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The estimation on how well the linear filter in the AEC3 is performing is done through an estimation of the ERLE. That estimation is then used for knowing how much the suppressor needs to react in order to cancel all the echoes. In the current code, the ERLE is quite conservative during farend inactivity and it is common that it goes to a minimum value during those periods. Under highly varying conditions, that is probably the right approach. However, in other scenarios where conditions does not change that fast there is a loss in transparency that could be avoided by means of a different ERLE estimation. In the current CL, the ERLE estimation has been changed in the following way: - During farend activity the ERLE is estimated through a 1st order AR smoother. This smoother goes faster toward lower ERLE values than to larger ones in order to avoid overestimation of this value. Furthermore, during the beginning of the farend burst, an estimation of the ERLE is done that aim to represent the performance of the linear filter during onsets. Under highly variant environments, those quantities, the ERLE during onsets and the one computed during the whole farend duration, would differ a lot. If the environment is more stationary, those quantities would be much more similar. - During nearend activity the ERLE estimation is decreased toward a value of the ERLE during onsets. Bug: webrtc:9040 Change-Id: Ieab86370a4333d2d0cd7041047d29651de4f6827 Reviewed-on: https://webrtc-review.googlesource.com/62342 Commit-Queue: Jesus de Vicente Pena <devicentepena@webrtc.org> Reviewed-by: Per Åhgren <peah@webrtc.org> Cr-Commit-Position: refs/heads/master@{#22568}
133 lines
4.4 KiB
C++
133 lines
4.4 KiB
C++
/*
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* Copyright (c) 2017 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/aec3/erle_estimator.h"
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#include "test/gtest.h"
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namespace webrtc {
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namespace {
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constexpr int kLowFrequencyLimit = kFftLengthBy2 / 2;
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constexpr float kMaxErleLf = 8.f;
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constexpr float kMaxErleHf = 1.5f;
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constexpr float kMinErle = 1.0f;
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constexpr float kTrueErle = 10.f;
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constexpr float kTrueErleOnsets = 1.0f;
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void VerifyErleBands(const std::array<float, kFftLengthBy2Plus1>& erle,
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float reference_lf,
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float reference_hf) {
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std::for_each(
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erle.begin(), erle.begin() + kLowFrequencyLimit,
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[reference_lf](float a) { EXPECT_NEAR(reference_lf, a, 0.001); });
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std::for_each(
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erle.begin() + kLowFrequencyLimit, erle.end(),
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[reference_hf](float a) { EXPECT_NEAR(reference_hf, a, 0.001); });
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}
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void VerifyErle(const std::array<float, kFftLengthBy2Plus1>& erle,
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float erle_time_domain,
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float reference_lf,
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float reference_hf) {
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VerifyErleBands(erle, reference_lf, reference_hf);
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EXPECT_NEAR(reference_lf, erle_time_domain, 0.001);
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}
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void FormFarendFrame(std::array<float, kFftLengthBy2Plus1>* X2,
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std::array<float, kFftLengthBy2Plus1>* E2,
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std::array<float, kFftLengthBy2Plus1>* Y2,
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float erle) {
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X2->fill(500 * 1000.f * 1000.f);
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E2->fill(1000.f * 1000.f);
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Y2->fill(erle * (*E2)[0]);
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}
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void FormNearendFrame(std::array<float, kFftLengthBy2Plus1>* X2,
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std::array<float, kFftLengthBy2Plus1>* E2,
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std::array<float, kFftLengthBy2Plus1>* Y2) {
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X2->fill(0.f);
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Y2->fill(500.f * 1000.f * 1000.f);
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E2->fill((*Y2)[0]);
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}
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} // namespace
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TEST(ErleEstimator, VerifyErleIncreaseAndHold) {
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std::array<float, kFftLengthBy2Plus1> X2;
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std::array<float, kFftLengthBy2Plus1> E2;
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std::array<float, kFftLengthBy2Plus1> Y2;
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ErleEstimator estimator(kMinErle, kMaxErleLf, kMaxErleHf);
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// Verifies that the ERLE estimate is properly increased to higher values.
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FormFarendFrame(&X2, &E2, &Y2, kTrueErle);
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for (size_t k = 0; k < 200; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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VerifyErle(estimator.Erle(), estimator.ErleTimeDomain(), 8.f, 1.5f);
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FormNearendFrame(&X2, &E2, &Y2);
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// Verifies that the ERLE is not immediately decreased during nearend
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// activity.
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for (size_t k = 0; k < 98; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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VerifyErle(estimator.Erle(), estimator.ErleTimeDomain(), 8.f, 1.5f);
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}
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TEST(ErleEstimator, VerifyErleTrackingOnOnsets) {
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std::array<float, kFftLengthBy2Plus1> X2;
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std::array<float, kFftLengthBy2Plus1> E2;
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std::array<float, kFftLengthBy2Plus1> Y2;
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ErleEstimator estimator(kMinErle, kMaxErleLf, kMaxErleHf);
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for (size_t burst = 0; burst < 20; ++burst) {
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FormFarendFrame(&X2, &E2, &Y2, kTrueErleOnsets);
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for (size_t k = 0; k < 10; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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FormFarendFrame(&X2, &E2, &Y2, kTrueErle);
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for (size_t k = 0; k < 200; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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FormNearendFrame(&X2, &E2, &Y2);
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for (size_t k = 0; k < 100; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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}
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VerifyErleBands(estimator.ErleOnsets(), kMinErle, kMinErle);
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FormNearendFrame(&X2, &E2, &Y2);
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for (size_t k = 0; k < 1000; k++) {
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estimator.Update(X2, Y2, E2);
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}
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// Verifies that during ne activity, Erle converges to the Erle for onsets.
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VerifyErle(estimator.Erle(), estimator.ErleTimeDomain(), kMinErle, kMinErle);
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}
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TEST(ErleEstimator, VerifyNoErleUpdateDuringLowActivity) {
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std::array<float, kFftLengthBy2Plus1> X2;
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std::array<float, kFftLengthBy2Plus1> E2;
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std::array<float, kFftLengthBy2Plus1> Y2;
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ErleEstimator estimator(kMinErle, kMaxErleLf, kMaxErleHf);
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// Verifies that the ERLE estimate is is not updated for low-level render
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// signals.
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X2.fill(1000.f * 1000.f);
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Y2.fill(10 * E2[0]);
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for (size_t k = 0; k < 200; ++k) {
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estimator.Update(X2, Y2, E2);
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}
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VerifyErle(estimator.Erle(), estimator.ErleTimeDomain(), kMinErle, kMinErle);
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}
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} // namespace webrtc
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