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154 lines
6.3 KiB
C
154 lines
6.3 KiB
C
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/*
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* Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#ifndef MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
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#define MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
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#include <array>
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#include <string>
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#include "modules/audio_processing/agc2/agc2_common.h"
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#include "rtc_base/constructormagic.h"
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#include "rtc_base/gtest_prod_util.h"
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#include "system_wrappers/include/metrics.h"
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namespace webrtc {
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class ApmDataDumper;
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constexpr float kInputLevelScalingFactor = 32768.0f;
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// Defined as DbfsToLinear(kLimiterMaxInputLevelDbFs)
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constexpr float kMaxInputLevelLinear = static_cast<float>(36766.300710566735);
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// Interpolated gain curve using under-approximation to avoid saturation.
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//
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// The goal of this class is allowing fast look ups to get an accurate
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// estimates of the gain to apply given an estimated input level.
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class InterpolatedGainCurve {
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public:
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enum class GainCurveRegion {
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kIdentity = 0,
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kKnee = 1,
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kLimiter = 2,
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kSaturation = 3
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};
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struct Stats {
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// Region in which the output level equals the input one.
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size_t look_ups_identity_region = 0;
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// Smoothing between the identity and the limiter regions.
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size_t look_ups_knee_region = 0;
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// Limiter region in which the output and input levels are linearly related.
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size_t look_ups_limiter_region = 0;
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// Region in which saturation may occur since the input level is beyond the
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// maximum expected by the limiter.
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size_t look_ups_saturation_region = 0;
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// True if stats have been populated.
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bool available = false;
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// The current region, and for how many frames the level has been
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// in that region.
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GainCurveRegion region = GainCurveRegion::kIdentity;
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int64_t region_duration_frames = 0;
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};
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InterpolatedGainCurve(ApmDataDumper* apm_data_dumper,
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std::string histogram_name_prefix);
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~InterpolatedGainCurve();
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Stats get_stats() const { return stats_; }
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// Given a non-negative input level (linear scale), a scalar factor to apply
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// to a sub-frame is returned.
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// Levels above kLimiterMaxInputLevelDbFs will be reduced to 0 dBFS
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// after applying this gain
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float LookUpGainToApply(float input_level) const;
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private:
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// For comparing 'approximation_params_*_' with ones computed by
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// ComputeInterpolatedGainCurve.
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FRIEND_TEST_ALL_PREFIXES(AutomaticGainController2InterpolatedGainCurve,
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CheckApproximationParams);
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struct RegionLogger {
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metrics::Histogram* identity_histogram;
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metrics::Histogram* knee_histogram;
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metrics::Histogram* limiter_histogram;
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metrics::Histogram* saturation_histogram;
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RegionLogger(std::string identity_histogram_name,
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std::string knee_histogram_name,
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std::string limiter_histogram_name,
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std::string saturation_histogram_name);
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~RegionLogger();
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void LogRegionStats(const InterpolatedGainCurve::Stats& stats) const;
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} region_logger_;
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void UpdateStats(float input_level) const;
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ApmDataDumper* const apm_data_dumper_;
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static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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approximation_params_x_ = {
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{30057.296875, 30148.986328125, 30240.67578125, 30424.052734375,
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30607.4296875, 30790.806640625, 30974.18359375, 31157.560546875,
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31340.939453125, 31524.31640625, 31707.693359375, 31891.0703125,
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32074.447265625, 32257.82421875, 32441.201171875, 32624.580078125,
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32807.95703125, 32991.33203125, 33174.7109375, 33358.08984375,
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33541.46484375, 33724.84375, 33819.53515625, 34009.5390625,
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34200.05859375, 34389.81640625, 34674.48828125, 35054.375,
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35434.86328125, 35814.81640625, 36195.16796875, 36575.03125}};
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static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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approximation_params_m_ = {
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{-3.515235675877192989e-07, -1.050251626111275982e-06,
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-2.085213736791047268e-06, -3.443004743530764244e-06,
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-4.773849468620028347e-06, -6.077375928725814447e-06,
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-7.353257842623861507e-06, -8.601219633419532329e-06,
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-9.821013009059242904e-06, -1.101243378798244521e-05,
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-1.217532644659513608e-05, -1.330956911260727793e-05,
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-1.441507538402220234e-05, -1.549179251014720649e-05,
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-1.653970684856176376e-05, -1.755882840370759368e-05,
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-1.854918446042574942e-05, -1.951086778717581183e-05,
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-2.044398024736437947e-05, -2.1348627342376858e-05,
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-2.222496914328075945e-05, -2.265374678245279938e-05,
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-2.242570917587727308e-05, -2.220122041762806475e-05,
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-2.19802095671184361e-05, -2.176260204578284174e-05,
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-2.133731686626560986e-05, -2.092481918225530535e-05,
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-2.052459603874012828e-05, -2.013615448959171772e-05,
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-1.975903069251216948e-05, -1.939277899509761482e-05}};
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static constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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approximation_params_q_ = {
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{1.010565876960754395, 1.031631827354431152, 1.062929749488830566,
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1.104239225387573242, 1.144973039627075195, 1.185109615325927734,
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1.224629044532775879, 1.263512492179870605, 1.301741957664489746,
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1.339300632476806641, 1.376173257827758789, 1.412345528602600098,
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1.447803974151611328, 1.482536554336547852, 1.516532182693481445,
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1.549780607223510742, 1.582272171974182129, 1.613999366760253906,
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1.644955039024353027, 1.675132393836975098, 1.704526185989379883,
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1.718986630439758301, 1.711274504661560059, 1.703639745712280273,
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1.696081161499023438, 1.688597679138183594, 1.673851132392883301,
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1.659391283988952637, 1.645209431648254395, 1.631297469139099121,
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1.617647409439086914, 1.604251742362976074}};
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// Stats.
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mutable Stats stats_;
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RTC_DISALLOW_COPY_AND_ASSIGN(InterpolatedGainCurve);
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};
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} // namespace webrtc
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#endif // MODULES_AUDIO_PROCESSING_AGC2_INTERPOLATED_GAIN_CURVE_H_
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