1 files changed, 103 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/ns/speech_probability_estimator.cc b/third_party/libwebrtc/modules/audio_processing/ns/speech_probability_estimator.cc
new file mode 100644
index 0000000000..fce9bc8e07
--- /dev/null
+++ b/third_party/libwebrtc/modules/audio_processing/ns/speech_probability_estimator.cc
@@ -0,0 +1,103 @@
+/*
+ *  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,
+    rtc::ArrayView<const float, kFftSizeBy2Plus1> prior_snr,
+    rtc::ArrayView<const float, kFftSizeBy2Plus1> post_snr,
+    rtc::ArrayView<const float, kFftSizeBy2Plus1> conservative_noise_spectrum,
+    rtc::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