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/*
* Copyright (c) 2019 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 "modules/audio_processing/ns/speech_probability_estimator.h"
#include <math.h>
#include <algorithm>
#include "modules/audio_processing/ns/fast_math.h"
#include "rtc_base/checks.h"
namespace webrtc {
SpeechProbabilityEstimator::SpeechProbabilityEstimator() {
speech_probability_.fill(0.f);
}
void SpeechProbabilityEstimator::Update(
int32_t num_analyzed_frames,
ArrayView<const float, kFftSizeBy2Plus1> prior_snr,
ArrayView<const float, kFftSizeBy2Plus1> post_snr,
ArrayView<const float, kFftSizeBy2Plus1> conservative_noise_spectrum,
ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum,
float signal_spectral_sum,
float signal_energy) {
// Update models.
if (num_analyzed_frames < kLongStartupPhaseBlocks) {
signal_model_estimator_.AdjustNormalization(num_analyzed_frames,
signal_energy);
}
signal_model_estimator_.Update(prior_snr, post_snr,
conservative_noise_spectrum, signal_spectrum,
signal_spectral_sum, signal_energy);
const SignalModel& model = signal_model_estimator_.get_model();
const PriorSignalModel& prior_model =
signal_model_estimator_.get_prior_model();
// Width parameter in sigmoid map for prior model.
constexpr float kWidthPrior0 = 4.f;
// Width for pause region: lower range, so increase width in tanh map.
constexpr float kWidthPrior1 = 2.f * kWidthPrior0;
// Average LRT feature: use larger width in tanh map for pause regions.
float width_prior = model.lrt < prior_model.lrt ? kWidthPrior1 : kWidthPrior0;
// Compute indicator function: sigmoid map.
float indicator0 =
0.5f * (tanh(width_prior * (model.lrt - prior_model.lrt)) + 1.f);
// Spectral flatness feature: use larger width in tanh map for pause regions.
width_prior = model.spectral_flatness > prior_model.flatness_threshold
? kWidthPrior1
: kWidthPrior0;
// Compute indicator function: sigmoid map.
float indicator1 =
0.5f * (tanh(1.f * width_prior *
(prior_model.flatness_threshold - model.spectral_flatness)) +
1.f);
// For template spectrum-difference : use larger width in tanh map for pause
// regions.
width_prior = model.spectral_diff < prior_model.template_diff_threshold
? kWidthPrior1
: kWidthPrior0;
// Compute indicator function: sigmoid map.
float indicator2 =
0.5f * (tanh(width_prior * (model.spectral_diff -
prior_model.template_diff_threshold)) +
1.f);
// Combine the indicator function with the feature weights.
float ind_prior = prior_model.lrt_weighting * indicator0 +
prior_model.flatness_weighting * indicator1 +
prior_model.difference_weighting * indicator2;
// Compute the prior probability.
prior_speech_prob_ += 0.1f * (ind_prior - prior_speech_prob_);
// Make sure probabilities are within range: keep floor to 0.01.
prior_speech_prob_ = std::max(std::min(prior_speech_prob_, 1.f), 0.01f);
// Final speech probability: combine prior model with LR factor:.
float gain_prior =
(1.f - prior_speech_prob_) / (prior_speech_prob_ + 0.0001f);
std::array<float, kFftSizeBy2Plus1> inv_lrt;
ExpApproximationSignFlip(model.avg_log_lrt, inv_lrt);
for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
speech_probability_[i] = 1.f / (1.f + gain_prior * inv_lrt[i]);
}
}
} // namespace webrtc
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