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/*
* Copyright (c) 2019 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/ns/noise_estimator.h"
#include <algorithm>
#include <array>
#include <cstddef>
#include <cstdint>
#include <numbers>
#include "api/array_view.h"
#include "modules/audio_processing/ns/fast_math.h"
#include "modules/audio_processing/ns/ns_common.h"
#include "modules/audio_processing/ns/suppression_params.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace {
using std::numbers::ln10_v;
// Log(i).
// clang-format off
constexpr std::array<float, 129> log_table = {
0.f, 0.f, 0.f, 0.f, 0.f, 1.609438f, 1.791759f,
1.945910f, 2.079442f, 2.197225f, ln10_v<float>, 2.397895f, 2.484907f,
2.564949f,
2.639057f, 2.708050f, 2.772589f, 2.833213f, 2.890372f, 2.944439f, 2.995732f,
3.044522f, 3.091043f, 3.135494f, 3.178054f, 3.218876f, 3.258097f, 3.295837f,
3.332205f, 3.367296f, 3.401197f, 3.433987f, 3.465736f, 3.496507f, 3.526361f,
3.555348f, 3.583519f, 3.610918f, 3.637586f, 3.663562f, 3.688879f, 3.713572f,
3.737669f, 3.761200f, 3.784190f, 3.806663f, 3.828641f, 3.850147f, 3.871201f,
3.891820f, 3.912023f, 3.931826f, 3.951244f, 3.970292f, 3.988984f, 4.007333f,
4.025352f, 4.043051f, 4.060443f, 4.077538f, 4.094345f, 4.110874f, 4.127134f,
4.143135f, 4.158883f, 4.174387f, 4.189655f, 4.204693f, 4.219508f, 4.234107f,
4.248495f, 4.262680f, 4.276666f, 4.290460f, 4.304065f, 4.317488f, 4.330733f,
4.343805f, 4.356709f, 4.369448f, 4.382027f, 4.394449f, 4.406719f, 4.418841f,
4.430817f, 4.442651f, 4.454347f, 4.465908f, 4.477337f, 4.488636f, 4.499810f,
4.510859f, 4.521789f, 4.532599f, 4.543295f, 4.553877f, 4.564348f, 4.574711f,
4.584968f, 4.595119f, 4.605170f, 4.615121f, 4.624973f, 4.634729f, 4.644391f,
4.653960f, 4.663439f, 4.672829f, 4.682131f, 4.691348f, 4.700480f, 4.709530f,
4.718499f, 4.727388f, 4.736198f, 4.744932f, 4.753591f, 4.762174f, 4.770685f,
4.779124f, 4.787492f, 4.795791f, 4.804021f, 4.812184f, 4.820282f, 4.828314f,
4.836282f, 4.844187f, 4.852030f};
// clang-format on
} // namespace
NoiseEstimator::NoiseEstimator(const SuppressionParams& suppression_params)
: suppression_params_(suppression_params) {
noise_spectrum_.fill(0.f);
prev_noise_spectrum_.fill(0.f);
conservative_noise_spectrum_.fill(0.f);
parametric_noise_spectrum_.fill(0.f);
}
void NoiseEstimator::PrepareAnalysis() {
std::copy(noise_spectrum_.begin(), noise_spectrum_.end(),
prev_noise_spectrum_.begin());
}
void NoiseEstimator::PreUpdate(
int32_t num_analyzed_frames,
ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum,
float signal_spectral_sum) {
quantile_noise_estimator_.Estimate(signal_spectrum, noise_spectrum_);
if (num_analyzed_frames < kShortStartupPhaseBlocks) {
// Compute simplified noise model during startup.
const size_t kStartBand = 5;
float sum_log_i_log_magn = 0.f;
float sum_log_i = 0.f;
float sum_log_i_square = 0.f;
float sum_log_magn = 0.f;
for (size_t i = kStartBand; i < kFftSizeBy2Plus1; ++i) {
float log_i = log_table[i];
sum_log_i += log_i;
sum_log_i_square += log_i * log_i;
float log_signal = LogApproximation(signal_spectrum[i]);
sum_log_magn += log_signal;
sum_log_i_log_magn += log_i * log_signal;
}
// Estimate the parameter for the level of the white noise.
constexpr float kOneByFftSizeBy2Plus1 = 1.f / kFftSizeBy2Plus1;
white_noise_level_ += signal_spectral_sum * kOneByFftSizeBy2Plus1 *
suppression_params_.over_subtraction_factor;
// Estimate pink noise parameters.
float denom = sum_log_i_square * (kFftSizeBy2Plus1 - kStartBand) -
sum_log_i * sum_log_i;
float num =
sum_log_i_square * sum_log_magn - sum_log_i * sum_log_i_log_magn;
RTC_DCHECK_NE(denom, 0.f);
float pink_noise_adjustment = num / denom;
// Constrain the estimated spectrum to be positive.
pink_noise_adjustment = std::max(pink_noise_adjustment, 0.f);
pink_noise_numerator_ += pink_noise_adjustment;
num = sum_log_i * sum_log_magn -
(kFftSizeBy2Plus1 - kStartBand) * sum_log_i_log_magn;
RTC_DCHECK_NE(denom, 0.f);
pink_noise_adjustment = num / denom;
// Constrain the pink noise power to be in the interval [0, 1].
pink_noise_adjustment = std::max(std::min(pink_noise_adjustment, 1.f), 0.f);
pink_noise_exp_ += pink_noise_adjustment;
const float one_by_num_analyzed_frames_plus_1 =
1.f / (num_analyzed_frames + 1.f);
// Calculate the frequency-independent parts of parametric noise estimate.
float parametric_exp = 0.f;
float parametric_num = 0.f;
if (pink_noise_exp_ > 0.f) {
// Use pink noise estimate.
parametric_num = ExpApproximation(pink_noise_numerator_ *
one_by_num_analyzed_frames_plus_1);
parametric_num *= num_analyzed_frames + 1.f;
parametric_exp = pink_noise_exp_ * one_by_num_analyzed_frames_plus_1;
}
constexpr float kOneByShortStartupPhaseBlocks =
1.f / kShortStartupPhaseBlocks;
for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
// Estimate the background noise using the white and pink noise
// parameters.
if (pink_noise_exp_ == 0.f) {
// Use white noise estimate.
parametric_noise_spectrum_[i] = white_noise_level_;
} else {
// Use pink noise estimate.
float use_band = i < kStartBand ? kStartBand : i;
float parametric_denom = PowApproximation(use_band, parametric_exp);
RTC_DCHECK_NE(parametric_denom, 0.f);
parametric_noise_spectrum_[i] = parametric_num / parametric_denom;
}
}
// Weight quantile noise with modeled noise.
for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
noise_spectrum_[i] *= num_analyzed_frames;
float tmp = parametric_noise_spectrum_[i] *
(kShortStartupPhaseBlocks - num_analyzed_frames);
noise_spectrum_[i] += tmp * one_by_num_analyzed_frames_plus_1;
noise_spectrum_[i] *= kOneByShortStartupPhaseBlocks;
}
}
}
void NoiseEstimator::PostUpdate(
ArrayView<const float> speech_probability,
ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum) {
// Time-avg parameter for noise_spectrum update.
constexpr float kNoiseUpdate = 0.9f;
float gamma = kNoiseUpdate;
for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
const float prob_speech = speech_probability[i];
const float prob_non_speech = 1.f - prob_speech;
// Temporary noise update used for speech frames if update value is less
// than previous.
float noise_update_tmp =
gamma * prev_noise_spectrum_[i] +
(1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
prob_speech * prev_noise_spectrum_[i]);
// Time-constant based on speech/noise_spectrum state.
float gamma_old = gamma;
// Increase gamma for frame likely to be seech.
constexpr float kProbRange = .2f;
gamma = prob_speech > kProbRange ? .99f : kNoiseUpdate;
// Conservative noise_spectrum update.
if (prob_speech < kProbRange) {
conservative_noise_spectrum_[i] +=
0.05f * (signal_spectrum[i] - conservative_noise_spectrum_[i]);
}
// Noise_spectrum update.
if (gamma == gamma_old) {
noise_spectrum_[i] = noise_update_tmp;
} else {
noise_spectrum_[i] =
gamma * prev_noise_spectrum_[i] +
(1.f - gamma) * (prob_non_speech * signal_spectrum[i] +
prob_speech * prev_noise_spectrum_[i]);
// Allow for noise_spectrum update downwards: If noise_spectrum update
// decreases the noise_spectrum, it is safe, so allow it to happen.
noise_spectrum_[i] = std::min(noise_spectrum_[i], noise_update_tmp);
}
}
}
} // namespace webrtc
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