121 lines
4.2 KiB
C++
121 lines
4.2 KiB
C++
/*
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* Copyright (c) 2012 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "modules/audio_processing/vad/pitch_based_vad.h"
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#include <string.h>
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#include "modules/audio_processing/vad/common.h"
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#include "modules/audio_processing/vad/noise_gmm_tables.h"
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#include "modules/audio_processing/vad/vad_circular_buffer.h"
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#include "modules/audio_processing/vad/voice_gmm_tables.h"
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namespace webrtc {
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static_assert(kNoiseGmmDim == kVoiceGmmDim,
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"noise and voice gmm dimension not equal");
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// These values should match MATLAB counterparts for unit-tests to pass.
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static const int kPosteriorHistorySize = 500; // 5 sec of 10 ms frames.
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static const double kInitialPriorProbability = 0.3;
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static const int kTransientWidthThreshold = 7;
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static const double kLowProbabilityThreshold = 0.2;
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static double LimitProbability(double p) {
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const double kLimHigh = 0.99;
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const double kLimLow = 0.01;
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if (p > kLimHigh)
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p = kLimHigh;
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else if (p < kLimLow)
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p = kLimLow;
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return p;
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}
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PitchBasedVad::PitchBasedVad()
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: p_prior_(kInitialPriorProbability),
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circular_buffer_(VadCircularBuffer::Create(kPosteriorHistorySize)) {
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// Setup noise GMM.
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noise_gmm_.dimension = kNoiseGmmDim;
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noise_gmm_.num_mixtures = kNoiseGmmNumMixtures;
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noise_gmm_.weight = kNoiseGmmWeights;
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noise_gmm_.mean = &kNoiseGmmMean[0][0];
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noise_gmm_.covar_inverse = &kNoiseGmmCovarInverse[0][0][0];
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// Setup voice GMM.
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voice_gmm_.dimension = kVoiceGmmDim;
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voice_gmm_.num_mixtures = kVoiceGmmNumMixtures;
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voice_gmm_.weight = kVoiceGmmWeights;
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voice_gmm_.mean = &kVoiceGmmMean[0][0];
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voice_gmm_.covar_inverse = &kVoiceGmmCovarInverse[0][0][0];
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}
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PitchBasedVad::~PitchBasedVad() {}
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int PitchBasedVad::VoicingProbability(const AudioFeatures& features,
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double* p_combined) {
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double p;
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double gmm_features[3];
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double pdf_features_given_voice;
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double pdf_features_given_noise;
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// These limits are the same in matlab implementation 'VoicingProbGMM().'
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const double kLimLowLogPitchGain = -2.0;
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const double kLimHighLogPitchGain = -0.9;
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const double kLimLowSpectralPeak = 200;
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const double kLimHighSpectralPeak = 2000;
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const double kEps = 1e-12;
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for (size_t n = 0; n < features.num_frames; n++) {
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gmm_features[0] = features.log_pitch_gain[n];
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gmm_features[1] = features.spectral_peak[n];
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gmm_features[2] = features.pitch_lag_hz[n];
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pdf_features_given_voice = EvaluateGmm(gmm_features, voice_gmm_);
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pdf_features_given_noise = EvaluateGmm(gmm_features, noise_gmm_);
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if (features.spectral_peak[n] < kLimLowSpectralPeak ||
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features.spectral_peak[n] > kLimHighSpectralPeak ||
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features.log_pitch_gain[n] < kLimLowLogPitchGain) {
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pdf_features_given_voice = kEps * pdf_features_given_noise;
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} else if (features.log_pitch_gain[n] > kLimHighLogPitchGain) {
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pdf_features_given_noise = kEps * pdf_features_given_voice;
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}
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p = p_prior_ * pdf_features_given_voice /
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(pdf_features_given_voice * p_prior_ +
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pdf_features_given_noise * (1 - p_prior_));
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p = LimitProbability(p);
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// Combine pitch-based probability with standalone probability, before
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// updating prior probabilities.
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double prod_active = p * p_combined[n];
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double prod_inactive = (1 - p) * (1 - p_combined[n]);
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p_combined[n] = prod_active / (prod_active + prod_inactive);
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if (UpdatePrior(p_combined[n]) < 0)
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return -1;
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// Limit prior probability. With a zero prior probability the posterior
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// probability is always zero.
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p_prior_ = LimitProbability(p_prior_);
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}
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return 0;
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}
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int PitchBasedVad::UpdatePrior(double p) {
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circular_buffer_->Insert(p);
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if (circular_buffer_->RemoveTransient(kTransientWidthThreshold,
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kLowProbabilityThreshold) < 0)
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return -1;
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p_prior_ = circular_buffer_->Mean();
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return 0;
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}
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} // namespace webrtc
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