| /* |
| * 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/include/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 (size_t 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 |