Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 203 insertions, 0 deletions

diff --git a/third_party/libwebrtc/modules/audio_processing/agc2/interpolated_gain_curve_unittest.cc b/third_party/libwebrtc/modules/audio_processing/agc2/interpolated_gain_curve_unittest.cc
new file mode 100644
index 0000000000..7861ae997d
--- /dev/null
+++ b/third_party/libwebrtc/modules/audio_processing/agc2/interpolated_gain_curve_unittest.cc
@@ -0,0 +1,203 @@
+/*
+ *  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/interpolated_gain_curve.h"
+
+#include <array>
+#include <type_traits>
+#include <vector>
+
+#include "api/array_view.h"
+#include "common_audio/include/audio_util.h"
+#include "modules/audio_processing/agc2/agc2_common.h"
+#include "modules/audio_processing/agc2/compute_interpolated_gain_curve.h"
+#include "modules/audio_processing/agc2/limiter_db_gain_curve.h"
+#include "modules/audio_processing/logging/apm_data_dumper.h"
+#include "rtc_base/checks.h"
+#include "rtc_base/gunit.h"
+
+namespace webrtc {
+namespace {
+
+constexpr double kLevelEpsilon = 1e-2 * kMaxAbsFloatS16Value;
+constexpr float kInterpolatedGainCurveTolerance = 1.f / 32768.f;
+ApmDataDumper apm_data_dumper(0);
+static_assert(std::is_trivially_destructible<LimiterDbGainCurve>::value, "");
+const LimiterDbGainCurve limiter;
+
+}  // namespace
+
+TEST(GainController2InterpolatedGainCurve, CreateUse) {
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      kLevelEpsilon, DbfsToFloatS16(limiter.max_input_level_db() + 1), 500);
+  for (const auto level : levels) {
+    EXPECT_GE(igc.LookUpGainToApply(level), 0.0f);
+  }
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckValidOutput) {
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      kLevelEpsilon, limiter.max_input_level_linear() * 2.0, 500);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    const float gain = igc.LookUpGainToApply(level);
+    EXPECT_LE(0.0f, gain);
+    EXPECT_LE(gain, 1.0f);
+  }
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckMonotonicity) {
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      kLevelEpsilon, limiter.max_input_level_linear() + kLevelEpsilon + 0.5,
+      500);
+  float prev_gain = igc.LookUpGainToApply(0.0f);
+  for (const auto level : levels) {
+    const float gain = igc.LookUpGainToApply(level);
+    EXPECT_GE(prev_gain, gain);
+    prev_gain = gain;
+  }
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckApproximation) {
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      kLevelEpsilon, limiter.max_input_level_linear() - kLevelEpsilon, 500);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    EXPECT_LT(
+        std::fabs(limiter.GetGainLinear(level) - igc.LookUpGainToApply(level)),
+        kInterpolatedGainCurveTolerance);
+  }
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckRegionBoundaries) {
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const std::vector<double> levels{
+      {kLevelEpsilon, limiter.knee_start_linear() + kLevelEpsilon,
+       limiter.limiter_start_linear() + kLevelEpsilon,
+       limiter.max_input_level_linear() + kLevelEpsilon}};
+  for (const auto level : levels) {
+    igc.LookUpGainToApply(level);
+  }
+
+  const auto stats = igc.get_stats();
+  EXPECT_EQ(1ul, stats.look_ups_identity_region);
+  EXPECT_EQ(1ul, stats.look_ups_knee_region);
+  EXPECT_EQ(1ul, stats.look_ups_limiter_region);
+  EXPECT_EQ(1ul, stats.look_ups_saturation_region);
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckIdentityRegion) {
+  constexpr size_t kNumSteps = 10;
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels =
+      test::LinSpace(kLevelEpsilon, limiter.knee_start_linear(), kNumSteps);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    EXPECT_EQ(1.0f, igc.LookUpGainToApply(level));
+  }
+
+  const auto stats = igc.get_stats();
+  EXPECT_EQ(kNumSteps - 1, stats.look_ups_identity_region);
+  EXPECT_EQ(1ul, stats.look_ups_knee_region);
+  EXPECT_EQ(0ul, stats.look_ups_limiter_region);
+  EXPECT_EQ(0ul, stats.look_ups_saturation_region);
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckNoOverApproximationKnee) {
+  constexpr size_t kNumSteps = 10;
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels =
+      test::LinSpace(limiter.knee_start_linear() + kLevelEpsilon,
+                     limiter.limiter_start_linear(), kNumSteps);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    // Small tolerance added (needed because comparing a float with a double).
+    EXPECT_LE(igc.LookUpGainToApply(level),
+              limiter.GetGainLinear(level) + 1e-7);
+  }
+
+  const auto stats = igc.get_stats();
+  EXPECT_EQ(0ul, stats.look_ups_identity_region);
+  EXPECT_EQ(kNumSteps - 1, stats.look_ups_knee_region);
+  EXPECT_EQ(1ul, stats.look_ups_limiter_region);
+  EXPECT_EQ(0ul, stats.look_ups_saturation_region);
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckNoOverApproximationBeyondKnee) {
+  constexpr size_t kNumSteps = 10;
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      limiter.limiter_start_linear() + kLevelEpsilon,
+      limiter.max_input_level_linear() - kLevelEpsilon, kNumSteps);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    // Small tolerance added (needed because comparing a float with a double).
+    EXPECT_LE(igc.LookUpGainToApply(level),
+              limiter.GetGainLinear(level) + 1e-7);
+  }
+
+  const auto stats = igc.get_stats();
+  EXPECT_EQ(0ul, stats.look_ups_identity_region);
+  EXPECT_EQ(0ul, stats.look_ups_knee_region);
+  EXPECT_EQ(kNumSteps, stats.look_ups_limiter_region);
+  EXPECT_EQ(0ul, stats.look_ups_saturation_region);
+}
+
+TEST(GainController2InterpolatedGainCurve,
+     CheckNoOverApproximationWithSaturation) {
+  constexpr size_t kNumSteps = 3;
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  const auto levels = test::LinSpace(
+      limiter.max_input_level_linear() + kLevelEpsilon,
+      limiter.max_input_level_linear() + kLevelEpsilon + 0.5, kNumSteps);
+  for (const auto level : levels) {
+    SCOPED_TRACE(std::to_string(level));
+    EXPECT_LE(igc.LookUpGainToApply(level), limiter.GetGainLinear(level));
+  }
+
+  const auto stats = igc.get_stats();
+  EXPECT_EQ(0ul, stats.look_ups_identity_region);
+  EXPECT_EQ(0ul, stats.look_ups_knee_region);
+  EXPECT_EQ(0ul, stats.look_ups_limiter_region);
+  EXPECT_EQ(kNumSteps, stats.look_ups_saturation_region);
+}
+
+TEST(GainController2InterpolatedGainCurve, CheckApproximationParams) {
+  test::InterpolatedParameters parameters =
+      test::ComputeInterpolatedGainCurveApproximationParams();
+
+  InterpolatedGainCurve igc(&apm_data_dumper, "");
+
+  for (size_t i = 0; i < kInterpolatedGainCurveTotalPoints; ++i) {
+    // The tolerance levels are chosen to account for deviations due
+    // to computing with single precision floating point numbers.
+    EXPECT_NEAR(igc.approximation_params_x_[i],
+                parameters.computed_approximation_params_x[i], 0.9f);
+    EXPECT_NEAR(igc.approximation_params_m_[i],
+                parameters.computed_approximation_params_m[i], 0.00001f);
+    EXPECT_NEAR(igc.approximation_params_q_[i],
+                parameters.computed_approximation_params_q[i], 0.001f);
+  }
+}
+
+}  // namespace webrtc