| /* |
| * 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; |
| } |
