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Update common_audio

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Corresponds to upstream commit 524e9b043e7e86fd72353b987c9d5f6a1ebf83e1

Update notes:

 * Moved src/ to webrtc/ to easily diff against the third_party/webrtc
   in the chromium tree

 * ARM/NEON/MIPS support is not yet hooked up

 * Tests have not been copied
This commit is contained in:
Arun Raghavan
2015-10-13 12:16:16 +05:30
parent 9413986e79
commit c4fb4e38de
230 changed files with 11201 additions and 8656 deletions
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webrtc/common_audio/vad/include/vad.h Normal file
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/*
* Copyright (c) 2014 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.
*/
#ifndef WEBRTC_COMMON_AUDIO_VAD_INCLUDE_VAD_H_
#define WEBRTC_COMMON_AUDIO_VAD_INCLUDE_VAD_H_
#include "webrtc/base/checks.h"
#include "webrtc/base/scoped_ptr.h"
#include "webrtc/common_audio/vad/include/webrtc_vad.h"
#include "webrtc/typedefs.h"
namespace webrtc {
class Vad {
public:
enum Aggressiveness {
kVadNormal = 0,
kVadLowBitrate = 1,
kVadAggressive = 2,
kVadVeryAggressive = 3
};
enum Activity { kPassive = 0, kActive = 1, kError = -1 };
virtual ~Vad() = default;
// Calculates a VAD decision for the given audio frame. Valid sample rates
// are 8000, 16000, and 32000 Hz; the number of samples must be such that the
// frame is 10, 20, or 30 ms long.
virtual Activity VoiceActivity(const int16_t* audio,
size_t num_samples,
int sample_rate_hz) = 0;
// Resets VAD state.
virtual void Reset() = 0;
};
// Returns a Vad instance that's implemented on top of WebRtcVad.
rtc::scoped_ptr<Vad> CreateVad(Vad::Aggressiveness aggressiveness);
} // namespace webrtc
#endif // WEBRTC_COMMON_AUDIO_VAD_INCLUDE_VAD_H_

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webrtc/common_audio/vad/include/webrtc_vad.h Normal file
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/*
* 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.
*/
/*
* This header file includes the VAD API calls. Specific function calls are given below.
*/
#ifndef WEBRTC_COMMON_AUDIO_VAD_INCLUDE_WEBRTC_VAD_H_ // NOLINT
#define WEBRTC_COMMON_AUDIO_VAD_INCLUDE_WEBRTC_VAD_H_
#include <stddef.h>
#include "webrtc/typedefs.h"
typedef struct WebRtcVadInst VadInst;
#ifdef __cplusplus
extern "C" {
#endif
// Creates an instance to the VAD structure.
VadInst* WebRtcVad_Create();
// Frees the dynamic memory of a specified VAD instance.
//
// - handle [i] : Pointer to VAD instance that should be freed.
void WebRtcVad_Free(VadInst* handle);
// Initializes a VAD instance.
//
// - handle [i/o] : Instance that should be initialized.
//
// returns : 0 - (OK),
// -1 - (NULL pointer or Default mode could not be set).
int WebRtcVad_Init(VadInst* handle);
// Sets the VAD operating mode. A more aggressive (higher mode) VAD is more
// restrictive in reporting speech. Put in other words the probability of being
// speech when the VAD returns 1 is increased with increasing mode. As a
// consequence also the missed detection rate goes up.
//
// - handle [i/o] : VAD instance.
// - mode [i] : Aggressiveness mode (0, 1, 2, or 3).
//
// returns : 0 - (OK),
// -1 - (NULL pointer, mode could not be set or the VAD instance
// has not been initialized).
int WebRtcVad_set_mode(VadInst* handle, int mode);
// Calculates a VAD decision for the |audio_frame|. For valid sampling rates
// frame lengths, see the description of WebRtcVad_ValidRatesAndFrameLengths().
//
// - handle [i/o] : VAD Instance. Needs to be initialized by
// WebRtcVad_Init() before call.
// - fs [i] : Sampling frequency (Hz): 8000, 16000, or 32000
// - audio_frame [i] : Audio frame buffer.
// - frame_length [i] : Length of audio frame buffer in number of samples.
//
// returns : 1 - (Active Voice),
// 0 - (Non-active Voice),
// -1 - (Error)
int WebRtcVad_Process(VadInst* handle, int fs, const int16_t* audio_frame,
size_t frame_length);
// Checks for valid combinations of |rate| and |frame_length|. We support 10,
// 20 and 30 ms frames and the rates 8000, 16000 and 32000 Hz.
//
// - rate [i] : Sampling frequency (Hz).
// - frame_length [i] : Speech frame buffer length in number of samples.
//
// returns : 0 - (valid combination), -1 - (invalid combination)
int WebRtcVad_ValidRateAndFrameLength(int rate, size_t frame_length);
#ifdef __cplusplus
}
#endif
#endif // WEBRTC_COMMON_AUDIO_VAD_INCLUDE_WEBRTC_VAD_H_ // NOLINT

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webrtc/common_audio/vad/vad.cc Normal file
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/*
* Copyright (c) 2014 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 "webrtc/common_audio/vad/include/vad.h"
#include "webrtc/base/checks.h"
namespace webrtc {
namespace {
class VadImpl final : public Vad {
public:
explicit VadImpl(Aggressiveness aggressiveness)
: handle_(nullptr), aggressiveness_(aggressiveness) {
Reset();
}
~VadImpl() override { WebRtcVad_Free(handle_); }
Activity VoiceActivity(const int16_t* audio,
size_t num_samples,
int sample_rate_hz) override {
int ret = WebRtcVad_Process(handle_, sample_rate_hz, audio, num_samples);
switch (ret) {
case 0:
return kPassive;
case 1:
return kActive;
default:
RTC_DCHECK(false) << "WebRtcVad_Process returned an error.";
return kError;
}
}
void Reset() override {
if (handle_)
WebRtcVad_Free(handle_);
handle_ = WebRtcVad_Create();
RTC_CHECK(handle_);
RTC_CHECK_EQ(WebRtcVad_Init(handle_), 0);
RTC_CHECK_EQ(WebRtcVad_set_mode(handle_, aggressiveness_), 0);
}
private:
VadInst* handle_;
Aggressiveness aggressiveness_;
};
} // namespace
rtc::scoped_ptr<Vad> CreateVad(Vad::Aggressiveness aggressiveness) {
return rtc::scoped_ptr<Vad>(new VadImpl(aggressiveness));
}
} // namespace webrtc

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webrtc/common_audio/vad/vad_core.c Normal file
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/*
* 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 "webrtc/common_audio/vad/vad_core.h"
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/common_audio/vad/vad_filterbank.h"
#include "webrtc/common_audio/vad/vad_gmm.h"
#include "webrtc/common_audio/vad/vad_sp.h"
#include "webrtc/typedefs.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;
}
// 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 << 2) > 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 = 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;
}

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/*
* 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.
*/
/*
* This header file includes the descriptions of the core VAD calls.
*/
#ifndef WEBRTC_COMMON_AUDIO_VAD_VAD_CORE_H_
#define WEBRTC_COMMON_AUDIO_VAD_VAD_CORE_H_
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/typedefs.h"
enum { kNumChannels = 6 }; // Number of frequency bands (named channels).
enum { kNumGaussians = 2 }; // Number of Gaussians per channel in the GMM.
enum { kTableSize = kNumChannels * kNumGaussians };
enum { kMinEnergy = 10 }; // Minimum energy required to trigger audio signal.
typedef struct VadInstT_
{
int vad;
int32_t downsampling_filter_states[4];
WebRtcSpl_State48khzTo8khz state_48_to_8;
int16_t noise_means[kTableSize];
int16_t speech_means[kTableSize];
int16_t noise_stds[kTableSize];
int16_t speech_stds[kTableSize];
// TODO(bjornv): Change to |frame_count|.
int32_t frame_counter;
int16_t over_hang; // Over Hang
int16_t num_of_speech;
// TODO(bjornv): Change to |age_vector|.
int16_t index_vector[16 * kNumChannels];
int16_t low_value_vector[16 * kNumChannels];
// TODO(bjornv): Change to |median|.
int16_t mean_value[kNumChannels];
int16_t upper_state[5];
int16_t lower_state[5];
int16_t hp_filter_state[4];
int16_t over_hang_max_1[3];
int16_t over_hang_max_2[3];
int16_t individual[3];
int16_t total[3];
int init_flag;
} VadInstT;
// Initializes the core VAD component. The default aggressiveness mode is
// controlled by |kDefaultMode| in vad_core.c.
//
// - self [i/o] : Instance that should be initialized
//
// returns : 0 (OK), -1 (NULL pointer in or if the default mode can't be
// set)
int WebRtcVad_InitCore(VadInstT* self);
/****************************************************************************
* WebRtcVad_set_mode_core(...)
*
* This function changes the VAD settings
*
* Input:
* - inst : VAD instance
* - mode : Aggressiveness degree
* 0 (High quality) - 3 (Highly aggressive)
*
* Output:
* - inst : Changed instance
*
* Return value : 0 - Ok
* -1 - Error
*/
int WebRtcVad_set_mode_core(VadInstT* self, int mode);
/****************************************************************************
* WebRtcVad_CalcVad48khz(...)
* WebRtcVad_CalcVad32khz(...)
* WebRtcVad_CalcVad16khz(...)
* WebRtcVad_CalcVad8khz(...)
*
* Calculate probability for active speech and make VAD decision.
*
* Input:
* - inst : Instance that should be initialized
* - speech_frame : Input speech frame
* - frame_length : Number of input samples
*
* Output:
* - inst : Updated filter states etc.
*
* Return value : VAD decision
* 0 - No active speech
* 1-6 - Active speech
*/
int WebRtcVad_CalcVad48khz(VadInstT* inst, const int16_t* speech_frame,
size_t frame_length);
int WebRtcVad_CalcVad32khz(VadInstT* inst, const int16_t* speech_frame,
size_t frame_length);
int WebRtcVad_CalcVad16khz(VadInstT* inst, const int16_t* speech_frame,
size_t frame_length);
int WebRtcVad_CalcVad8khz(VadInstT* inst, const int16_t* speech_frame,
size_t frame_length);
#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_CORE_H_

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/*
* Copyright (c) 2011 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.
*/
/*
* This header file includes the macros used in VAD.
*/
#ifndef WEBRTC_VAD_DEFINES_H_
#define WEBRTC_VAD_DEFINES_H_
#define NUM_CHANNELS 6 // Eight frequency bands
#define NUM_MODELS 2 // Number of Gaussian models
#define NUM_TABLE_VALUES NUM_CHANNELS * NUM_MODELS
#define MIN_ENERGY 10
#define ALPHA1 6553 // 0.2 in Q15
#define ALPHA2 32439 // 0.99 in Q15
#define NSP_MAX 6 // Maximum number of VAD=1 frames in a row counted
#define MIN_STD 384 // Minimum standard deviation
// Mode 0, Quality thresholds - Different thresholds for the different frame lengths
#define INDIVIDUAL_10MS_Q 24
#define INDIVIDUAL_20MS_Q 21 // (log10(2)*66)<<2 ~=16
#define INDIVIDUAL_30MS_Q 24
#define TOTAL_10MS_Q 57
#define TOTAL_20MS_Q 48
#define TOTAL_30MS_Q 57
#define OHMAX1_10MS_Q 8 // Max Overhang 1
#define OHMAX2_10MS_Q 14 // Max Overhang 2
#define OHMAX1_20MS_Q 4 // Max Overhang 1
#define OHMAX2_20MS_Q 7 // Max Overhang 2
#define OHMAX1_30MS_Q 3
#define OHMAX2_30MS_Q 5
// Mode 1, Low bitrate thresholds - Different thresholds for the different frame lengths
#define INDIVIDUAL_10MS_LBR 37
#define INDIVIDUAL_20MS_LBR 32
#define INDIVIDUAL_30MS_LBR 37
#define TOTAL_10MS_LBR 100
#define TOTAL_20MS_LBR 80
#define TOTAL_30MS_LBR 100
#define OHMAX1_10MS_LBR 8 // Max Overhang 1
#define OHMAX2_10MS_LBR 14 // Max Overhang 2
#define OHMAX1_20MS_LBR 4
#define OHMAX2_20MS_LBR 7
#define OHMAX1_30MS_LBR 3
#define OHMAX2_30MS_LBR 5
// Mode 2, Very aggressive thresholds - Different thresholds for the different frame lengths
#define INDIVIDUAL_10MS_AGG 82
#define INDIVIDUAL_20MS_AGG 78
#define INDIVIDUAL_30MS_AGG 82
#define TOTAL_10MS_AGG 285 //580
#define TOTAL_20MS_AGG 260
#define TOTAL_30MS_AGG 285
#define OHMAX1_10MS_AGG 6 // Max Overhang 1
#define OHMAX2_10MS_AGG 9 // Max Overhang 2
#define OHMAX1_20MS_AGG 3
#define OHMAX2_20MS_AGG 5
#define OHMAX1_30MS_AGG 2
#define OHMAX2_30MS_AGG 3
// Mode 3, Super aggressive thresholds - Different thresholds for the different frame lengths
#define INDIVIDUAL_10MS_VAG 94
#define INDIVIDUAL_20MS_VAG 94
#define INDIVIDUAL_30MS_VAG 94
#define TOTAL_10MS_VAG 1100 //1700
#define TOTAL_20MS_VAG 1050
#define TOTAL_30MS_VAG 1100
#define OHMAX1_10MS_VAG 6 // Max Overhang 1
#define OHMAX2_10MS_VAG 9 // Max Overhang 2
#define OHMAX1_20MS_VAG 3
#define OHMAX2_20MS_VAG 5
#define OHMAX1_30MS_VAG 2
#define OHMAX2_30MS_VAG 3
#endif // WEBRTC_VAD_DEFINES_H_

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/*
* 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 "webrtc/common_audio/vad/vad_filterbank.h"
#include <assert.h>
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/typedefs.h"
// Constants used in LogOfEnergy().
static const int16_t kLogConst = 24660; // 160*log10(2) in Q9.
static const int16_t kLogEnergyIntPart = 14336; // 14 in Q10
// Coefficients used by HighPassFilter, Q14.
static const int16_t kHpZeroCoefs[3] = { 6631, -13262, 6631 };
static const int16_t kHpPoleCoefs[3] = { 16384, -7756, 5620 };
// Allpass filter coefficients, upper and lower, in Q15.
// Upper: 0.64, Lower: 0.17
static const int16_t kAllPassCoefsQ15[2] = { 20972, 5571 };
// Adjustment for division with two in SplitFilter.
static const int16_t kOffsetVector[6] = { 368, 368, 272, 176, 176, 176 };
// High pass filtering, with a cut-off frequency at 80 Hz, if the |data_in| is
// sampled at 500 Hz.
//
// - data_in [i] : Input audio data sampled at 500 Hz.
// - data_length [i] : Length of input and output data.
// - filter_state [i/o] : State of the filter.
// - data_out [o] : Output audio data in the frequency interval
// 80 - 250 Hz.
static void HighPassFilter(const int16_t* data_in, size_t data_length,
int16_t* filter_state, int16_t* data_out) {
size_t i;
const int16_t* in_ptr = data_in;
int16_t* out_ptr = data_out;
int32_t tmp32 = 0;
// The sum of the absolute values of the impulse response:
// The zero/pole-filter has a max amplification of a single sample of: 1.4546
// Impulse response: 0.4047 -0.6179 -0.0266 0.1993 0.1035 -0.0194
// The all-zero section has a max amplification of a single sample of: 1.6189
// Impulse response: 0.4047 -0.8094 0.4047 0 0 0
// The all-pole section has a max amplification of a single sample of: 1.9931
// Impulse response: 1.0000 0.4734 -0.1189 -0.2187 -0.0627 0.04532
for (i = 0; i < data_length; i++) {
// All-zero section (filter coefficients in Q14).
tmp32 = kHpZeroCoefs[0] * *in_ptr;
tmp32 += kHpZeroCoefs[1] * filter_state[0];
tmp32 += kHpZeroCoefs[2] * filter_state[1];
filter_state[1] = filter_state[0];
filter_state[0] = *in_ptr++;
// All-pole section (filter coefficients in Q14).
tmp32 -= kHpPoleCoefs[1] * filter_state[2];
tmp32 -= kHpPoleCoefs[2] * filter_state[3];
filter_state[3] = filter_state[2];
filter_state[2] = (int16_t) (tmp32 >> 14);
*out_ptr++ = filter_state[2];
}
}
// All pass filtering of |data_in|, used before splitting the signal into two
// frequency bands (low pass vs high pass).
// Note that |data_in| and |data_out| can NOT correspond to the same address.
//
// - data_in [i] : Input audio signal given in Q0.
// - data_length [i] : Length of input and output data.
// - filter_coefficient [i] : Given in Q15.
// - filter_state [i/o] : State of the filter given in Q(-1).
// - data_out [o] : Output audio signal given in Q(-1).
static void AllPassFilter(const int16_t* data_in, size_t data_length,
int16_t filter_coefficient, int16_t* filter_state,
int16_t* data_out) {
// The filter can only cause overflow (in the w16 output variable)
// if more than 4 consecutive input numbers are of maximum value and
// has the the same sign as the impulse responses first taps.
// First 6 taps of the impulse response:
// 0.6399 0.5905 -0.3779 0.2418 -0.1547 0.0990
size_t i;
int16_t tmp16 = 0;
int32_t tmp32 = 0;
int32_t state32 = ((int32_t) (*filter_state) << 16); // Q15
for (i = 0; i < data_length; i++) {
tmp32 = state32 + filter_coefficient * *data_in;
tmp16 = (int16_t) (tmp32 >> 16); // Q(-1)
*data_out++ = tmp16;
state32 = (*data_in << 14) - filter_coefficient * tmp16; // Q14
state32 <<= 1; // Q15.
data_in += 2;
}
*filter_state = (int16_t) (state32 >> 16); // Q(-1)
}
// Splits |data_in| into |hp_data_out| and |lp_data_out| corresponding to
// an upper (high pass) part and a lower (low pass) part respectively.
//
// - data_in [i] : Input audio data to be split into two frequency bands.
// - data_length [i] : Length of |data_in|.
// - upper_state [i/o] : State of the upper filter, given in Q(-1).
// - lower_state [i/o] : State of the lower filter, given in Q(-1).
// - hp_data_out [o] : Output audio data of the upper half of the spectrum.
// The length is |data_length| / 2.
// - lp_data_out [o] : Output audio data of the lower half of the spectrum.
// The length is |data_length| / 2.
static void SplitFilter(const int16_t* data_in, size_t data_length,
int16_t* upper_state, int16_t* lower_state,
int16_t* hp_data_out, int16_t* lp_data_out) {
size_t i;
size_t half_length = data_length >> 1; // Downsampling by 2.
int16_t tmp_out;
// All-pass filtering upper branch.
AllPassFilter(&data_in[0], half_length, kAllPassCoefsQ15[0], upper_state,
hp_data_out);
// All-pass filtering lower branch.
AllPassFilter(&data_in[1], half_length, kAllPassCoefsQ15[1], lower_state,
lp_data_out);
// Make LP and HP signals.
for (i = 0; i < half_length; i++) {
tmp_out = *hp_data_out;
*hp_data_out++ -= *lp_data_out;
*lp_data_out++ += tmp_out;
}
}
// Calculates the energy of |data_in| in dB, and also updates an overall
// |total_energy| if necessary.
//
// - data_in [i] : Input audio data for energy calculation.
// - data_length [i] : Length of input data.
// - offset [i] : Offset value added to |log_energy|.
// - total_energy [i/o] : An external energy updated with the energy of
// |data_in|.
// NOTE: |total_energy| is only updated if
// |total_energy| <= |kMinEnergy|.
// - log_energy [o] : 10 * log10("energy of |data_in|") given in Q4.
static void LogOfEnergy(const int16_t* data_in, size_t data_length,
int16_t offset, int16_t* total_energy,
int16_t* log_energy) {
// |tot_rshifts| accumulates the number of right shifts performed on |energy|.
int tot_rshifts = 0;
// The |energy| will be normalized to 15 bits. We use unsigned integer because
// we eventually will mask out the fractional part.
uint32_t energy = 0;
assert(data_in != NULL);
assert(data_length > 0);
energy = (uint32_t) WebRtcSpl_Energy((int16_t*) data_in, data_length,
&tot_rshifts);
if (energy != 0) {
// By construction, normalizing to 15 bits is equivalent with 17 leading
// zeros of an unsigned 32 bit value.
int normalizing_rshifts = 17 - WebRtcSpl_NormU32(energy);
// In a 15 bit representation the leading bit is 2^14. log2(2^14) in Q10 is
// (14 << 10), which is what we initialize |log2_energy| with. For a more
// detailed derivations, see below.
int16_t log2_energy = kLogEnergyIntPart;
tot_rshifts += normalizing_rshifts;
// Normalize |energy| to 15 bits.
// |tot_rshifts| is now the total number of right shifts performed on
// |energy| after normalization. This means that |energy| is in
// Q(-tot_rshifts).
if (normalizing_rshifts < 0) {
energy <<= -normalizing_rshifts;
} else {
energy >>= normalizing_rshifts;
}
// Calculate the energy of |data_in| in dB, in Q4.
//
// 10 * log10("true energy") in Q4 = 2^4 * 10 * log10("true energy") =
// 160 * log10(|energy| * 2^|tot_rshifts|) =
// 160 * log10(2) * log2(|energy| * 2^|tot_rshifts|) =
// 160 * log10(2) * (log2(|energy|) + log2(2^|tot_rshifts|)) =
// (160 * log10(2)) * (log2(|energy|) + |tot_rshifts|) =
// |kLogConst| * (|log2_energy| + |tot_rshifts|)
//
// We know by construction that |energy| is normalized to 15 bits. Hence,
// |energy| = 2^14 + frac_Q15, where frac_Q15 is a fractional part in Q15.
// Further, we'd like |log2_energy| in Q10
// log2(|energy|) in Q10 = 2^10 * log2(2^14 + frac_Q15) =
// 2^10 * log2(2^14 * (1 + frac_Q15 * 2^-14)) =
// 2^10 * (14 + log2(1 + frac_Q15 * 2^-14)) ~=
// (14 << 10) + 2^10 * (frac_Q15 * 2^-14) =
// (14 << 10) + (frac_Q15 * 2^-4) = (14 << 10) + (frac_Q15 >> 4)
//
// Note that frac_Q15 = (|energy| & 0x00003FFF)
// Calculate and add the fractional part to |log2_energy|.
log2_energy += (int16_t) ((energy & 0x00003FFF) >> 4);
// |kLogConst| is in Q9, |log2_energy| in Q10 and |tot_rshifts| in Q0.
// Note that we in our derivation above have accounted for an output in Q4.
*log_energy = (int16_t)(((kLogConst * log2_energy) >> 19) +
((tot_rshifts * kLogConst) >> 9));
if (*log_energy < 0) {
*log_energy = 0;
}
} else {
*log_energy = offset;
return;
}
*log_energy += offset;
// Update the approximate |total_energy| with the energy of |data_in|, if
// |total_energy| has not exceeded |kMinEnergy|. |total_energy| is used as an
// energy indicator in WebRtcVad_GmmProbability() in vad_core.c.
if (*total_energy <= kMinEnergy) {
if (tot_rshifts >= 0) {
// We know by construction that the |energy| > |kMinEnergy| in Q0, so add
// an arbitrary value such that |total_energy| exceeds |kMinEnergy|.
*total_energy += kMinEnergy + 1;
} else {
// By construction |energy| is represented by 15 bits, hence any number of
// right shifted |energy| will fit in an int16_t. In addition, adding the
// value to |total_energy| is wrap around safe as long as
// |kMinEnergy| < 8192.
*total_energy += (int16_t) (energy >> -tot_rshifts); // Q0.
}
}
}
int16_t WebRtcVad_CalculateFeatures(VadInstT* self, const int16_t* data_in,
size_t data_length, int16_t* features) {
int16_t total_energy = 0;
// We expect |data_length| to be 80, 160 or 240 samples, which corresponds to
// 10, 20 or 30 ms in 8 kHz. Therefore, the intermediate downsampled data will
// have at most 120 samples after the first split and at most 60 samples after
// the second split.
int16_t hp_120[120], lp_120[120];
int16_t hp_60[60], lp_60[60];
const size_t half_data_length = data_length >> 1;
size_t length = half_data_length; // |data_length| / 2, corresponds to
// bandwidth = 2000 Hz after downsampling.
// Initialize variables for the first SplitFilter().
int frequency_band = 0;
const int16_t* in_ptr = data_in; // [0 - 4000] Hz.
int16_t* hp_out_ptr = hp_120; // [2000 - 4000] Hz.
int16_t* lp_out_ptr = lp_120; // [0 - 2000] Hz.
assert(data_length <= 240);
assert(4 < kNumChannels - 1); // Checking maximum |frequency_band|.
// Split at 2000 Hz and downsample.
SplitFilter(in_ptr, data_length, &self->upper_state[frequency_band],
&self->lower_state[frequency_band], hp_out_ptr, lp_out_ptr);
// For the upper band (2000 Hz - 4000 Hz) split at 3000 Hz and downsample.
frequency_band = 1;
in_ptr = hp_120; // [2000 - 4000] Hz.
hp_out_ptr = hp_60; // [3000 - 4000] Hz.
lp_out_ptr = lp_60; // [2000 - 3000] Hz.
SplitFilter(in_ptr, length, &self->upper_state[frequency_band],
&self->lower_state[frequency_band], hp_out_ptr, lp_out_ptr);
// Energy in 3000 Hz - 4000 Hz.
length >>= 1; // |data_length| / 4 <=> bandwidth = 1000 Hz.
LogOfEnergy(hp_60, length, kOffsetVector[5], &total_energy, &features[5]);
// Energy in 2000 Hz - 3000 Hz.
LogOfEnergy(lp_60, length, kOffsetVector[4], &total_energy, &features[4]);
// For the lower band (0 Hz - 2000 Hz) split at 1000 Hz and downsample.
frequency_band = 2;
in_ptr = lp_120; // [0 - 2000] Hz.
hp_out_ptr = hp_60; // [1000 - 2000] Hz.
lp_out_ptr = lp_60; // [0 - 1000] Hz.
length = half_data_length; // |data_length| / 2 <=> bandwidth = 2000 Hz.
SplitFilter(in_ptr, length, &self->upper_state[frequency_band],
&self->lower_state[frequency_band], hp_out_ptr, lp_out_ptr);
// Energy in 1000 Hz - 2000 Hz.
length >>= 1; // |data_length| / 4 <=> bandwidth = 1000 Hz.
LogOfEnergy(hp_60, length, kOffsetVector[3], &total_energy, &features[3]);
// For the lower band (0 Hz - 1000 Hz) split at 500 Hz and downsample.
frequency_band = 3;
in_ptr = lp_60; // [0 - 1000] Hz.
hp_out_ptr = hp_120; // [500 - 1000] Hz.
lp_out_ptr = lp_120; // [0 - 500] Hz.
SplitFilter(in_ptr, length, &self->upper_state[frequency_band],
&self->lower_state[frequency_band], hp_out_ptr, lp_out_ptr);
// Energy in 500 Hz - 1000 Hz.
length >>= 1; // |data_length| / 8 <=> bandwidth = 500 Hz.
LogOfEnergy(hp_120, length, kOffsetVector[2], &total_energy, &features[2]);
// For the lower band (0 Hz - 500 Hz) split at 250 Hz and downsample.
frequency_band = 4;
in_ptr = lp_120; // [0 - 500] Hz.
hp_out_ptr = hp_60; // [250 - 500] Hz.
lp_out_ptr = lp_60; // [0 - 250] Hz.
SplitFilter(in_ptr, length, &self->upper_state[frequency_band],
&self->lower_state[frequency_band], hp_out_ptr, lp_out_ptr);
// Energy in 250 Hz - 500 Hz.
length >>= 1; // |data_length| / 16 <=> bandwidth = 250 Hz.
LogOfEnergy(hp_60, length, kOffsetVector[1], &total_energy, &features[1]);
// Remove 0 Hz - 80 Hz, by high pass filtering the lower band.
HighPassFilter(lp_60, length, self->hp_filter_state, hp_120);
// Energy in 80 Hz - 250 Hz.
LogOfEnergy(hp_120, length, kOffsetVector[0], &total_energy, &features[0]);
return total_energy;
}

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/*
* 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.
*/
/*
* This file includes feature calculating functionality used in vad_core.c.
*/
#ifndef WEBRTC_COMMON_AUDIO_VAD_VAD_FILTERBANK_H_
#define WEBRTC_COMMON_AUDIO_VAD_VAD_FILTERBANK_H_
#include "webrtc/common_audio/vad/vad_core.h"
#include "webrtc/typedefs.h"
// Takes |data_length| samples of |data_in| and calculates the logarithm of the
// energy of each of the |kNumChannels| = 6 frequency bands used by the VAD:
// 80 Hz - 250 Hz
// 250 Hz - 500 Hz
// 500 Hz - 1000 Hz
// 1000 Hz - 2000 Hz
// 2000 Hz - 3000 Hz
// 3000 Hz - 4000 Hz
//
// The values are given in Q4 and written to |features|. Further, an approximate
// overall energy is returned. The return value is used in
// WebRtcVad_GmmProbability() as a signal indicator, hence it is arbitrary above
// the threshold |kMinEnergy|.
//
// - self [i/o] : State information of the VAD.
// - data_in [i] : Input audio data, for feature extraction.
// - data_length [i] : Audio data size, in number of samples.
// - features [o] : 10 * log10(energy in each frequency band), Q4.
// - returns : Total energy of the signal (NOTE! This value is not
// exact. It is only used in a comparison.)
int16_t WebRtcVad_CalculateFeatures(VadInstT* self, const int16_t* data_in,
size_t data_length, int16_t* features);
#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_FILTERBANK_H_

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/*
* Copyright (c) 2011 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 "webrtc/common_audio/vad/vad_gmm.h"
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/typedefs.h"
static const int32_t kCompVar = 22005;
static const int16_t kLog2Exp = 5909; // log2(exp(1)) in Q12.
// For a normal distribution, the probability of |input| is calculated and
// returned (in Q20). The formula for normal distributed probability is
//
// 1 / s * exp(-(x - m)^2 / (2 * s^2))
//
// where the parameters are given in the following Q domains:
// m = |mean| (Q7)
// s = |std| (Q7)
// x = |input| (Q4)
// in addition to the probability we output |delta| (in Q11) used when updating
// the noise/speech model.
int32_t WebRtcVad_GaussianProbability(int16_t input,
int16_t mean,
int16_t std,
int16_t* delta) {
int16_t tmp16, inv_std, inv_std2, exp_value = 0;
int32_t tmp32;
// Calculate |inv_std| = 1 / s, in Q10.
// 131072 = 1 in Q17, and (|std| >> 1) is for rounding instead of truncation.
// Q-domain: Q17 / Q7 = Q10.
tmp32 = (int32_t) 131072 + (int32_t) (std >> 1);
inv_std = (int16_t) WebRtcSpl_DivW32W16(tmp32, std);
// Calculate |inv_std2| = 1 / s^2, in Q14.
tmp16 = (inv_std >> 2); // Q10 -> Q8.
// Q-domain: (Q8 * Q8) >> 2 = Q14.
inv_std2 = (int16_t)((tmp16 * tmp16) >> 2);
// TODO(bjornv): Investigate if changing to
// inv_std2 = (int16_t)((inv_std * inv_std) >> 6);
// gives better accuracy.
tmp16 = (input << 3); // Q4 -> Q7
tmp16 = tmp16 - mean; // Q7 - Q7 = Q7
// To be used later, when updating noise/speech model.
// |delta| = (x - m) / s^2, in Q11.
// Q-domain: (Q14 * Q7) >> 10 = Q11.
*delta = (int16_t)((inv_std2 * tmp16) >> 10);
// Calculate the exponent |tmp32| = (x - m)^2 / (2 * s^2), in Q10. Replacing
// division by two with one shift.
// Q-domain: (Q11 * Q7) >> 8 = Q10.
tmp32 = (*delta * tmp16) >> 9;
// If the exponent is small enough to give a non-zero probability we calculate
// |exp_value| ~= exp(-(x - m)^2 / (2 * s^2))
// ~= exp2(-log2(exp(1)) * |tmp32|).
if (tmp32 < kCompVar) {
// Calculate |tmp16| = log2(exp(1)) * |tmp32|, in Q10.
// Q-domain: (Q12 * Q10) >> 12 = Q10.
tmp16 = (int16_t)((kLog2Exp * tmp32) >> 12);
tmp16 = -tmp16;
exp_value = (0x0400 | (tmp16 & 0x03FF));
tmp16 ^= 0xFFFF;
tmp16 >>= 10;
tmp16 += 1;
// Get |exp_value| = exp(-|tmp32|) in Q10.
exp_value >>= tmp16;
}
// Calculate and return (1 / s) * exp(-(x - m)^2 / (2 * s^2)), in Q20.
// Q-domain: Q10 * Q10 = Q20.
return inv_std * exp_value;
}

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/*
* Copyright (c) 2011 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.
*/
// Gaussian probability calculations internally used in vad_core.c.
#ifndef WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_
#define WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_
#include "webrtc/typedefs.h"
// Calculates the probability for |input|, given that |input| comes from a
// normal distribution with mean and standard deviation (|mean|, |std|).
//
// Inputs:
// - input : input sample in Q4.
// - mean : mean input in the statistical model, Q7.
// - std : standard deviation, Q7.
//
// Output:
//
// - delta : input used when updating the model, Q11.
// |delta| = (|input| - |mean|) / |std|^2.
//
// Return:
// (probability for |input|) =
// 1 / |std| * exp(-(|input| - |mean|)^2 / (2 * |std|^2));
int32_t WebRtcVad_GaussianProbability(int16_t input,
int16_t mean,
int16_t std,
int16_t* delta);
#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_

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/*
* 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 "webrtc/common_audio/vad/vad_sp.h"
#include <assert.h>
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/common_audio/vad/vad_core.h"
#include "webrtc/typedefs.h"
// Allpass filter coefficients, upper and lower, in Q13.
// Upper: 0.64, Lower: 0.17.
static const int16_t kAllPassCoefsQ13[2] = { 5243, 1392 }; // Q13.
static const int16_t kSmoothingDown = 6553; // 0.2 in Q15.
static const int16_t kSmoothingUp = 32439; // 0.99 in Q15.
// TODO(bjornv): Move this function to vad_filterbank.c.
// Downsampling filter based on splitting filter and allpass functions.
void WebRtcVad_Downsampling(const int16_t* signal_in,
int16_t* signal_out,
int32_t* filter_state,
size_t in_length) {
int16_t tmp16_1 = 0, tmp16_2 = 0;
int32_t tmp32_1 = filter_state[0];
int32_t tmp32_2 = filter_state[1];
size_t n = 0;
// Downsampling by 2 gives half length.
size_t half_length = (in_length >> 1);
// Filter coefficients in Q13, filter state in Q0.
for (n = 0; n < half_length; n++) {
// All-pass filtering upper branch.
tmp16_1 = (int16_t) ((tmp32_1 >> 1) +
((kAllPassCoefsQ13[0] * *signal_in) >> 14));
*signal_out = tmp16_1;
tmp32_1 = (int32_t)(*signal_in++) - ((kAllPassCoefsQ13[0] * tmp16_1) >> 12);
// All-pass filtering lower branch.
tmp16_2 = (int16_t) ((tmp32_2 >> 1) +
((kAllPassCoefsQ13[1] * *signal_in) >> 14));
*signal_out++ += tmp16_2;
tmp32_2 = (int32_t)(*signal_in++) - ((kAllPassCoefsQ13[1] * tmp16_2) >> 12);
}
// Store the filter states.
filter_state[0] = tmp32_1;
filter_state[1] = tmp32_2;
}
// Inserts |feature_value| into |low_value_vector|, if it is one of the 16
// smallest values the last 100 frames. Then calculates and returns the median
// of the five smallest values.
int16_t WebRtcVad_FindMinimum(VadInstT* self,
int16_t feature_value,
int channel) {
int i = 0, j = 0;
int position = -1;
// Offset to beginning of the 16 minimum values in memory.
const int offset = (channel << 4);
int16_t current_median = 1600;
int16_t alpha = 0;
int32_t tmp32 = 0;
// Pointer to memory for the 16 minimum values and the age of each value of
// the |channel|.
int16_t* age = &self->index_vector[offset];
int16_t* smallest_values = &self->low_value_vector[offset];
assert(channel < kNumChannels);
// Each value in |smallest_values| is getting 1 loop older. Update |age|, and
// remove old values.
for (i = 0; i < 16; i++) {
if (age[i] != 100) {
age[i]++;
} else {
// Too old value. Remove from memory and shift larger values downwards.
for (j = i; j < 16; j++) {
smallest_values[j] = smallest_values[j + 1];
age[j] = age[j + 1];
}
age[15] = 101;
smallest_values[15] = 10000;
}
}
// Check if |feature_value| is smaller than any of the values in
// |smallest_values|. If so, find the |position| where to insert the new value
// (|feature_value|).
if (feature_value < smallest_values[7]) {
if (feature_value < smallest_values[3]) {
if (feature_value < smallest_values[1]) {
if (feature_value < smallest_values[0]) {
position = 0;
} else {
position = 1;
}
} else if (feature_value < smallest_values[2]) {
position = 2;
} else {
position = 3;
}
} else if (feature_value < smallest_values[5]) {
if (feature_value < smallest_values[4]) {
position = 4;
} else {
position = 5;
}
} else if (feature_value < smallest_values[6]) {
position = 6;
} else {
position = 7;
}
} else if (feature_value < smallest_values[15]) {
if (feature_value < smallest_values[11]) {
if (feature_value < smallest_values[9]) {
if (feature_value < smallest_values[8]) {
position = 8;
} else {
position = 9;
}
} else if (feature_value < smallest_values[10]) {
position = 10;
} else {
position = 11;
}
} else if (feature_value < smallest_values[13]) {
if (feature_value < smallest_values[12]) {
position = 12;
} else {
position = 13;
}
} else if (feature_value < smallest_values[14]) {
position = 14;
} else {
position = 15;
}
}
// If we have detected a new small value, insert it at the correct position
// and shift larger values up.
if (position > -1) {
for (i = 15; i > position; i--) {
smallest_values[i] = smallest_values[i - 1];
age[i] = age[i - 1];
}
smallest_values[position] = feature_value;
age[position] = 1;
}
// Get |current_median|.
if (self->frame_counter > 2) {
current_median = smallest_values[2];
} else if (self->frame_counter > 0) {
current_median = smallest_values[0];
}
// Smooth the median value.
if (self->frame_counter > 0) {
if (current_median < self->mean_value[channel]) {
alpha = kSmoothingDown; // 0.2 in Q15.
} else {
alpha = kSmoothingUp; // 0.99 in Q15.
}
}
tmp32 = (alpha + 1) * self->mean_value[channel];
tmp32 += (WEBRTC_SPL_WORD16_MAX - alpha) * current_median;
tmp32 += 16384;
self->mean_value[channel] = (int16_t) (tmp32 >> 15);
return self->mean_value[channel];
}

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/*
* Copyright (c) 2011 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.
*/
// This file includes specific signal processing tools used in vad_core.c.
#ifndef WEBRTC_COMMON_AUDIO_VAD_VAD_SP_H_
#define WEBRTC_COMMON_AUDIO_VAD_VAD_SP_H_
#include "webrtc/common_audio/vad/vad_core.h"
#include "webrtc/typedefs.h"
// Downsamples the signal by a factor 2, eg. 32->16 or 16->8.
//
// Inputs:
// - signal_in : Input signal.
// - in_length : Length of input signal in samples.
//
// Input & Output:
// - filter_state : Current filter states of the two all-pass filters. The
// |filter_state| is updated after all samples have been
// processed.
//
// Output:
// - signal_out : Downsampled signal (of length |in_length| / 2).
void WebRtcVad_Downsampling(const int16_t* signal_in,
int16_t* signal_out,
int32_t* filter_state,
size_t in_length);
// Updates and returns the smoothed feature minimum. As minimum we use the
// median of the five smallest feature values in a 100 frames long window.
// As long as |handle->frame_counter| is zero, that is, we haven't received any
// "valid" data, FindMinimum() outputs the default value of 1600.
//
// Inputs:
// - feature_value : New feature value to update with.
// - channel : Channel number.
//
// Input & Output:
// - handle : State information of the VAD.
//
// Returns:
// : Smoothed minimum value for a moving window.
int16_t WebRtcVad_FindMinimum(VadInstT* handle,
int16_t feature_value,
int channel);
#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_SP_H_

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/*
* 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 "webrtc/common_audio/vad/include/webrtc_vad.h"
#include <stdlib.h>
#include <string.h>
#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
#include "webrtc/common_audio/vad/vad_core.h"
#include "webrtc/typedefs.h"
static const int kInitCheck = 42;
static const int kValidRates[] = { 8000, 16000, 32000, 48000 };
static const size_t kRatesSize = sizeof(kValidRates) / sizeof(*kValidRates);
static const int kMaxFrameLengthMs = 30;
VadInst* WebRtcVad_Create() {
VadInstT* self = (VadInstT*)malloc(sizeof(VadInstT));
WebRtcSpl_Init();
self->init_flag = 0;
return (VadInst*)self;
}
void WebRtcVad_Free(VadInst* handle) {
free(handle);
}
// TODO(bjornv): Move WebRtcVad_InitCore() code here.
int WebRtcVad_Init(VadInst* handle) {
// Initialize the core VAD component.
return WebRtcVad_InitCore((VadInstT*) handle);
}
// TODO(bjornv): Move WebRtcVad_set_mode_core() code here.
int WebRtcVad_set_mode(VadInst* handle, int mode) {
VadInstT* self = (VadInstT*) handle;
if (handle == NULL) {
return -1;
}
if (self->init_flag != kInitCheck) {
return -1;
}
return WebRtcVad_set_mode_core(self, mode);
}
int WebRtcVad_Process(VadInst* handle, int fs, const int16_t* audio_frame,
size_t frame_length) {
int vad = -1;
VadInstT* self = (VadInstT*) handle;
if (handle == NULL) {
return -1;
}
if (self->init_flag != kInitCheck) {
return -1;
}
if (audio_frame == NULL) {
return -1;
}
if (WebRtcVad_ValidRateAndFrameLength(fs, frame_length) != 0) {
return -1;
}
if (fs == 48000) {
vad = WebRtcVad_CalcVad48khz(self, audio_frame, frame_length);
} else if (fs == 32000) {
vad = WebRtcVad_CalcVad32khz(self, audio_frame, frame_length);
} else if (fs == 16000) {
vad = WebRtcVad_CalcVad16khz(self, audio_frame, frame_length);
} else if (fs == 8000) {
vad = WebRtcVad_CalcVad8khz(self, audio_frame, frame_length);
}
if (vad > 0) {
vad = 1;
}
return vad;
}
int WebRtcVad_ValidRateAndFrameLength(int rate, size_t frame_length) {
int return_value = -1;
size_t i;
int valid_length_ms;
size_t valid_length;
// We only allow 10, 20 or 30 ms frames. Loop through valid frame rates and
// see if we have a matching pair.
for (i = 0; i < kRatesSize; i++) {
if (kValidRates[i] == rate) {
for (valid_length_ms = 10; valid_length_ms <= kMaxFrameLengthMs;
valid_length_ms += 10) {
valid_length = (size_t)(kValidRates[i] / 1000 * valid_length_ms);
if (frame_length == valid_length) {
return_value = 0;
break;
}
}
break;
}
}
return return_value;
}