c6abf6cd3f
Some API deprecation -- ExperimentalAgc and ExperimentalNs are gone. We're continuing to carry iSAC even though it's gone upstream, but maybe we'll want to drop that soon.
686 lines
26 KiB
C
686 lines
26 KiB
C
/*
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* Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "common_audio/vad/vad_core.h"
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#include "rtc_base/sanitizer.h"
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#include "common_audio/signal_processing/include/signal_processing_library.h"
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#include "common_audio/vad/vad_filterbank.h"
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#include "common_audio/vad/vad_gmm.h"
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#include "common_audio/vad/vad_sp.h"
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// Spectrum Weighting
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static const int16_t kSpectrumWeight[kNumChannels] = { 6, 8, 10, 12, 14, 16 };
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static const int16_t kNoiseUpdateConst = 655; // Q15
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static const int16_t kSpeechUpdateConst = 6554; // Q15
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static const int16_t kBackEta = 154; // Q8
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// Minimum difference between the two models, Q5
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static const int16_t kMinimumDifference[kNumChannels] = {
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544, 544, 576, 576, 576, 576 };
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// Upper limit of mean value for speech model, Q7
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static const int16_t kMaximumSpeech[kNumChannels] = {
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11392, 11392, 11520, 11520, 11520, 11520 };
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// Minimum value for mean value
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static const int16_t kMinimumMean[kNumGaussians] = { 640, 768 };
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// Upper limit of mean value for noise model, Q7
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static const int16_t kMaximumNoise[kNumChannels] = {
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9216, 9088, 8960, 8832, 8704, 8576 };
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// Start values for the Gaussian models, Q7
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// Weights for the two Gaussians for the six channels (noise)
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static const int16_t kNoiseDataWeights[kTableSize] = {
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34, 62, 72, 66, 53, 25, 94, 66, 56, 62, 75, 103 };
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// Weights for the two Gaussians for the six channels (speech)
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static const int16_t kSpeechDataWeights[kTableSize] = {
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48, 82, 45, 87, 50, 47, 80, 46, 83, 41, 78, 81 };
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// Means for the two Gaussians for the six channels (noise)
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static const int16_t kNoiseDataMeans[kTableSize] = {
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6738, 4892, 7065, 6715, 6771, 3369, 7646, 3863, 7820, 7266, 5020, 4362 };
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// Means for the two Gaussians for the six channels (speech)
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static const int16_t kSpeechDataMeans[kTableSize] = {
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8306, 10085, 10078, 11823, 11843, 6309, 9473, 9571, 10879, 7581, 8180, 7483
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};
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// Stds for the two Gaussians for the six channels (noise)
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static const int16_t kNoiseDataStds[kTableSize] = {
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378, 1064, 493, 582, 688, 593, 474, 697, 475, 688, 421, 455 };
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// Stds for the two Gaussians for the six channels (speech)
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static const int16_t kSpeechDataStds[kTableSize] = {
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555, 505, 567, 524, 585, 1231, 509, 828, 492, 1540, 1079, 850 };
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// Constants used in GmmProbability().
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//
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// Maximum number of counted speech (VAD = 1) frames in a row.
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static const int16_t kMaxSpeechFrames = 6;
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// Minimum standard deviation for both speech and noise.
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static const int16_t kMinStd = 384;
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// Constants in WebRtcVad_InitCore().
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// Default aggressiveness mode.
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static const short kDefaultMode = 0;
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static const int kInitCheck = 42;
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// Constants used in WebRtcVad_set_mode_core().
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//
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// Thresholds for different frame lengths (10 ms, 20 ms and 30 ms).
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//
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// Mode 0, Quality.
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static const int16_t kOverHangMax1Q[3] = { 8, 4, 3 };
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static const int16_t kOverHangMax2Q[3] = { 14, 7, 5 };
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static const int16_t kLocalThresholdQ[3] = { 24, 21, 24 };
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static const int16_t kGlobalThresholdQ[3] = { 57, 48, 57 };
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// Mode 1, Low bitrate.
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static const int16_t kOverHangMax1LBR[3] = { 8, 4, 3 };
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static const int16_t kOverHangMax2LBR[3] = { 14, 7, 5 };
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static const int16_t kLocalThresholdLBR[3] = { 37, 32, 37 };
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static const int16_t kGlobalThresholdLBR[3] = { 100, 80, 100 };
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// Mode 2, Aggressive.
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static const int16_t kOverHangMax1AGG[3] = { 6, 3, 2 };
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static const int16_t kOverHangMax2AGG[3] = { 9, 5, 3 };
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static const int16_t kLocalThresholdAGG[3] = { 82, 78, 82 };
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static const int16_t kGlobalThresholdAGG[3] = { 285, 260, 285 };
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// Mode 3, Very aggressive.
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static const int16_t kOverHangMax1VAG[3] = { 6, 3, 2 };
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static const int16_t kOverHangMax2VAG[3] = { 9, 5, 3 };
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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 weighted average w.r.t. number of Gaussians. The `data` are
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// updated with an `offset` before averaging.
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//
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// - data [i/o] : Data to average.
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// - offset [i] : An offset added to `data`.
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// - weights [i] : Weights used for averaging.
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//
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// returns : The weighted average.
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static int32_t WeightedAverage(int16_t* data, int16_t offset,
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const int16_t* weights) {
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int k;
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int32_t weighted_average = 0;
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for (k = 0; k < kNumGaussians; k++) {
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data[k * kNumChannels] += offset;
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weighted_average += data[k * kNumChannels] * weights[k * kNumChannels];
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}
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return weighted_average;
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}
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// An s16 x s32 -> s32 multiplication that's allowed to overflow. (It's still
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// undefined behavior, so not a good idea; this just makes UBSan ignore the
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// violation, so that our old code can continue to do what it's always been
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// doing.)
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static inline int32_t RTC_NO_SANITIZE("signed-integer-overflow")
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OverflowingMulS16ByS32ToS32(int16_t a, int32_t b) {
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return a * b;
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}
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// Calculates the probabilities for both speech and background noise using
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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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// - self [i/o] : Pointer to VAD instance
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// - features [i] : Feature vector of length `kNumChannels`
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// = 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* self, int16_t* features,
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int16_t total_power, size_t frame_length) {
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int channel, k;
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int16_t feature_minimum;
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int16_t h0, h1;
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int16_t log_likelihood_ratio;
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int16_t vadflag = 0;
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int16_t shifts_h0, shifts_h1;
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int16_t tmp_s16, tmp1_s16, tmp2_s16;
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int16_t diff;
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int gaussian;
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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] = { 0 }; // Conditional probability = 0.
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int16_t sgprvec[kTableSize] = { 0 }; // Conditional probability = 0.
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int32_t h0_test, h1_test;
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int32_t tmp1_s32, tmp2_s32;
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int32_t sum_log_likelihood_ratios = 0;
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int32_t noise_global_mean, speech_global_mean;
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int32_t noise_probability[kNumGaussians], speech_probability[kNumGaussians];
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int16_t overhead1, overhead2, individualTest, totalTest;
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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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// The signal power of current frame is large enough for processing. The
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// processing consists of two parts:
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// 1) Calculating the likelihood of speech and thereby a VAD decision.
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// 2) Updating the underlying model, w.r.t., the decision made.
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// The detection scheme is an LRT with hypothesis
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// H0: Noise
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// H1: Speech
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//
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// We combine a global LRT with local tests, for each frequency sub-band,
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// here defined as `channel`.
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for (channel = 0; channel < kNumChannels; channel++) {
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// For each channel we model the probability with a GMM consisting of
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// `kNumGaussians`, with different means and standard deviations depending
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// on H0 or H1.
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h0_test = 0;
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h1_test = 0;
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for (k = 0; k < kNumGaussians; k++) {
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gaussian = channel + k * kNumChannels;
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// Probability under H0, that is, probability of frame being noise.
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// Value given in Q27 = Q7 * Q20.
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tmp1_s32 = WebRtcVad_GaussianProbability(features[channel],
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self->noise_means[gaussian],
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self->noise_stds[gaussian],
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&deltaN[gaussian]);
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noise_probability[k] = kNoiseDataWeights[gaussian] * tmp1_s32;
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h0_test += noise_probability[k]; // Q27
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// Probability under H1, that is, probability of frame being speech.
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// Value given in Q27 = Q7 * Q20.
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tmp1_s32 = WebRtcVad_GaussianProbability(features[channel],
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self->speech_means[gaussian],
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self->speech_stds[gaussian],
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&deltaS[gaussian]);
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speech_probability[k] = kSpeechDataWeights[gaussian] * tmp1_s32;
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h1_test += speech_probability[k]; // Q27
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}
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// Calculate the log likelihood ratio: log2(Pr{X|H1} / Pr{X|H1}).
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// Approximation:
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// log2(Pr{X|H1} / Pr{X|H1}) = log2(Pr{X|H1}*2^Q) - log2(Pr{X|H1}*2^Q)
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// = log2(h1_test) - log2(h0_test)
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// = log2(2^(31-shifts_h1)*(1+b1))
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// - log2(2^(31-shifts_h0)*(1+b0))
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// = shifts_h0 - shifts_h1
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// + log2(1+b1) - log2(1+b0)
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// ~= shifts_h0 - shifts_h1
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//
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// Note that b0 and b1 are values less than 1, hence, 0 <= log2(1+b0) < 1.
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// Further, b0 and b1 are independent and on the average the two terms
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// cancel.
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shifts_h0 = WebRtcSpl_NormW32(h0_test);
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shifts_h1 = WebRtcSpl_NormW32(h1_test);
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if (h0_test == 0) {
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shifts_h0 = 31;
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}
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if (h1_test == 0) {
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shifts_h1 = 31;
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}
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log_likelihood_ratio = shifts_h0 - shifts_h1;
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// Update `sum_log_likelihood_ratios` with spectrum weighting. This is
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// used for the global VAD decision.
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sum_log_likelihood_ratios +=
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(int32_t) (log_likelihood_ratio * kSpectrumWeight[channel]);
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// Local VAD decision.
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if ((log_likelihood_ratio * 4) > individualTest) {
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vadflag = 1;
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}
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// TODO(bjornv): The conditional probabilities below are applied on the
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// hard coded number of Gaussians set to two. Find a way to generalize.
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// Calculate local noise probabilities used later when updating the GMM.
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h0 = (int16_t) (h0_test >> 12); // Q15
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if (h0 > 0) {
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// High probability of noise. Assign conditional probabilities for each
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// Gaussian in the GMM.
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tmp1_s32 = (noise_probability[0] & 0xFFFFF000) << 2; // Q29
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ngprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h0); // Q14
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ngprvec[channel + kNumChannels] = 16384 - ngprvec[channel];
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} else {
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// Low noise probability. Assign conditional probability 1 to the first
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// Gaussian and 0 to the rest (which is already set at initialization).
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ngprvec[channel] = 16384;
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}
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// Calculate local speech probabilities used later when updating the GMM.
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h1 = (int16_t) (h1_test >> 12); // Q15
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if (h1 > 0) {
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// High probability of speech. Assign conditional probabilities for each
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// Gaussian in the GMM. Otherwise use the initialized values, i.e., 0.
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tmp1_s32 = (speech_probability[0] & 0xFFFFF000) << 2; // Q29
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sgprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h1); // Q14
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sgprvec[channel + kNumChannels] = 16384 - sgprvec[channel];
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}
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}
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// Make a global VAD decision.
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vadflag |= (sum_log_likelihood_ratios >= totalTest);
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// Update the model parameters.
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maxspe = 12800;
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for (channel = 0; channel < kNumChannels; channel++) {
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// Get minimum value in past which is used for long term correction in Q4.
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feature_minimum = WebRtcVad_FindMinimum(self, features[channel], channel);
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// Compute the "global" mean, that is the sum of the two means weighted.
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noise_global_mean = WeightedAverage(&self->noise_means[channel], 0,
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&kNoiseDataWeights[channel]);
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tmp1_s16 = (int16_t) (noise_global_mean >> 6); // Q8
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for (k = 0; k < kNumGaussians; k++) {
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gaussian = channel + k * kNumChannels;
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nmk = self->noise_means[gaussian];
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smk = self->speech_means[gaussian];
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nsk = self->noise_stds[gaussian];
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ssk = self->speech_stds[gaussian];
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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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// deltaN = (x-mu)/sigma^2
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// ngprvec[k] = `noise_probability[k]` /
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// (`noise_probability[0]` + `noise_probability[1]`)
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// (Q14 * Q11 >> 11) = Q14.
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delt = (int16_t)((ngprvec[gaussian] * deltaN[gaussian]) >> 11);
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// Q7 + (Q14 * Q15 >> 22) = Q7.
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nmk2 = nmk + (int16_t)((delt * kNoiseUpdateConst) >> 22);
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}
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// Long term correction of the noise mean.
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// Q8 - Q8 = Q8.
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ndelt = (feature_minimum << 4) - tmp1_s16;
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// Q7 + (Q8 * Q8) >> 9 = Q7.
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nmk3 = nmk2 + (int16_t)((ndelt * kBackEta) >> 9);
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// Control that the noise mean does not drift to much.
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tmp_s16 = (int16_t) ((k + 5) << 7);
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if (nmk3 < tmp_s16) {
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nmk3 = tmp_s16;
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}
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tmp_s16 = (int16_t) ((72 + k - channel) << 7);
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if (nmk3 > tmp_s16) {
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nmk3 = tmp_s16;
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}
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self->noise_means[gaussian] = nmk3;
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if (vadflag) {
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// Update speech mean vector:
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// `deltaS` = (x-mu)/sigma^2
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// sgprvec[k] = `speech_probability[k]` /
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// (`speech_probability[0]` + `speech_probability[1]`)
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// (Q14 * Q11) >> 11 = Q14.
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delt = (int16_t)((sgprvec[gaussian] * deltaS[gaussian]) >> 11);
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// Q14 * Q15 >> 21 = Q8.
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tmp_s16 = (int16_t)((delt * kSpeechUpdateConst) >> 21);
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// Q7 + (Q8 >> 1) = Q7. With rounding.
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smk2 = smk + ((tmp_s16 + 1) >> 1);
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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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}
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if (smk2 > maxmu) {
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smk2 = maxmu;
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}
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self->speech_means[gaussian] = smk2; // Q7.
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// (Q7 >> 3) = Q4. With rounding.
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tmp_s16 = ((smk + 4) >> 3);
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tmp_s16 = features[channel] - tmp_s16; // Q4
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// (Q11 * Q4 >> 3) = Q12.
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tmp1_s32 = (deltaS[gaussian] * tmp_s16) >> 3;
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tmp2_s32 = tmp1_s32 - 4096;
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tmp_s16 = sgprvec[gaussian] >> 2;
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// (Q14 >> 2) * Q12 = Q24.
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tmp1_s32 = tmp_s16 * tmp2_s32;
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tmp2_s32 = tmp1_s32 >> 4; // Q20
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// 0.1 * Q20 / Q7 = Q13.
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if (tmp2_s32 > 0) {
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tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(tmp2_s32, ssk * 10);
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} else {
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tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(-tmp2_s32, ssk * 10);
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tmp_s16 = -tmp_s16;
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}
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// Divide by 4 giving an update factor of 0.025 (= 0.1 / 4).
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// Note that division by 4 equals shift by 2, hence,
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// (Q13 >> 8) = (Q13 >> 6) / 4 = Q7.
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tmp_s16 += 128; // Rounding.
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ssk += (tmp_s16 >> 8);
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if (ssk < kMinStd) {
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ssk = kMinStd;
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}
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self->speech_stds[gaussian] = ssk;
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} else {
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// Update GMM variance vectors.
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// deltaN * (features[channel] - nmk) - 1
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// Q4 - (Q7 >> 3) = Q4.
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tmp_s16 = features[channel] - (nmk >> 3);
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// (Q11 * Q4 >> 3) = Q12.
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tmp1_s32 = (deltaN[gaussian] * tmp_s16) >> 3;
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tmp1_s32 -= 4096;
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// (Q14 >> 2) * Q12 = Q24.
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tmp_s16 = (ngprvec[gaussian] + 2) >> 2;
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tmp2_s32 = OverflowingMulS16ByS32ToS32(tmp_s16, tmp1_s32);
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// Q20 * approx 0.001 (2^-10=0.0009766), hence,
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// (Q24 >> 14) = (Q24 >> 4) / 2^10 = Q20.
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tmp1_s32 = tmp2_s32 >> 14;
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// Q20 / Q7 = Q13.
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if (tmp1_s32 > 0) {
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tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, nsk);
|
|
} else {
|
|
tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(-tmp1_s32, nsk);
|
|
tmp_s16 = -tmp_s16;
|
|
}
|
|
tmp_s16 += 32; // Rounding
|
|
nsk += tmp_s16 >> 6; // Q13 >> 6 = Q7.
|
|
if (nsk < kMinStd) {
|
|
nsk = kMinStd;
|
|
}
|
|
self->noise_stds[gaussian] = nsk;
|
|
}
|
|
}
|
|
|
|
// Separate models if they are too close.
|
|
// `noise_global_mean` in Q14 (= Q7 * Q7).
|
|
noise_global_mean = WeightedAverage(&self->noise_means[channel], 0,
|
|
&kNoiseDataWeights[channel]);
|
|
|
|
// `speech_global_mean` in Q14 (= Q7 * Q7).
|
|
speech_global_mean = WeightedAverage(&self->speech_means[channel], 0,
|
|
&kSpeechDataWeights[channel]);
|
|
|
|
// `diff` = "global" speech mean - "global" noise mean.
|
|
// (Q14 >> 9) - (Q14 >> 9) = Q5.
|
|
diff = (int16_t) (speech_global_mean >> 9) -
|
|
(int16_t) (noise_global_mean >> 9);
|
|
if (diff < kMinimumDifference[channel]) {
|
|
tmp_s16 = kMinimumDifference[channel] - diff;
|
|
|
|
// `tmp1_s16` = ~0.8 * (kMinimumDifference - diff) in Q7.
|
|
// `tmp2_s16` = ~0.2 * (kMinimumDifference - diff) in Q7.
|
|
tmp1_s16 = (int16_t)((13 * tmp_s16) >> 2);
|
|
tmp2_s16 = (int16_t)((3 * tmp_s16) >> 2);
|
|
|
|
// Move Gaussian means for speech model by `tmp1_s16` and update
|
|
// `speech_global_mean`. Note that `self->speech_means[channel]` is
|
|
// changed after the call.
|
|
speech_global_mean = WeightedAverage(&self->speech_means[channel],
|
|
tmp1_s16,
|
|
&kSpeechDataWeights[channel]);
|
|
|
|
// Move Gaussian means for noise model by -`tmp2_s16` and update
|
|
// `noise_global_mean`. Note that `self->noise_means[channel]` is
|
|
// changed after the call.
|
|
noise_global_mean = WeightedAverage(&self->noise_means[channel],
|
|
-tmp2_s16,
|
|
&kNoiseDataWeights[channel]);
|
|
}
|
|
|
|
// Control that the speech & noise means do not drift to much.
|
|
maxspe = kMaximumSpeech[channel];
|
|
tmp2_s16 = (int16_t) (speech_global_mean >> 7);
|
|
if (tmp2_s16 > maxspe) {
|
|
// Upper limit of speech model.
|
|
tmp2_s16 -= maxspe;
|
|
|
|
for (k = 0; k < kNumGaussians; k++) {
|
|
self->speech_means[channel + k * kNumChannels] -= tmp2_s16;
|
|
}
|
|
}
|
|
|
|
tmp2_s16 = (int16_t) (noise_global_mean >> 7);
|
|
if (tmp2_s16 > kMaximumNoise[channel]) {
|
|
tmp2_s16 -= kMaximumNoise[channel];
|
|
|
|
for (k = 0; k < kNumGaussians; k++) {
|
|
self->noise_means[channel + k * kNumChannels] -= tmp2_s16;
|
|
}
|
|
}
|
|
}
|
|
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.
|
|
int WebRtcVad_InitCore(VadInstT* self) {
|
|
int i;
|
|
|
|
if (self == NULL) {
|
|
return -1;
|
|
}
|
|
|
|
// Initialization of general struct variables.
|
|
self->vad = 1; // Speech active (=1).
|
|
self->frame_counter = 0;
|
|
self->over_hang = 0;
|
|
self->num_of_speech = 0;
|
|
|
|
// Initialization of downsampling filter state.
|
|
memset(self->downsampling_filter_states, 0,
|
|
sizeof(self->downsampling_filter_states));
|
|
|
|
// Initialization of 48 to 8 kHz downsampling.
|
|
WebRtcSpl_ResetResample48khzTo8khz(&self->state_48_to_8);
|
|
|
|
// Read initial PDF parameters.
|
|
for (i = 0; i < kTableSize; i++) {
|
|
self->noise_means[i] = kNoiseDataMeans[i];
|
|
self->speech_means[i] = kSpeechDataMeans[i];
|
|
self->noise_stds[i] = kNoiseDataStds[i];
|
|
self->speech_stds[i] = kSpeechDataStds[i];
|
|
}
|
|
|
|
// Initialize Index and Minimum value vectors.
|
|
for (i = 0; i < 16 * kNumChannels; i++) {
|
|
self->low_value_vector[i] = 10000;
|
|
self->index_vector[i] = 0;
|
|
}
|
|
|
|
// Initialize splitting filter states.
|
|
memset(self->upper_state, 0, sizeof(self->upper_state));
|
|
memset(self->lower_state, 0, sizeof(self->lower_state));
|
|
|
|
// Initialize high pass filter states.
|
|
memset(self->hp_filter_state, 0, sizeof(self->hp_filter_state));
|
|
|
|
// Initialize mean value memory, for WebRtcVad_FindMinimum().
|
|
for (i = 0; i < kNumChannels; i++) {
|
|
self->mean_value[i] = 1600;
|
|
}
|
|
|
|
// Set aggressiveness mode to default (=`kDefaultMode`).
|
|
if (WebRtcVad_set_mode_core(self, kDefaultMode) != 0) {
|
|
return -1;
|
|
}
|
|
|
|
self->init_flag = kInitCheck;
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Set aggressiveness mode
|
|
int WebRtcVad_set_mode_core(VadInstT* self, int mode) {
|
|
int return_value = 0;
|
|
|
|
switch (mode) {
|
|
case 0:
|
|
// Quality mode.
|
|
memcpy(self->over_hang_max_1, kOverHangMax1Q,
|
|
sizeof(self->over_hang_max_1));
|
|
memcpy(self->over_hang_max_2, kOverHangMax2Q,
|
|
sizeof(self->over_hang_max_2));
|
|
memcpy(self->individual, kLocalThresholdQ,
|
|
sizeof(self->individual));
|
|
memcpy(self->total, kGlobalThresholdQ,
|
|
sizeof(self->total));
|
|
break;
|
|
case 1:
|
|
// Low bitrate mode.
|
|
memcpy(self->over_hang_max_1, kOverHangMax1LBR,
|
|
sizeof(self->over_hang_max_1));
|
|
memcpy(self->over_hang_max_2, kOverHangMax2LBR,
|
|
sizeof(self->over_hang_max_2));
|
|
memcpy(self->individual, kLocalThresholdLBR,
|
|
sizeof(self->individual));
|
|
memcpy(self->total, kGlobalThresholdLBR,
|
|
sizeof(self->total));
|
|
break;
|
|
case 2:
|
|
// Aggressive mode.
|
|
memcpy(self->over_hang_max_1, kOverHangMax1AGG,
|
|
sizeof(self->over_hang_max_1));
|
|
memcpy(self->over_hang_max_2, kOverHangMax2AGG,
|
|
sizeof(self->over_hang_max_2));
|
|
memcpy(self->individual, kLocalThresholdAGG,
|
|
sizeof(self->individual));
|
|
memcpy(self->total, kGlobalThresholdAGG,
|
|
sizeof(self->total));
|
|
break;
|
|
case 3:
|
|
// Very aggressive mode.
|
|
memcpy(self->over_hang_max_1, kOverHangMax1VAG,
|
|
sizeof(self->over_hang_max_1));
|
|
memcpy(self->over_hang_max_2, kOverHangMax2VAG,
|
|
sizeof(self->over_hang_max_2));
|
|
memcpy(self->individual, kLocalThresholdVAG,
|
|
sizeof(self->individual));
|
|
memcpy(self->total, kGlobalThresholdVAG,
|
|
sizeof(self->total));
|
|
break;
|
|
default:
|
|
return_value = -1;
|
|
break;
|
|
}
|
|
|
|
return return_value;
|
|
}
|
|
|
|
// Calculate VAD decision by first extracting feature values and then calculate
|
|
// probability for both speech and background noise.
|
|
|
|
int WebRtcVad_CalcVad48khz(VadInstT* inst, const int16_t* speech_frame,
|
|
size_t frame_length) {
|
|
int vad;
|
|
size_t i;
|
|
int16_t speech_nb[240]; // 30 ms in 8 kHz.
|
|
// `tmp_mem` is a temporary memory used by resample function, length is
|
|
// frame length in 10 ms (480 samples) + 256 extra.
|
|
int32_t tmp_mem[480 + 256] = { 0 };
|
|
const size_t kFrameLen10ms48khz = 480;
|
|
const size_t kFrameLen10ms8khz = 80;
|
|
size_t num_10ms_frames = frame_length / kFrameLen10ms48khz;
|
|
|
|
for (i = 0; i < num_10ms_frames; i++) {
|
|
WebRtcSpl_Resample48khzTo8khz(speech_frame,
|
|
&speech_nb[i * kFrameLen10ms8khz],
|
|
&inst->state_48_to_8,
|
|
tmp_mem);
|
|
}
|
|
|
|
// Do VAD on an 8 kHz signal
|
|
vad = WebRtcVad_CalcVad8khz(inst, speech_nb, frame_length / 6);
|
|
|
|
return vad;
|
|
}
|
|
|
|
int WebRtcVad_CalcVad32khz(VadInstT* inst, const int16_t* speech_frame,
|
|
size_t frame_length)
|
|
{
|
|
size_t len;
|
|
int vad;
|
|
int16_t speechWB[480]; // Downsampled speech frame: 960 samples (30ms in SWB)
|
|
int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
|
|
|
|
|
|
// Downsample signal 32->16->8 before doing VAD
|
|
WebRtcVad_Downsampling(speech_frame, speechWB, &(inst->downsampling_filter_states[2]),
|
|
frame_length);
|
|
len = frame_length / 2;
|
|
|
|
WebRtcVad_Downsampling(speechWB, speechNB, inst->downsampling_filter_states, len);
|
|
len /= 2;
|
|
|
|
// Do VAD on an 8 kHz signal
|
|
vad = WebRtcVad_CalcVad8khz(inst, speechNB, len);
|
|
|
|
return vad;
|
|
}
|
|
|
|
int WebRtcVad_CalcVad16khz(VadInstT* inst, const int16_t* speech_frame,
|
|
size_t frame_length)
|
|
{
|
|
size_t len;
|
|
int vad;
|
|
int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
|
|
|
|
// Wideband: Downsample signal before doing VAD
|
|
WebRtcVad_Downsampling(speech_frame, speechNB, inst->downsampling_filter_states,
|
|
frame_length);
|
|
|
|
len = frame_length / 2;
|
|
vad = WebRtcVad_CalcVad8khz(inst, speechNB, len);
|
|
|
|
return vad;
|
|
}
|
|
|
|
int WebRtcVad_CalcVad8khz(VadInstT* inst, const int16_t* speech_frame,
|
|
size_t frame_length)
|
|
{
|
|
int16_t feature_vector[kNumChannels], total_power;
|
|
|
|
// Get power in the bands
|
|
total_power = WebRtcVad_CalculateFeatures(inst, speech_frame, frame_length,
|
|
feature_vector);
|
|
|
|
// Make a VAD
|
|
inst->vad = GmmProbability(inst, feature_vector, total_power, frame_length);
|
|
|
|
return inst->vad;
|
|
}
|