mirror of
https://github.com/danog/libtgvoip.git
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5caaaafa42
I'm now using the entire audio processing module from WebRTC as opposed to individual DSP algorithms pulled from there before. Seems to work better this way.
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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