst->speech_means[0];
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nstd1ptr = &inst->noise_stds[0];
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sstd1ptr = &inst->speech_stds[0];
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maxspe = 12800;
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// Update the model's parameters
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for (n = 0; n < kNumChannels; n++)
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{
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pos = WEBRTC_SPL_LSHIFT_W16(n, 1);
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// Get min value in past which is used for long term correction
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backval = WebRtcVad_FindMinimum(inst, feature_vector[n], n); // Q4
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// Compute the "global" mean, that is the sum of the two means weighted
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nmid = WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n], *nmean1ptr); // Q7 * Q7
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nmid += WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n+kNumChannels],
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*(nmean1ptr+kNumChannels));
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tmp16_1 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 6); // Q8
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for (k = 0; k < kNumGaussians; k++)
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{
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nr = pos + k;
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nmean2ptr = nmean1ptr + k * kNumChannels;
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smean2ptr = smean1ptr + k * kNumChannels;
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nstd2ptr = nstd1ptr + k * kNumChannels;
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sstd2ptr = sstd1ptr + k * kNumChannels;
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nmk = *nmean2ptr;
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smk = *smean2ptr;
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nsk = *nstd2ptr;
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ssk = *sstd2ptr;
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// Update noise mean vector if the frame consists of noise only
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nmk2 = nmk;
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if (!vadflag)
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{
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// deltaN = (x-mu)/sigma^2
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// ngprvec[k] = probn[k]/(probn[0] + probn[1])
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delt = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(ngprvec[nr],
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deltaN[nr], 11); // Q14*Q11
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nmk2 = nmk + (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(delt,
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kNoiseUpdateConst,
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22); // Q7+(Q14*Q15>>22)
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}
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// Long term correction of the noise mean
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ndelt = WEBRTC_SPL_LSHIFT_W16(backval, 4);
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ndelt -= tmp16_1; // Q8 - Q8
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nmk3 = nmk2 + (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(ndelt,
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kBackEta,
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9); // Q7+(Q8*Q8)>>9
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// Control that the noise mean does not drift to much
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tmp16 = WEBRTC_SPL_LSHIFT_W16(k+5, 7);
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if (nmk3 < tmp16)
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nmk3 = tmp16;
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tmp16 = WEBRTC_SPL_LSHIFT_W16(72+k-n, 7);
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if (nmk3 > tmp16)
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nmk3 = tmp16;
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*nmean2ptr = nmk3;
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if (vadflag)
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{
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// Update speech mean vector:
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// deltaS = (x-mu)/sigma^2
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// sgprvec[k] = probn[k]/(probn[0] + probn[1])
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delt = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(sgprvec[nr],
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deltaS[nr],
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11); // (Q14*Q11)>>11=Q14
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tmp16 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(delt,
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kSpeechUpdateConst,
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21) + 1;
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smk2 = smk + (tmp16 >> 1); // Q7 + (Q14 * Q15 >> 22)
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// Control that the speech mean does not drift to much
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maxmu = maxspe + 640;
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if (smk2 < kMinimumMean[k])
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smk2 = kMinimumMean[k];
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if (smk2 > maxmu)
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smk2 = maxmu;
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*smean2ptr = smk2;
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// (Q7>>3) = Q4
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tmp16 = WEBRTC_SPL_RSHIFT_W16((smk + 4), 3);
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tmp16 = feature_vector[n] - tmp16; // Q4
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tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaS[nr], tmp16, 3);
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tmp32_2 = tmp32_1 - (int32_t)4096; // Q12
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tmp16 = WEBRTC_SPL_RSHIFT_W16((sgprvec[nr]), 2);
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tmp32_1 = (int32_t)(tmp16 * tmp32_2);// (Q15>>3)*(Q14>>2)=Q12*Q12=Q24
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tmp32_2 = WEBRTC_SPL_RSHIFT_W32(tmp32_1, 4); // Q20
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// 0.1 * Q20 / Q7 = Q13
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if (tmp32_2 > 0)
|
||||
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, ssk * 10);
|
||||
else
|
||||
{
|
||||
tmp16 = (int16_t)WebRtcSpl_DivW32W16(-tmp32_2, ssk * 10);
|
||||
tmp16 = -tmp16;
|
||||
}
|
||||
// divide by 4 giving an update factor of 0.025
|
||||
tmp16 += 128; // Rounding
|
||||
ssk += WEBRTC_SPL_RSHIFT_W16(tmp16, 8);
|
||||
// Division with 8 plus Q7
|
||||
if (ssk < kMinStd)
|
||||
ssk = kMinStd;
|
||||
*sstd2ptr = ssk;
|
||||
} else
|
||||
{
|
||||
// Update GMM variance vectors
|
||||
// deltaN * (feature_vector[n] - nmk) - 1, Q11 * Q4
|
||||
tmp16 = feature_vector[n] - WEBRTC_SPL_RSHIFT_W16(nmk, 3);
|
||||
|
||||
// (Q15>>3) * (Q14>>2) = Q12 * Q12 = Q24
|
||||
tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaN[nr], tmp16, 3) - 4096;
|
||||
tmp16 = WEBRTC_SPL_RSHIFT_W16((ngprvec[nr]+2), 2);
|
||||
tmp32_2 = (int32_t)(tmp16 * tmp32_1);
|
||||
tmp32_1 = WEBRTC_SPL_RSHIFT_W32(tmp32_2, 14);
|
||||
// Q20 * approx 0.001 (2^-10=0.0009766)
|
||||
|
||||
// Q20 / Q7 = Q13
|
||||
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_1, nsk);
|
||||
if (tmp32_1 > 0)
|
||||
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_1, nsk);
|
||||
else
|
||||
{
|
||||
tmp16 = (int16_t)WebRtcSpl_DivW32W16(-tmp32_1, nsk);
|
||||
tmp16 = -tmp16;
|
||||
}
|
||||
tmp16 += 32; // Rounding
|
||||
nsk += WEBRTC_SPL_RSHIFT_W16(tmp16, 6);
|
||||
|
||||
if (nsk < kMinStd)
|
||||
nsk = kMinStd;
|
||||
|
||||
*nstd2ptr = nsk;
|
||||
}
|
||||
}
|
||||
|
||||
// Separate models if they are too close - nmid in Q14
|
||||
nmid = WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n], *nmean1ptr);
|
||||
nmid += WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n+kNumChannels], *nmean2ptr);
|
||||
|
||||
// smid in Q14
|
||||
smid = WEBRTC_SPL_MUL_16_16(kSpeechDataWeights[n], *smean1ptr);
|
||||
smid += WEBRTC_SPL_MUL_16_16(kSpeechDataWeights[n+kNumChannels], *smean2ptr);
|
||||
|
||||
// diff = "global" speech mean - "global" noise mean
|
||||
diff = (int16_t)WEBRTC_SPL_RSHIFT_W32(smid, 9);
|
||||
tmp16 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 9);
|
||||
diff -= tmp16;
|
||||
|
||||
if (diff < kMinimumDifference[n])
|
||||
{
|
||||
|
||||
tmp16 = kMinimumDifference[n] - diff; // Q5
|
||||
|
||||
// tmp16_1 = ~0.8 * (kMinimumDifference - diff) in Q7
|
||||
// tmp16_2 = ~0.2 * (kMinimumDifference - diff) in Q7
|
||||
tmp16_1 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(13, tmp16, 2);
|
||||
tmp16_2 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(3, tmp16, 2);
|
||||
|
||||
// First Gauss, speech model
|
||||
tmp16 = tmp16_1 + *smean1ptr;
|
||||
*smean1ptr = tmp16;
|
||||
smid = WEBRTC_SPL_MUL_16_16(tmp16, kSpeechDataWeights[n]);
|
||||
|
||||
// Second Gauss, speech model
|
||||
tmp16 = tmp16_1 + *smean2ptr;
|
||||
*smean2ptr = tmp16;
|
||||
smid += WEBRTC_SPL_MUL_16_16(tmp16, kSpeechDataWeights[n+kNumChannels]);
|
||||
|
||||
// First Gauss, noise model
|
||||
tmp16 = *nmean1ptr - tmp16_2;
|
||||
*nmean1ptr = tmp16;
|
||||
|
||||
nmid = WEBRTC_SPL_MUL_16_16(tmp16, kNoiseDataWeights[n]);
|
||||
|
||||
// Second Gauss, noise model
|
||||
tmp16 = *nmean2ptr - tmp16_2;
|
||||
*nmean2ptr = tmp16;
|
||||
nmid += WEBRTC_SPL_MUL_16_16(tmp16, kNoiseDataWeights[n+kNumChannels]);
|
||||
}
|
||||
|
||||
// Control that the speech & noise means do not drift to much
|
||||
maxspe = kMaximumSpeech[n];
|
||||
tmp16_2 = (int16_t)WEBRTC_SPL_RSHIFT_W32(smid, 7);
|
||||
if (tmp16_2 > maxspe)
|
||||
{ // Upper limit of speech model
|
||||
tmp16_2 -= maxspe;
|
||||
|
||||
*smean1ptr -= tmp16_2;
|
||||
*smean2ptr -= tmp16_2;
|
||||
}
|
||||
|
||||
tmp16_2 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 7);
|
||||
if (tmp16_2 > kMaximumNoise[n])
|
||||
{
|
||||
tmp16_2 -= kMaximumNoise[n];
|
||||
|
||||
*nmean1ptr -= tmp16_2;
|
||||
*nmean2ptr -= tmp16_2;
|
||||
}
|
||||
|
||||
nmean1ptr++;
|
||||
smean1ptr++;
|
||||
nstd1ptr++;
|
||||
sstd1ptr++;
|
||||
}
|
||||
inst->frame_counter++;
|
||||
} else
|
||||
{
|
||||
vadflag = 0;
|
||||
// Overall test.
|
||||
if (dotVal >= totalTest) {
|
||||
vadflag |= 1;
|
||||
}
|
||||
|
||||
// Hangover smoothing
|
||||
if (!vadflag)
|
||||
{
|
||||
if (inst->over_hang > 0)
|
||||
{
|
||||
vadflag = 2 + inst->over_hang;
|
||||
inst->over_hang = inst->over_hang - 1;
|
||||
// Set pointers to the means and standard deviations.
|
||||
nmean1ptr = &self->noise_means[0];
|
||||
smean1ptr = &self->speech_means[0];
|
||||
nstd1ptr = &self->noise_stds[0];
|
||||
sstd1ptr = &self->speech_stds[0];
|
||||
|
||||
maxspe = 12800;
|
||||
|
||||
// Update the model parameters.
|
||||
for (n = 0; n < kNumChannels; n++) {
|
||||
pos = (n << 1);
|
||||
|
||||
// Get minimum value in past which is used for long term correction.
|
||||
backval = WebRtcVad_FindMinimum(self, feature_vector[n], n); // Q4
|
||||
|
||||
// Compute the "global" mean, that is the sum of the two means weighted.
|
||||
nmid = WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n], *nmean1ptr); // Q7 * Q7
|
||||
nmid += WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n + kNumChannels],
|
||||
*(nmean1ptr + kNumChannels));
|
||||
tmp16_1 = (int16_t) (nmid >> 6); // Q8
|
||||
|
||||
for (k = 0; k < kNumGaussians; k++) {
|
||||
nr = pos + k;
|
||||
|
||||
nmean2ptr = nmean1ptr + k * kNumChannels;
|
||||
smean2ptr = smean1ptr + k * kNumChannels;
|
||||
nstd2ptr = nstd1ptr + k * kNumChannels;
|
||||
sstd2ptr = sstd1ptr + k * kNumChannels;
|
||||
nmk = *nmean2ptr;
|
||||
smk = *smean2ptr;
|
||||
nsk = *nstd2ptr;
|
||||
ssk = *sstd2ptr;
|
||||
|
||||
// Update noise mean vector if the frame consists of noise only.
|
||||
nmk2 = nmk;
|
||||
if (!vadflag) {
|
||||
// deltaN = (x-mu)/sigma^2
|
||||
// ngprvec[k] = probn[k]/(probn[0] + probn[1])
|
||||
|
||||
// (Q14 * Q11 >> 11) = Q14.
|
||||
delt = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(ngprvec[nr], deltaN[nr],
|
||||
11);
|
||||
// Q7 + (Q14 * Q15 >> 22) = Q7.
|
||||
nmk2 = nmk + (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(delt,
|
||||
kNoiseUpdateConst,
|
||||
22);
|
||||
}
|
||||
inst->num_of_speech = 0;
|
||||
} else
|
||||
{
|
||||
inst->num_of_speech = inst->num_of_speech + 1;
|
||||
if (inst->num_of_speech > kMaxSpeechFrames)
|
||||
{
|
||||
inst->num_of_speech = kMaxSpeechFrames;
|
||||
inst->over_hang = overhead2;
|
||||
} else
|
||||
inst->over_hang = overhead1;
|
||||
|
||||
// Long term correction of the noise mean.
|
||||
// Q8 - Q8 = Q8.
|
||||
ndelt = (backval << 4) - tmp16_1;
|
||||
// Q7 + (Q8 * Q8) >> 9 = Q7.
|
||||
nmk3 = nmk2 + (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(ndelt, kBackEta, 9);
|
||||
|
||||
// Control that the noise mean does not drift to much.
|
||||
tmp16 = (int16_t) ((k + 5) << 7);
|
||||
if (nmk3 < tmp16) {
|
||||
nmk3 = tmp16;
|
||||
}
|
||||
tmp16 = (int16_t) ((72 + k - n) << 7);
|
||||
if (nmk3 > tmp16) {
|
||||
nmk3 = tmp16;
|
||||
}
|
||||
*nmean2ptr = nmk3;
|
||||
|
||||
if (vadflag) {
|
||||
// Update speech mean vector:
|
||||
// |deltaS| = (x-mu)/sigma^2
|
||||
// sgprvec[k] = probn[k]/(probn[0] + probn[1])
|
||||
|
||||
// (Q14 * Q11) >> 11 = Q14.
|
||||
delt = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(sgprvec[nr], deltaS[nr],
|
||||
11);
|
||||
// Q14 * Q15 >> 21 = Q8.
|
||||
tmp16 = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(delt, kSpeechUpdateConst,
|
||||
21);
|
||||
// Q7 + (Q8 >> 1) = Q7. With rounding.
|
||||
smk2 = smk + ((tmp16 + 1) >> 1);
|
||||
|
||||
// Control that the speech mean does not drift to much.
|
||||
maxmu = maxspe + 640;
|
||||
if (smk2 < kMinimumMean[k]) {
|
||||
smk2 = kMinimumMean[k];
|
||||
}
|
||||
if (smk2 > maxmu) {
|
||||
smk2 = maxmu;
|
||||
}
|
||||
*smean2ptr = smk2; // Q7.
|
||||
|
||||
// (Q7 >> 3) = Q4. With rounding.
|
||||
tmp16 = ((smk + 4) >> 3);
|
||||
|
||||
tmp16 = feature_vector[n] - tmp16; // Q4
|
||||
// (Q11 * Q4 >> 3) = Q12.
|
||||
tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaS[nr], tmp16, 3);
|
||||
tmp32_2 = tmp32_1 - 4096;
|
||||
tmp16 = (sgprvec[nr] >> 2);
|
||||
// (Q14 >> 2) * Q12 = Q24.
|
||||
tmp32_1 = tmp16 * tmp32_2;
|
||||
|
||||
tmp32_2 = (tmp32_1 >> 4); // Q20
|
||||
|
||||
// 0.1 * Q20 / Q7 = Q13.
|
||||
if (tmp32_2 > 0) {
|
||||
tmp16 = (int16_t) WebRtcSpl_DivW32W16(tmp32_2, ssk * 10);
|
||||
} else {
|
||||
tmp16 = (int16_t) WebRtcSpl_DivW32W16(-tmp32_2, ssk * 10);
|
||||
tmp16 = -tmp16;
|
||||
}
|
||||
// Divide by 4 giving an update factor of 0.025 (= 0.1 / 4).
|
||||
// Note that division by 4 equals shift by 2, hence,
|
||||
// (Q13 >> 8) = (Q13 >> 6) / 4 = Q7.
|
||||
tmp16 += 128; // Rounding.
|
||||
ssk += (tmp16 >> 8);
|
||||
if (ssk < kMinStd) {
|
||||
ssk = kMinStd;
|
||||
}
|
||||
*sstd2ptr = ssk;
|
||||
} else {
|
||||
// Update GMM variance vectors.
|
||||
// deltaN * (feature_vector[n] - nmk) - 1
|
||||
// Q4 - (Q7 >> 3) = Q4.
|
||||
tmp16 = feature_vector[n] - (nmk >> 3);
|
||||
// (Q11 * Q4 >> 3) = Q12.
|
||||
tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaN[nr], tmp16, 3) - 4096;
|
||||
|
||||
// (Q14 >> 2) * Q12 = Q24.
|
||||
tmp16 = ((ngprvec[nr] + 2) >> 2);
|
||||
tmp32_2 = tmp16 * tmp32_1;
|
||||
// Q20 * approx 0.001 (2^-10=0.0009766), hence,
|
||||
// (Q24 >> 14) = (Q24 >> 4) / 2^10 = Q20.
|
||||
tmp32_1 = (tmp32_2 >> 14);
|
||||
|
||||
// Q20 / Q7 = Q13.
|
||||
if (tmp32_1 > 0) {
|
||||
tmp16 = (int16_t) WebRtcSpl_DivW32W16(tmp32_1, nsk);
|
||||
} else {
|
||||
tmp16 = (int16_t) WebRtcSpl_DivW32W16(-tmp32_1, nsk);
|
||||
tmp16 = -tmp16;
|
||||
}
|
||||
tmp16 += 32; // Rounding
|
||||
nsk += (tmp16 >> 6); // Q13 >> 6 = Q7.
|
||||
if (nsk < kMinStd) {
|
||||
nsk = kMinStd;
|
||||
}
|
||||
*nstd2ptr = nsk;
|
||||
}
|
||||
}
|
||||
|
||||
// Separate models if they are too close.
|
||||
// |nmid| in Q14 (= Q7 * Q7).
|
||||
nmid = WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n], *nmean1ptr);
|
||||
nmid += WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n + kNumChannels],
|
||||
*nmean2ptr);
|
||||
|
||||
// |smid| in Q14 (= Q7 * Q7).
|
||||
smid = WEBRTC_SPL_MUL_16_16(kSpeechDataWeights[n], *smean1ptr);
|
||||
smid += WEBRTC_SPL_MUL_16_16(kSpeechDataWeights[n + kNumChannels],
|
||||
*smean2ptr);
|
||||
|
||||
// |diff| = "global" speech mean - "global" noise mean.
|
||||
// (Q14 >> 9) - (Q14 >> 9) = Q5.
|
||||
diff = (int16_t) (smid >> 9) - (int16_t) (nmid >> 9);
|
||||
if (diff < kMinimumDifference[n]) {
|
||||
tmp16 = kMinimumDifference[n] - diff;
|
||||
|
||||
// |tmp16_1| = ~0.8 * (kMinimumDifference - diff) in Q7.
|
||||
// |tmp16_2| = ~0.2 * (kMinimumDifference - diff) in Q7.
|
||||
tmp16_1 = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(13, tmp16, 2);
|
||||
tmp16_2 = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(3, tmp16, 2);
|
||||
|
||||
// First Gaussian, speech model.
|
||||
tmp16 = tmp16_1 + *smean1ptr;
|
||||
*smean1ptr = tmp16;
|
||||
smid = WEBRTC_SPL_MUL_16_16(tmp16, kSpeechDataWeights[n]);
|
||||
|
||||
// Second Gaussian, speech model.
|
||||
tmp16 = tmp16_1 + *smean2ptr;
|
||||
*smean2ptr = tmp16;
|
||||
smid += WEBRTC_SPL_MUL_16_16(tmp16,
|
||||
kSpeechDataWeights[n + kNumChannels]);
|
||||
|
||||
// First Gaussian, noise model.
|
||||
tmp16 = *nmean1ptr - tmp16_2;
|
||||
*nmean1ptr = tmp16;
|
||||
nmid = WEBRTC_SPL_MUL_16_16(tmp16, kNoiseDataWeights[n]);
|
||||
|
||||
// Second Gaussian, noise model.
|
||||
tmp16 = *nmean2ptr - tmp16_2;
|
||||
*nmean2ptr = tmp16;
|
||||
nmid += WEBRTC_SPL_MUL_16_16(tmp16,
|
||||
kNoiseDataWeights[n + kNumChannels]);
|
||||
}
|
||||
|
||||
// Control that the speech & noise means do not drift to much.
|
||||
maxspe = kMaximumSpeech[n];
|
||||
tmp16_2 = (int16_t) (smid >> 7);
|
||||
if (tmp16_2 > maxspe) {
|
||||
// Upper limit of speech model.
|
||||
tmp16_2 -= maxspe;
|
||||
|
||||
*smean1ptr -= tmp16_2;
|
||||
*smean2ptr -= tmp16_2;
|
||||
}
|
||||
|
||||
tmp16_2 = (int16_t) (nmid >> 7);
|
||||
if (tmp16_2 > kMaximumNoise[n]) {
|
||||
tmp16_2 -= kMaximumNoise[n];
|
||||
|
||||
*nmean1ptr -= tmp16_2;
|
||||
*nmean2ptr -= tmp16_2;
|
||||
}
|
||||
|
||||
nmean1ptr++;
|
||||
smean1ptr++;
|
||||
nstd1ptr++;
|
||||
sstd1ptr++;
|
||||
}
|
||||
return vadflag;
|
||||
self->frame_counter++;
|
||||
}
|
||||
|
||||
// Smooth with respect to transition hysteresis.
|
||||
if (!vadflag) {
|
||||
if (self->over_hang > 0) {
|
||||
vadflag = 2 + self->over_hang;
|
||||
self->over_hang--;
|
||||
}
|
||||
self->num_of_speech = 0;
|
||||
} else {
|
||||
self->num_of_speech++;
|
||||
if (self->num_of_speech > kMaxSpeechFrames) {
|
||||
self->num_of_speech = kMaxSpeechFrames;
|
||||
self->over_hang = overhead2;
|
||||
} else {
|
||||
self->over_hang = overhead1;
|
||||
}
|
||||
}
|
||||
return vadflag;
|
||||
}
|
||||
|
||||
// Initialize the VAD. Set aggressiveness mode to default value.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user