xref: /external/webrtc/common_audio/vad/vad_core.c

/*
 *  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;
}