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/*
* Copyright (c) 2018 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.
*/
#ifndef MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
#define MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
#include <array>
#include "absl/strings/string_view.h"
#include "modules/audio_processing/agc2/agc2_common.h"
#include "rtc_base/gtest_prod_util.h"
#include "system_wrappers/include/metrics.h"
namespace webrtc {
class ApmDataDumper;
constexpr float kInputLevelScalingFactor = 32768.0f;
// Defined as DbfsToLinear(kLimiterMaxInputLevelDbFs)
constexpr float kMaxInputLevelLinear = static_cast<float>(36766.300710566735);
// Interpolated gain curve using under-approximation to avoid saturation.
//
// The goal of this class is allowing fast look ups to get an accurate
// estimates of the gain to apply given an estimated input level.
class InterpolatedGainCurve {
public:
enum class GainCurveRegion {
kIdentity = 0,
kKnee = 1,
kLimiter = 2,
kSaturation = 3
};
struct Stats {
// Region in which the output level equals the input one.
size_t look_ups_identity_region = 0;
// Smoothing between the identity and the limiter regions.
size_t look_ups_knee_region = 0;
// Limiter region in which the output and input levels are linearly related.
size_t look_ups_limiter_region = 0;
// Region in which saturation may occur since the input level is beyond the
// maximum expected by the limiter.
size_t look_ups_saturation_region = 0;
// True if stats have been populated.
bool available = false;
// The current region, and for how many frames the level has been
// in that region.
GainCurveRegion region = GainCurveRegion::kIdentity;
int64_t region_duration_frames = 0;
};
InterpolatedGainCurve(ApmDataDumper* apm_data_dumper,
absl::string_view histogram_name_prefix);
~InterpolatedGainCurve();
InterpolatedGainCurve(const InterpolatedGainCurve&) = delete;
InterpolatedGainCurve& operator=(const InterpolatedGainCurve&) = delete;
Stats get_stats() const { return stats_; }
// Given a non-negative input level (linear scale), a scalar factor to apply
// to a sub-frame is returned.
// Levels above kLimiterMaxInputLevelDbFs will be reduced to 0 dBFS
// after applying this gain
float LookUpGainToApply(float input_level) const;
private:
// For comparing 'approximation_params_*_' with ones computed by
// ComputeInterpolatedGainCurve.
FRIEND_TEST_ALL_PREFIXES(GainController2InterpolatedGainCurve,
CheckApproximationParams);
struct RegionLogger {
metrics::Histogram* identity_histogram;
metrics::Histogram* knee_histogram;
metrics::Histogram* limiter_histogram;
metrics::Histogram* saturation_histogram;
RegionLogger(absl::string_view identity_histogram_name,
absl::string_view knee_histogram_name,
absl::string_view limiter_histogram_name,
absl::string_view saturation_histogram_name);
~RegionLogger();
void LogRegionStats(const InterpolatedGainCurve::Stats& stats) const;
} region_logger_;
void UpdateStats(float input_level) const;
ApmDataDumper* const apm_data_dumper_;
static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
approximation_params_x_ = {
{30057.296875, 30148.986328125, 30240.67578125, 30424.052734375,
30607.4296875, 30790.806640625, 30974.18359375, 31157.560546875,
31340.939453125, 31524.31640625, 31707.693359375, 31891.0703125,
32074.447265625, 32257.82421875, 32441.201171875, 32624.580078125,
32807.95703125, 32991.33203125, 33174.7109375, 33358.08984375,
33541.46484375, 33724.84375, 33819.53515625, 34009.5390625,
34200.05859375, 34389.81640625, 34674.48828125, 35054.375,
35434.86328125, 35814.81640625, 36195.16796875, 36575.03125}};
static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
approximation_params_m_ = {
{-3.515235675877192989e-07, -1.050251626111275982e-06,
-2.085213736791047268e-06, -3.443004743530764244e-06,
-4.773849468620028347e-06, -6.077375928725814447e-06,
-7.353257842623861507e-06, -8.601219633419532329e-06,
-9.821013009059242904e-06, -1.101243378798244521e-05,
-1.217532644659513608e-05, -1.330956911260727793e-05,
-1.441507538402220234e-05, -1.549179251014720649e-05,
-1.653970684856176376e-05, -1.755882840370759368e-05,
-1.854918446042574942e-05, -1.951086778717581183e-05,
-2.044398024736437947e-05, -2.1348627342376858e-05,
-2.222496914328075945e-05, -2.265374678245279938e-05,
-2.242570917587727308e-05, -2.220122041762806475e-05,
-2.19802095671184361e-05, -2.176260204578284174e-05,
-2.133731686626560986e-05, -2.092481918225530535e-05,
-2.052459603874012828e-05, -2.013615448959171772e-05,
-1.975903069251216948e-05, -1.939277899509761482e-05}};
static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
approximation_params_q_ = {
{1.010565876960754395, 1.031631827354431152, 1.062929749488830566,
1.104239225387573242, 1.144973039627075195, 1.185109615325927734,
1.224629044532775879, 1.263512492179870605, 1.301741957664489746,
1.339300632476806641, 1.376173257827758789, 1.412345528602600098,
1.447803974151611328, 1.482536554336547852, 1.516532182693481445,
1.549780607223510742, 1.582272171974182129, 1.613999366760253906,
1.644955039024353027, 1.675132393836975098, 1.704526185989379883,
1.718986630439758301, 1.711274504661560059, 1.703639745712280273,
1.696081161499023438, 1.688597679138183594, 1.673851132392883301,
1.659391283988952637, 1.645209431648254395, 1.631297469139099121,
1.617647409439086914, 1.604251742362976074}};
// Stats.
mutable Stats stats_;
};
} // namespace webrtc
#endif // MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
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