compute_interpolated_gain_curve.cc | Explore in Territory

/*
 *  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.
 */

#include "modules/audio_processing/agc2/compute_interpolated_gain_curve.h"

#include <algorithm>
#include <cmath>
#include <queue>
#include <tuple>
#include <utility>
#include <vector>

#include "modules/audio_processing/agc2/agc2_common.h"
#include "modules/audio_processing/agc2/agc2_testing_common.h"
#include "modules/audio_processing/agc2/limiter_db_gain_curve.h"
#include "rtc_base/checks.h"

namespace webrtc {
namespace {

std::pair<double, double> ComputeLinearApproximationParams(
    const LimiterDbGainCurve* limiter,
    const double x) { … }

double ComputeAreaUnderPiecewiseLinearApproximation(
    const LimiterDbGainCurve* limiter,
    const double x0,
    const double x1) { … }

// Computes the approximation error in the limiter region for a given interval.
// The error is computed as the difference between the areas beneath the limiter
// curve to approximate and its linear under-approximation.
double LimiterUnderApproximationNegativeError(const LimiterDbGainCurve* limiter,
                                              const double x0,
                                              const double x1) { … }

// Automatically finds where to sample the beyond-knee region of a limiter using
// a greedy optimization algorithm that iteratively decreases the approximation
// error.
// The solution is sub-optimal because the algorithm is greedy and the points
// are assigned by halving intervals (starting with the whole beyond-knee region
// as a single interval). However, even if sub-optimal, this algorithm works
// well in practice and it is efficiently implemented using priority queues.
std::vector<double> SampleLimiterRegion(const LimiterDbGainCurve* limiter) { … }

// Compute the parameters to over-approximate the knee region via linear
// interpolation. Over-approximating is saturation-safe since the knee region is
// convex.
void PrecomputeKneeApproxParams(const LimiterDbGainCurve* limiter,
                                test::InterpolatedParameters* parameters) { … }

// Compute the parameters to under-approximate the beyond-knee region via linear
// interpolation and greedy sampling. Under-approximating is saturation-safe
// since the beyond-knee region is concave.
void PrecomputeBeyondKneeApproxParams(
    const LimiterDbGainCurve* limiter,
    test::InterpolatedParameters* parameters) { … }

}  // namespace

namespace test {

InterpolatedParameters ComputeInterpolatedGainCurveApproximationParams() { … }
}  // namespace test
}  // namespace webrtc
chromium/third_party/webrtc/modules/audio_processing/agc2/compute_interpolated_gain_curve.cc
