Use VAD to get a better speech power estimation in the IntelligibilityEnhancer
R=henrik.lundin@webrtc.org, turaj@webrtc.org
Review URL: https://codereview.webrtc.org/1693823004 .
Cr-Commit-Position: refs/heads/master@{#11713}
diff --git a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
index 6c44415..6d37199 100644
--- a/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
+++ b/webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
@@ -14,6 +14,7 @@
#include <stdlib.h>
#include <string.h>
#include <algorithm>
+#include <limits>
namespace webrtc {
@@ -21,45 +22,38 @@
namespace {
-// Return |current| changed towards |target|, with the change being at most
-// |limit|.
+// Return |current| changed towards |target|, with the relative change being at
+// most |limit|.
float UpdateFactor(float target, float current, float limit) {
- float delta = fabsf(target - current);
- float sign = copysign(1.f, target - current);
- return current + sign * fminf(delta, limit);
+ float gain = target / (current + std::numeric_limits<float>::epsilon());
+ if (gain < 1.f - limit) {
+ gain = 1.f - limit;
+ } else if (gain > 1.f + limit) {
+ gain = 1.f + limit;
+ }
+ return current * gain + std::numeric_limits<float>::epsilon();
}
} // namespace
-PowerEstimator::PowerEstimator(size_t num_freqs,
- float decay)
- : magnitude_(new float[num_freqs]()),
- power_(new float[num_freqs]()),
- num_freqs_(num_freqs),
- decay_(decay) {
- memset(magnitude_.get(), 0, sizeof(*magnitude_.get()) * num_freqs_);
- memset(power_.get(), 0, sizeof(*power_.get()) * num_freqs_);
-}
+template<typename T>
+PowerEstimator<T>::PowerEstimator(size_t num_freqs, float decay)
+ : power_(num_freqs, 0.f), decay_(decay) {}
-// Compute the magnitude from the beginning, with exponential decaying of the
-// series data.
-void PowerEstimator::Step(const std::complex<float>* data) {
- for (size_t i = 0; i < num_freqs_; ++i) {
- magnitude_[i] = decay_ * magnitude_[i] +
- (1.f - decay_) * std::abs(data[i]);
+template<typename T>
+void PowerEstimator<T>::Step(const T* data) {
+ for (size_t i = 0; i < power_.size(); ++i) {
+ power_[i] = decay_ * power_[i] +
+ (1.f - decay_) * std::abs(data[i]) * std::abs(data[i]);
}
}
-const float* PowerEstimator::Power() {
- for (size_t i = 0; i < num_freqs_; ++i) {
- power_[i] = magnitude_[i] * magnitude_[i];
- }
- return &power_[0];
-}
+template class PowerEstimator<float>;
+template class PowerEstimator<std::complex<float>>;
-GainApplier::GainApplier(size_t freqs, float change_limit)
+GainApplier::GainApplier(size_t freqs, float relative_change_limit)
: num_freqs_(freqs),
- change_limit_(change_limit),
+ relative_change_limit_(relative_change_limit),
target_(new float[freqs]()),
current_(new float[freqs]()) {
for (size_t i = 0; i < freqs; ++i) {
@@ -71,12 +65,8 @@
void GainApplier::Apply(const std::complex<float>* in_block,
std::complex<float>* out_block) {
for (size_t i = 0; i < num_freqs_; ++i) {
- float factor = sqrtf(fabsf(current_[i]));
- if (!std::isnormal(factor)) {
- factor = 1.f;
- }
- out_block[i] = factor * in_block[i];
- current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);
+ current_[i] = UpdateFactor(target_[i], current_[i], relative_change_limit_);
+ out_block[i] = sqrtf(fabsf(current_[i])) * in_block[i];
}
}