| Index: webrtc/modules/audio_processing/aec3/comfort_noise_generator.cc
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| diff --git a/webrtc/modules/audio_processing/aec3/comfort_noise_generator.cc b/webrtc/modules/audio_processing/aec3/comfort_noise_generator.cc
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| new file mode 100644
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| index 0000000000000000000000000000000000000000..5600b960b3bffd0c7d9fb2fbef3bf689972d9851
|
| --- /dev/null
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| +++ b/webrtc/modules/audio_processing/aec3/comfort_noise_generator.cc
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| @@ -0,0 +1,201 @@
|
| +/*
|
| + * Copyright (c) 2017 The WebRTC project authors. All Rights Reserved.
|
| + *
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| + * 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
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| + * 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 "webrtc/modules/audio_processing/aec3/comfort_noise_generator.h"
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| +
|
| +#include "webrtc/typedefs.h"
|
| +#if defined(WEBRTC_ARCH_X86_FAMILY)
|
| +#include <emmintrin.h>
|
| +#endif
|
| +#include <math.h>
|
| +#include <algorithm>
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| +#include <array>
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| +#include <functional>
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| +#include <numeric>
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| +
|
| +#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
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| +
|
| +namespace webrtc {
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| +
|
| +namespace {
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| +
|
| +// Creates an array of uniformly distributed variables.
|
| +void TableRandomValue(int16_t* vector, int16_t vector_length, uint32_t* seed) {
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| + for (int i = 0; i < vector_length; i++) {
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| + seed[0] = (seed[0] * ((int32_t)69069) + 1) & (0x80000000 - 1);
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| + vector[i] = (int16_t)(seed[0] >> 16);
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| + }
|
| +}
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| +
|
| +} // namespace
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| +
|
| +#if defined(WEBRTC_ARCH_X86_FAMILY)
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| +
|
| +void EstimateComfortNoise_SSE2(const std::array<float, kFftLengthBy2Plus1>& N2,
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| + uint32_t* seed,
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| + FftData* lower_band_noise,
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| + FftData* upper_band_noise) {
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| + FftData* N_low = lower_band_noise;
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| + FftData* N_high = upper_band_noise;
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| +
|
| + // Compute square root spectrum.
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| + std::array<float, kFftLengthBy2Plus1> N;
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| + for (size_t k = 0; k < kFftLengthBy2; k += 4) {
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| + __m128 v = _mm_loadu_ps(&N2[k]);
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| + v = _mm_sqrt_ps(v);
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| + _mm_storeu_ps(&N[k], v);
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| + }
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| +
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| + N[kFftLengthBy2] = sqrtf(N2[kFftLengthBy2]);
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| +
|
| + // Compute the noise level for the upper bands.
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| + constexpr float kOneByNumBands = 1.f / (kFftLengthBy2Plus1 / 2 + 1);
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| + constexpr int kFftLengthBy2Plus1By2 = kFftLengthBy2Plus1 / 2;
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| + const float high_band_noise_level =
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| + std::accumulate(N.begin() + kFftLengthBy2Plus1By2, N.end(), 0.f) *
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| + kOneByNumBands;
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| +
|
| + // Generate complex noise.
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| + std::array<int16_t, kFftLengthBy2 - 1> random_values_int;
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| + TableRandomValue(random_values_int.data(), random_values_int.size(), seed);
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| +
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| + std::array<float, kFftLengthBy2 - 1> sin;
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| + std::array<float, kFftLengthBy2 - 1> cos;
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| + constexpr float kScale = 6.28318530717959f / 32768.0f;
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| + std::transform(random_values_int.begin(), random_values_int.end(),
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| + sin.begin(), [&](int16_t a) { return -sinf(kScale * a); });
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| + std::transform(random_values_int.begin(), random_values_int.end(),
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| + cos.begin(), [&](int16_t a) { return cosf(kScale * a); });
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| +
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| + // Form low-frequency noise via spectral shaping.
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| + N_low->re[0] = N_low->re[kFftLengthBy2] = N_high->re[0] =
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| + N_high->re[kFftLengthBy2] = 0.f;
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| + std::transform(cos.begin(), cos.end(), N.begin() + 1, N_low->re.begin() + 1,
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| + std::multiplies<float>());
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| + std::transform(sin.begin(), sin.end(), N.begin() + 1, N_low->im.begin() + 1,
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| + std::multiplies<float>());
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| +
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| + // Form the high-frequency noise via simple levelling.
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| + std::transform(cos.begin(), cos.end(), N_high->re.begin() + 1,
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| + [&](float a) { return high_band_noise_level * a; });
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| + std::transform(sin.begin(), sin.end(), N_high->im.begin() + 1,
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| + [&](float a) { return high_band_noise_level * a; });
|
| +}
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| +
|
| +#endif
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| +
|
| +void EstimateComfortNoise(const std::array<float, kFftLengthBy2Plus1>& N2,
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| + uint32_t* seed,
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| + FftData* lower_band_noise,
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| + FftData* upper_band_noise) {
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| + FftData* N_low = lower_band_noise;
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| + FftData* N_high = upper_band_noise;
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| +
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| + // Compute square root spectrum.
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| + std::array<float, kFftLengthBy2Plus1> N;
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| + std::transform(N2.begin(), N2.end(), N.begin(),
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| + [](float a) { return sqrtf(a); });
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| +
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| + // Compute the noise level for the upper bands.
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| + constexpr float kOneByNumBands = 1.f / (kFftLengthBy2Plus1 / 2 + 1);
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| + constexpr int kFftLengthBy2Plus1By2 = kFftLengthBy2Plus1 / 2;
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| + const float high_band_noise_level =
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| + std::accumulate(N.begin() + kFftLengthBy2Plus1By2, N.end(), 0.f) *
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| + kOneByNumBands;
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| +
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| + // Generate complex noise.
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| + std::array<int16_t, kFftLengthBy2 - 1> random_values_int;
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| + TableRandomValue(random_values_int.data(), random_values_int.size(), seed);
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| +
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| + std::array<float, kFftLengthBy2 - 1> sin;
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| + std::array<float, kFftLengthBy2 - 1> cos;
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| + constexpr float kScale = 6.28318530717959f / 32768.0f;
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| + std::transform(random_values_int.begin(), random_values_int.end(),
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| + sin.begin(), [&](int16_t a) { return -sinf(kScale * a); });
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| + std::transform(random_values_int.begin(), random_values_int.end(),
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| + cos.begin(), [&](int16_t a) { return cosf(kScale * a); });
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| +
|
| + // Form low-frequency noise via spectral shaping.
|
| + N_low->re[0] = N_low->re[kFftLengthBy2] = N_high->re[0] =
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| + N_high->re[kFftLengthBy2] = 0.f;
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| + std::transform(cos.begin(), cos.end(), N.begin() + 1, N_low->re.begin() + 1,
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| + std::multiplies<float>());
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| + std::transform(sin.begin(), sin.end(), N.begin() + 1, N_low->im.begin() + 1,
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| + std::multiplies<float>());
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| +
|
| + // Form the high-frequency noise via simple levelling.
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| + std::transform(cos.begin(), cos.end(), N_high->re.begin() + 1,
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| + [&](float a) { return high_band_noise_level * a; });
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| + std::transform(sin.begin(), sin.end(), N_high->im.begin() + 1,
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| + [&](float a) { return high_band_noise_level * a; });
|
| +}
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| +
|
| +ComfortNoiseGenerator::ComfortNoiseGenerator(Aec3Optimization optimization)
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| + : optimization_(optimization),
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| + seed_(42),
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| + N2_initial_(new std::array<float, kFftLengthBy2Plus1>()) {
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| + N2_initial_->fill(0.f);
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| + Y2_smoothed_.fill(0.f);
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| + N2_.fill(1.0e6f);
|
| +}
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| +
|
| +ComfortNoiseGenerator::~ComfortNoiseGenerator() = default;
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| +
|
| +void ComfortNoiseGenerator::Compute(
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| + const AecState& aec_state,
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| + const std::array<float, kFftLengthBy2Plus1>& capture_spectrum,
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| + FftData* lower_band_noise,
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| + FftData* upper_band_noise) {
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| + RTC_DCHECK(lower_band_noise);
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| + RTC_DCHECK(upper_band_noise);
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| + const auto& Y2 = capture_spectrum;
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| +
|
| + if (!aec_state.SaturatedCapture()) {
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| + // Smooth Y2.
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| + std::transform(Y2_smoothed_.begin(), Y2_smoothed_.end(), Y2.begin(),
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| + Y2_smoothed_.begin(),
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| + [](float a, float b) { return a + 0.1f * (b - a); });
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| +
|
| + if (N2_counter_ > 50) {
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| + // Update N2 from Y2_smoothed.
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| + std::transform(N2_.begin(), N2_.end(), Y2_smoothed_.begin(), N2_.begin(),
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| + [](float a, float b) {
|
| + return b < a ? (0.9f * b + 0.1f * a) * 1.0002f
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| + : a * 1.0002f;
|
| + });
|
| + }
|
| +
|
| + if (N2_initial_) {
|
| + if (++N2_counter_ == 1000) {
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| + N2_initial_.reset();
|
| + } else {
|
| + // Compute the N2_initial from N2.
|
| + std::transform(
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| + N2_.begin(), N2_.end(), N2_initial_->begin(), N2_initial_->begin(),
|
| + [](float a, float b) { return a > b ? b + 0.001f * (a - b) : a; });
|
| + }
|
| + }
|
| + }
|
| +
|
| + // Choose N2 estimate to use.
|
| + const std::array<float, kFftLengthBy2Plus1>& N2 =
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| + N2_initial_ ? *N2_initial_ : N2_;
|
| +
|
| + if (optimization_ == Aec3Optimization::kNone) {
|
| + EstimateComfortNoise(N2, &seed_, lower_band_noise, upper_band_noise);
|
| + }
|
| +#if defined(WEBRTC_ARCH_X86_FAMILY)
|
| + if (optimization_ == Aec3Optimization::kSse2) {
|
| + EstimateComfortNoise_SSE2(N2, &seed_, lower_band_noise, upper_band_noise);
|
| + }
|
| +#endif
|
| +}
|
| +
|
| +} // namespace webrtc
|
|
|