/* * Copyright (c) 2012 The WebRTC project authors. All Rights Reserved. * * Use of this source code is governed by a BSD-style license * that can be found in the LICENSE file in the root of the source * tree. An additional intellectual property rights grant can be found * in the file PATENTS. All contributing project authors may * be found in the AUTHORS file in the root of the source tree. */ #include "common_audio/vad/vad_core.h" #include "rtc_base/sanitizer.h" #include "common_audio/signal_processing/include/signal_processing_library.h" #include "common_audio/vad/vad_filterbank.h" #include "common_audio/vad/vad_gmm.h" #include "common_audio/vad/vad_sp.h" // Spectrum Weighting static const int16_t kSpectrumWeight[kNumChannels] = { 6, 8, 10, 12, 14, 16 }; static const int16_t kNoiseUpdateConst = 655; // Q15 static const int16_t kSpeechUpdateConst = 6554; // Q15 static const int16_t kBackEta = 154; // Q8 // Minimum difference between the two models, Q5 static const int16_t kMinimumDifference[kNumChannels] = { 544, 544, 576, 576, 576, 576 }; // Upper limit of mean value for speech model, Q7 static const int16_t kMaximumSpeech[kNumChannels] = { 11392, 11392, 11520, 11520, 11520, 11520 }; // Minimum value for mean value static const int16_t kMinimumMean[kNumGaussians] = { 640, 768 }; // Upper limit of mean value for noise model, Q7 static const int16_t kMaximumNoise[kNumChannels] = { 9216, 9088, 8960, 8832, 8704, 8576 }; // Start values for the Gaussian models, Q7 // Weights for the two Gaussians for the six channels (noise) static const int16_t kNoiseDataWeights[kTableSize] = { 34, 62, 72, 66, 53, 25, 94, 66, 56, 62, 75, 103 }; // Weights for the two Gaussians for the six channels (speech) static const int16_t kSpeechDataWeights[kTableSize] = { 48, 82, 45, 87, 50, 47, 80, 46, 83, 41, 78, 81 }; // Means for the two Gaussians for the six channels (noise) static const int16_t kNoiseDataMeans[kTableSize] = { 6738, 4892, 7065, 6715, 6771, 3369, 7646, 3863, 7820, 7266, 5020, 4362 }; // Means for the two Gaussians for the six channels (speech) static const int16_t kSpeechDataMeans[kTableSize] = { 8306, 10085, 10078, 11823, 11843, 6309, 9473, 9571, 10879, 7581, 8180, 7483 }; // Stds for the two Gaussians for the six channels (noise) static const int16_t kNoiseDataStds[kTableSize] = { 378, 1064, 493, 582, 688, 593, 474, 697, 475, 688, 421, 455 }; // Stds for the two Gaussians for the six channels (speech) static const int16_t kSpeechDataStds[kTableSize] = { 555, 505, 567, 524, 585, 1231, 509, 828, 492, 1540, 1079, 850 }; // Constants used in GmmProbability(). // // Maximum number of counted speech (VAD = 1) frames in a row. static const int16_t kMaxSpeechFrames = 6; // Minimum standard deviation for both speech and noise. static const int16_t kMinStd = 384; // Constants in WebRtcVad_InitCore(). // Default aggressiveness mode. static const short kDefaultMode = 0; static const int kInitCheck = 42; // Constants used in WebRtcVad_set_mode_core(). // // Thresholds for different frame lengths (10 ms, 20 ms and 30 ms). // // Mode 0, Quality. static const int16_t kOverHangMax1Q[3] = { 8, 4, 3 }; static const int16_t kOverHangMax2Q[3] = { 14, 7, 5 }; static const int16_t kLocalThresholdQ[3] = { 24, 21, 24 }; static const int16_t kGlobalThresholdQ[3] = { 57, 48, 57 }; // Mode 1, Low bitrate. static const int16_t kOverHangMax1LBR[3] = { 8, 4, 3 }; static const int16_t kOverHangMax2LBR[3] = { 14, 7, 5 }; static const int16_t kLocalThresholdLBR[3] = { 37, 32, 37 }; static const int16_t kGlobalThresholdLBR[3] = { 100, 80, 100 }; // Mode 2, Aggressive. static const int16_t kOverHangMax1AGG[3] = { 6, 3, 2 }; static const int16_t kOverHangMax2AGG[3] = { 9, 5, 3 }; static const int16_t kLocalThresholdAGG[3] = { 82, 78, 82 }; static const int16_t kGlobalThresholdAGG[3] = { 285, 260, 285 }; // Mode 3, Very aggressive. static const int16_t kOverHangMax1VAG[3] = { 6, 3, 2 }; static const int16_t kOverHangMax2VAG[3] = { 9, 5, 3 }; static const int16_t kLocalThresholdVAG[3] = { 94, 94, 94 }; static const int16_t kGlobalThresholdVAG[3] = { 1100, 1050, 1100 }; // Calculates the weighted average w.r.t. number of Gaussians. The `data` are // updated with an `offset` before averaging. // // - data [i/o] : Data to average. // - offset [i] : An offset added to `data`. // - weights [i] : Weights used for averaging. // // returns : The weighted average. static int32_t WeightedAverage(int16_t* data, int16_t offset, const int16_t* weights) { int k; int32_t weighted_average = 0; for (k = 0; k < kNumGaussians; k++) { data[k * kNumChannels] += offset; weighted_average += data[k * kNumChannels] * weights[k * kNumChannels]; } return weighted_average; } // An s16 x s32 -> s32 multiplication that's allowed to overflow. (It's still // undefined behavior, so not a good idea; this just makes UBSan ignore the // violation, so that our old code can continue to do what it's always been // doing.) static inline int32_t RTC_NO_SANITIZE("signed-integer-overflow") OverflowingMulS16ByS32ToS32(int16_t a, int32_t b) { return a * b; } // Calculates the probabilities for both speech and background noise using // Gaussian Mixture Models (GMM). A hypothesis-test is performed to decide which // type of signal is most probable. // // - self [i/o] : Pointer to VAD instance // - features [i] : Feature vector of length `kNumChannels` // = log10(energy in frequency band) // - total_power [i] : Total power in audio frame. // - frame_length [i] : Number of input samples // // - returns : the VAD decision (0 - noise, 1 - speech). static int16_t GmmProbability(VadInstT* self, int16_t* features, int16_t total_power, size_t frame_length) { int channel, k; int16_t feature_minimum; int16_t h0, h1; int16_t log_likelihood_ratio; int16_t vadflag = 0; int16_t shifts_h0, shifts_h1; int16_t tmp_s16, tmp1_s16, tmp2_s16; int16_t diff; int gaussian; int16_t nmk, nmk2, nmk3, smk, smk2, nsk, ssk; int16_t delt, ndelt; int16_t maxspe, maxmu; int16_t deltaN[kTableSize], deltaS[kTableSize]; int16_t ngprvec[kTableSize] = { 0 }; // Conditional probability = 0. int16_t sgprvec[kTableSize] = { 0 }; // Conditional probability = 0. int32_t h0_test, h1_test; int32_t tmp1_s32, tmp2_s32; int32_t sum_log_likelihood_ratios = 0; int32_t noise_global_mean, speech_global_mean; int32_t noise_probability[kNumGaussians], speech_probability[kNumGaussians]; int16_t overhead1, overhead2, individualTest, totalTest; // Set various thresholds based on frame lengths (80, 160 or 240 samples). if (frame_length == 80) { overhead1 = self->over_hang_max_1[0]; overhead2 = self->over_hang_max_2[0]; individualTest = self->individual[0]; totalTest = self->total[0]; } else if (frame_length == 160) { overhead1 = self->over_hang_max_1[1]; overhead2 = self->over_hang_max_2[1]; individualTest = self->individual[1]; totalTest = self->total[1]; } else { overhead1 = self->over_hang_max_1[2]; overhead2 = self->over_hang_max_2[2]; individualTest = self->individual[2]; totalTest = self->total[2]; } if (total_power > kMinEnergy) { // The signal power of current frame is large enough for processing. The // processing consists of two parts: // 1) Calculating the likelihood of speech and thereby a VAD decision. // 2) Updating the underlying model, w.r.t., the decision made. // The detection scheme is an LRT with hypothesis // H0: Noise // H1: Speech // // We combine a global LRT with local tests, for each frequency sub-band, // here defined as `channel`. for (channel = 0; channel < kNumChannels; channel++) { // For each channel we model the probability with a GMM consisting of // `kNumGaussians`, with different means and standard deviations depending // on H0 or H1. h0_test = 0; h1_test = 0; for (k = 0; k < kNumGaussians; k++) { gaussian = channel + k * kNumChannels; // Probability under H0, that is, probability of frame being noise. // Value given in Q27 = Q7 * Q20. tmp1_s32 = WebRtcVad_GaussianProbability(features[channel], self->noise_means[gaussian], self->noise_stds[gaussian], &deltaN[gaussian]); noise_probability[k] = kNoiseDataWeights[gaussian] * tmp1_s32; h0_test += noise_probability[k]; // Q27 // Probability under H1, that is, probability of frame being speech. // Value given in Q27 = Q7 * Q20. tmp1_s32 = WebRtcVad_GaussianProbability(features[channel], self->speech_means[gaussian], self->speech_stds[gaussian], &deltaS[gaussian]); speech_probability[k] = kSpeechDataWeights[gaussian] * tmp1_s32; h1_test += speech_probability[k]; // Q27 } // Calculate the log likelihood ratio: log2(Pr{X|H1} / Pr{X|H1}). // Approximation: // log2(Pr{X|H1} / Pr{X|H1}) = log2(Pr{X|H1}*2^Q) - log2(Pr{X|H1}*2^Q) // = log2(h1_test) - log2(h0_test) // = log2(2^(31-shifts_h1)*(1+b1)) // - log2(2^(31-shifts_h0)*(1+b0)) // = shifts_h0 - shifts_h1 // + log2(1+b1) - log2(1+b0) // ~= shifts_h0 - shifts_h1 // // Note that b0 and b1 are values less than 1, hence, 0 <= log2(1+b0) < 1. // Further, b0 and b1 are independent and on the average the two terms // cancel. shifts_h0 = WebRtcSpl_NormW32(h0_test); shifts_h1 = WebRtcSpl_NormW32(h1_test); if (h0_test == 0) { shifts_h0 = 31; } if (h1_test == 0) { shifts_h1 = 31; } log_likelihood_ratio = shifts_h0 - shifts_h1; // Update `sum_log_likelihood_ratios` with spectrum weighting. This is // used for the global VAD decision. sum_log_likelihood_ratios += (int32_t) (log_likelihood_ratio * kSpectrumWeight[channel]); // Local VAD decision. if ((log_likelihood_ratio * 4) > individualTest) { vadflag = 1; } // TODO(bjornv): The conditional probabilities below are applied on the // hard coded number of Gaussians set to two. Find a way to generalize. // Calculate local noise probabilities used later when updating the GMM. h0 = (int16_t) (h0_test >> 12); // Q15 if (h0 > 0) { // High probability of noise. Assign conditional probabilities for each // Gaussian in the GMM. tmp1_s32 = (noise_probability[0] & 0xFFFFF000) << 2; // Q29 ngprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h0); // Q14 ngprvec[channel + kNumChannels] = 16384 - ngprvec[channel]; } else { // Low noise probability. Assign conditional probability 1 to the first // Gaussian and 0 to the rest (which is already set at initialization). ngprvec[channel] = 16384; } // Calculate local speech probabilities used later when updating the GMM. h1 = (int16_t) (h1_test >> 12); // Q15 if (h1 > 0) { // High probability of speech. Assign conditional probabilities for each // Gaussian in the GMM. Otherwise use the initialized values, i.e., 0. tmp1_s32 = (speech_probability[0] & 0xFFFFF000) << 2; // Q29 sgprvec[channel] = (int16_t) WebRtcSpl_DivW32W16(tmp1_s32, h1); // Q14 sgprvec[channel + kNumChannels] = 16384 - sgprvec[channel]; } } // Make a global VAD decision. vadflag |= (sum_log_likelihood_ratios >= totalTest); // Update the model parameters. maxspe = 12800; for (channel = 0; channel < kNumChannels; channel++) { // Get minimum value in past which is used for long term correction in Q4. feature_minimum = WebRtcVad_FindMinimum(self, features[channel], channel); // Compute the "global" mean, that is the sum of the two means weighted. noise_global_mean = WeightedAverage(&self->noise_means[channel], 0, &kNoiseDataWeights[channel]); tmp1_s16 = (int16_t) (noise_global_mean >> 6); // Q8 for (k = 0; k < kNumGaussians; k++) { gaussian = channel + k * kNumChannels; nmk = self->noise_means[gaussian]; smk = self->speech_means[gaussian]; nsk = self->noise_stds[gaussian]; ssk = self->speech_stds[gaussian]; // Update noise mean vector if the frame consists of noise only. nmk2 = nmk; if (!vadflag) { // deltaN = (x-mu)/sigma^2 // ngprvec[k] = `noise_probability[k]` / // (`noise_probability[0]` + `noise_probability[1]`) // (Q14 * Q11 >> 11) = Q14. delt = (int16_t)((ngprvec[gaussian] * deltaN[gaussian]) >> 11); // Q7 + (Q14 * Q15 >> 22) = Q7. nmk2 = nmk + (int16_t)((delt * kNoiseUpdateConst) >> 22); } // Long term correction of the noise mean. // Q8 - Q8 = Q8. ndelt = (feature_minimum << 4) - tmp1_s16; // Q7 + (Q8 * Q8) >> 9 = Q7. nmk3 = nmk2 + (int16_t)((ndelt * kBackEta) >> 9); // Control that the noise mean does not drift to much. tmp_s16 = (int16_t) ((k + 5) << 7); if (nmk3 < tmp_s16) { nmk3 = tmp_s16; } tmp_s16 = (int16_t) ((72 + k - channel) << 7); if (nmk3 > tmp_s16) { nmk3 = tmp_s16; } self->noise_means[gaussian] = nmk3; if (vadflag) { // Update speech mean vector: // `deltaS` = (x-mu)/sigma^2 // sgprvec[k] = `speech_probability[k]` / // (`speech_probability[0]` + `speech_probability[1]`) // (Q14 * Q11) >> 11 = Q14. delt = (int16_t)((sgprvec[gaussian] * deltaS[gaussian]) >> 11); // Q14 * Q15 >> 21 = Q8. tmp_s16 = (int16_t)((delt * kSpeechUpdateConst) >> 21); // Q7 + (Q8 >> 1) = Q7. With rounding. smk2 = smk + ((tmp_s16 + 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; } self->speech_means[gaussian] = smk2; // Q7. // (Q7 >> 3) = Q4. With rounding. tmp_s16 = ((smk + 4) >> 3); tmp_s16 = features[channel] - tmp_s16; // Q4 // (Q11 * Q4 >> 3) = Q12. tmp1_s32 = (deltaS[gaussian] * tmp_s16) >> 3; tmp2_s32 = tmp1_s32 - 4096; tmp_s16 = sgprvec[gaussian] >> 2; // (Q14 >> 2) * Q12 = Q24. tmp1_s32 = tmp_s16 * tmp2_s32; tmp2_s32 = tmp1_s32 >> 4; // Q20 // 0.1 * Q20 / Q7 = Q13. if (tmp2_s32 > 0) { tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(tmp2_s32, ssk * 10); } else { tmp_s16 = (int16_t) WebRtcSpl_DivW32W16(-tmp2_s32, ssk * 10); tmp_s16 = -tmp_s16; } // 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. tmp_s16 += 128; // Rounding. ssk += (tmp_s16 >> 8); if (ssk < kMinStd) { ssk = kMinStd; } self->speech_stds[gaussian] = ssk; } else { // Update GMM variance vectors. // deltaN * (features[channel] - nmk) - 1 // Q4 - (Q7 >> 3) = Q4. tmp_s16 = features[channel] - (nmk >> 3); // (Q11 * Q4 >> 3) = Q12. tmp1_s32 = (deltaN[gaussian] * tmp_s16) >> 3; tmp1_s32 -= 4096; // (Q14 >> 2) * Q12 = Q24. tmp_s16 = (ngprvec[gaussian] + 2) >> 2; tmp2_s32 = OverflowingMulS16ByS32ToS32(tmp_s16, tmp1_s32); // Q20 * approx 0.001 (2^-10=0.0009766), hence, // (Q24 >> 14) = (Q24 >> 4) / 2^10 = Q20. tmp1_s32 = tmp2_s32 >> 14; // Q20 / Q7 = Q13. if (tmp1_s32 > 0) { 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; }