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a05ee82c4c
This CL adds the GainCurveApplier (GCA). It owns a FixedDigitalLevelEstimator (LE) and an InterpolatedGainCurve (IGC). The GCA uses the LE to compute the input signal level, looks up a gain from IGC and applies it on the signal. The other IGC and LE submodules were added in previous CLs [1] and [2]. This CL also turns on AGC2 in the APM fuzzer. [1] https://webrtc-review.googlesource.com/c/src/+/51920 [2] https://webrtc-review.googlesource.com/c/src/+/52381 Bug: webrtc:7949 Change-Id: Idb10cc3ca9d6d2e4ac5824cc3391ed8aa680f6cd Reviewed-on: https://webrtc-review.googlesource.com/54361 Commit-Queue: Alex Loiko <aleloi@webrtc.org> Reviewed-by: Sam Zackrisson <saza@webrtc.org> Cr-Commit-Position: refs/heads/master@{#22103}
105 lines
4.1 KiB
C++
105 lines
4.1 KiB
C++
/*
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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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#include "modules/audio_processing/agc2/interpolated_gain_curve.h"
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#include "modules/audio_processing/agc2/agc2_common.h"
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#include "modules/audio_processing/logging/apm_data_dumper.h"
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#include "rtc_base/checks.h"
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#include "rtc_base/logging.h"
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namespace webrtc {
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constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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InterpolatedGainCurve::approximation_params_x_;
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constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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InterpolatedGainCurve::approximation_params_m_;
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constexpr std::array<float, kInterpolatedGainCurveTotalPoints>
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InterpolatedGainCurve::approximation_params_q_;
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InterpolatedGainCurve::InterpolatedGainCurve(ApmDataDumper* apm_data_dumper)
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: apm_data_dumper_(apm_data_dumper) {}
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InterpolatedGainCurve::~InterpolatedGainCurve() {
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if (stats_.available) {
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// TODO(alessiob): We might want to add these stats as RTC metrics.
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RTC_DCHECK(apm_data_dumper_);
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apm_data_dumper_->DumpRaw("agc2_interp_gain_curve_lookups_identity",
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stats_.look_ups_identity_region);
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apm_data_dumper_->DumpRaw("agc2_interp_gain_curve_lookups_knee",
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stats_.look_ups_knee_region);
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apm_data_dumper_->DumpRaw("agc2_interp_gain_curve_lookups_limiter",
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stats_.look_ups_limiter_region);
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apm_data_dumper_->DumpRaw("agc2_interp_gain_curve_lookups_saturation",
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stats_.look_ups_saturation_region);
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}
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}
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void InterpolatedGainCurve::UpdateStats(float input_level) const {
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stats_.available = true;
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if (input_level < approximation_params_x_[0]) {
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stats_.look_ups_identity_region++;
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} else if (input_level <
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approximation_params_x_[kInterpolatedGainCurveKneePoints - 1]) {
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stats_.look_ups_knee_region++;
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} else if (input_level < kMaxInputLevelLinear) {
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stats_.look_ups_limiter_region++;
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} else {
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stats_.look_ups_saturation_region++;
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}
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}
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// Looks up a gain to apply given a non-negative input level.
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// The cost of this operation depends on the region in which |input_level|
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// falls.
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// For the identity and the saturation regions the cost is O(1).
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// For the other regions, namely knee and limiter, the cost is
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// O(2 + log2(|LightkInterpolatedGainCurveTotalPoints|), plus O(1) for the
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// linear interpolation (one product and one sum).
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float InterpolatedGainCurve::LookUpGainToApply(float input_level) const {
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UpdateStats(input_level);
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if (input_level <= approximation_params_x_[0]) {
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// Identity region.
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return 1.0f;
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}
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if (input_level >= kMaxInputLevelLinear) {
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// Saturating lower bound. The saturing samples exactly hit the clipping
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// level. This method achieves has the lowest harmonic distorsion, but it
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// may reduce the amplitude of the non-saturating samples too much.
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return 32768.f / input_level;
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}
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// Knee and limiter regions; find the linear piece index. Spelling
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// out the complete type was the only way to silence both the clang
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// plugin and the windows compilers.
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std::array<float, kInterpolatedGainCurveTotalPoints>::const_iterator it =
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std::lower_bound(approximation_params_x_.begin(),
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approximation_params_x_.end(), input_level);
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const size_t index = std::distance(approximation_params_x_.begin(), it) - 1;
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RTC_DCHECK_LE(0, index);
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RTC_DCHECK_LT(index, approximation_params_m_.size());
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RTC_DCHECK_LE(approximation_params_x_[index], input_level);
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if (index < approximation_params_m_.size() - 1) {
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RTC_DCHECK_LE(input_level, approximation_params_x_[index + 1]);
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}
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// Piece-wise linear interploation.
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const float gain = approximation_params_m_[index] * input_level +
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approximation_params_q_[index];
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RTC_DCHECK_LE(0.f, gain);
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return gain;
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}
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} // namespace webrtc
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