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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/modules/audio_processing/vad/pitch_based_vad.h"
#include <assert.h>
#include <math.h>
#include <string.h>
#include "webrtc/modules/audio_processing/vad/vad_circular_buffer.h"
#include "webrtc/modules/audio_processing/vad/common.h"
#include "webrtc/modules/audio_processing/vad/noise_gmm_tables.h"
#include "webrtc/modules/audio_processing/vad/voice_gmm_tables.h"
#include "webrtc/modules/interface/module_common_types.h"
namespace webrtc {
static_assert(kNoiseGmmDim == kVoiceGmmDim,
"noise and voice gmm dimension not equal");
// These values should match MATLAB counterparts for unit-tests to pass.
static const int kPosteriorHistorySize = 500; // 5 sec of 10 ms frames.
static const double kInitialPriorProbability = 0.3;
static const int kTransientWidthThreshold = 7;
static const double kLowProbabilityThreshold = 0.2;
static double LimitProbability(double p) {
const double kLimHigh = 0.99;
const double kLimLow = 0.01;
if (p > kLimHigh)
p = kLimHigh;
else if (p < kLimLow)
p = kLimLow;
return p;
}
PitchBasedVad::PitchBasedVad()
: p_prior_(kInitialPriorProbability),
circular_buffer_(VadCircularBuffer::Create(kPosteriorHistorySize)) {
// Setup noise GMM.
noise_gmm_.dimension = kNoiseGmmDim;
noise_gmm_.num_mixtures = kNoiseGmmNumMixtures;
noise_gmm_.weight = kNoiseGmmWeights;
noise_gmm_.mean = &kNoiseGmmMean[0][0];
noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0];
// Setup voice GMM.
voice_gmm_.dimension = kVoiceGmmDim;
voice_gmm_.num_mixtures = kVoiceGmmNumMixtures;
voice_gmm_.weight = kVoiceGmmWeights;
voice_gmm_.mean = &kVoiceGmmMean[0][0];
voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0];
}
PitchBasedVad::~PitchBasedVad() {
}
int PitchBasedVad::VoicingProbability(const AudioFeatures& features,
double* p_combined) {
double p;
double gmm_features[3];
double pdf_features_given_voice;
double pdf_features_given_noise;
// These limits are the same in matlab implementation 'VoicingProbGMM().'
const double kLimLowLogPitchGain = -2.0;
const double kLimHighLogPitchGain = -0.9;
const double kLimLowSpectralPeak = 200;
const double kLimHighSpectralPeak = 2000;
const double kEps = 1e-12;
for (int n = 0; n < features.num_frames; n++) {
gmm_features[0] = features.log_pitch_gain[n];
gmm_features[1] = features.spectral_peak[n];
gmm_features[2] = features.pitch_lag_hz[n];
pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_);
pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_);
if (features.spectral_peak[n] < kLimLowSpectralPeak ||
features.spectral_peak[n] > kLimHighSpectralPeak ||
features.log_pitch_gain[n] < kLimLowLogPitchGain) {
pdf_features_given_voice = kEps * pdf_features_given_noise;
} else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) {
pdf_features_given_noise = kEps * pdf_features_given_voice;
}
p = p_prior_ * pdf_features_given_voice /
(pdf_features_given_voice * p_prior_ +
pdf_features_given_noise * (1 - p_prior_));
p = LimitProbability(p);
// Combine pitch-based probability with standalone probability, before
// updating prior probabilities.
double prod_active = p * p_combined[n];
double prod_inactive = (1 - p) * (1 - p_combined[n]);
p_combined[n] = prod_active / (prod_active + prod_inactive);
if (UpdatePrior(p_combined[n]) < 0)
return -1;
// Limit prior probability. With a zero prior probability the posterior
// probability is always zero.
p_prior_ = LimitProbability(p_prior_);
}
return 0;
}
int PitchBasedVad::UpdatePrior(double p) {
circular_buffer_->Insert(p);
if (circular_buffer_->RemoveTransient(kTransientWidthThreshold,
kLowProbabilityThreshold) < 0)
return -1;
p_prior_ = circular_buffer_->Mean();
return 0;
}
} // namespace webrtc