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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 19:33:14 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 19:33:14 +0000
commit36d22d82aa202bb199967e9512281e9a53db42c9 (patch)
tree105e8c98ddea1c1e4784a60a5a6410fa416be2de /third_party/libwebrtc/modules/audio_processing/ns/noise_suppressor.cc
parentInitial commit. (diff)
downloadfirefox-esr-36d22d82aa202bb199967e9512281e9a53db42c9.tar.xz
firefox-esr-36d22d82aa202bb199967e9512281e9a53db42c9.zip
Adding upstream version 115.7.0esr.upstream/115.7.0esrupstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/ns/noise_suppressor.cc')
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+/*
+ * Copyright (c) 2012 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_suppressor.h"
+
+#include <math.h>
+#include <stdlib.h>
+#include <string.h>
+#include <algorithm>
+
+#include "modules/audio_processing/ns/fast_math.h"
+#include "rtc_base/checks.h"
+
+namespace webrtc {
+
+namespace {
+
+// Maps sample rate to number of bands.
+size_t NumBandsForRate(size_t sample_rate_hz) {
+ RTC_DCHECK(sample_rate_hz == 16000 || sample_rate_hz == 32000 ||
+ sample_rate_hz == 48000);
+ return sample_rate_hz / 16000;
+}
+
+// Maximum number of channels for which the channel data is stored on
+// the stack. If the number of channels are larger than this, they are stored
+// using scratch memory that is pre-allocated on the heap. The reason for this
+// partitioning is not to waste heap space for handling the more common numbers
+// of channels, while at the same time not limiting the support for higher
+// numbers of channels by enforcing the channel data to be stored on the
+// stack using a fixed maximum value.
+constexpr size_t kMaxNumChannelsOnStack = 2;
+
+// Chooses the number of channels to store on the heap when that is required due
+// to the number of channels being larger than the pre-defined number
+// of channels to store on the stack.
+size_t NumChannelsOnHeap(size_t num_channels) {
+ return num_channels > kMaxNumChannelsOnStack ? num_channels : 0;
+}
+
+// Hybrib Hanning and flat window for the filterbank.
+constexpr std::array<float, 96> kBlocks160w256FirstHalf = {
+ 0.00000000f, 0.01636173f, 0.03271908f, 0.04906767f, 0.06540313f,
+ 0.08172107f, 0.09801714f, 0.11428696f, 0.13052619f, 0.14673047f,
+ 0.16289547f, 0.17901686f, 0.19509032f, 0.21111155f, 0.22707626f,
+ 0.24298018f, 0.25881905f, 0.27458862f, 0.29028468f, 0.30590302f,
+ 0.32143947f, 0.33688985f, 0.35225005f, 0.36751594f, 0.38268343f,
+ 0.39774847f, 0.41270703f, 0.42755509f, 0.44228869f, 0.45690388f,
+ 0.47139674f, 0.48576339f, 0.50000000f, 0.51410274f, 0.52806785f,
+ 0.54189158f, 0.55557023f, 0.56910015f, 0.58247770f, 0.59569930f,
+ 0.60876143f, 0.62166057f, 0.63439328f, 0.64695615f, 0.65934582f,
+ 0.67155895f, 0.68359230f, 0.69544264f, 0.70710678f, 0.71858162f,
+ 0.72986407f, 0.74095113f, 0.75183981f, 0.76252720f, 0.77301045f,
+ 0.78328675f, 0.79335334f, 0.80320753f, 0.81284668f, 0.82226822f,
+ 0.83146961f, 0.84044840f, 0.84920218f, 0.85772861f, 0.86602540f,
+ 0.87409034f, 0.88192126f, 0.88951608f, 0.89687274f, 0.90398929f,
+ 0.91086382f, 0.91749450f, 0.92387953f, 0.93001722f, 0.93590593f,
+ 0.94154407f, 0.94693013f, 0.95206268f, 0.95694034f, 0.96156180f,
+ 0.96592583f, 0.97003125f, 0.97387698f, 0.97746197f, 0.98078528f,
+ 0.98384601f, 0.98664333f, 0.98917651f, 0.99144486f, 0.99344778f,
+ 0.99518473f, 0.99665524f, 0.99785892f, 0.99879546f, 0.99946459f,
+ 0.99986614f};
+
+// Applies the filterbank window to a buffer.
+void ApplyFilterBankWindow(rtc::ArrayView<float, kFftSize> x) {
+ for (size_t i = 0; i < 96; ++i) {
+ x[i] = kBlocks160w256FirstHalf[i] * x[i];
+ }
+
+ for (size_t i = 161, k = 95; i < kFftSize; ++i, --k) {
+ RTC_DCHECK_NE(0, k);
+ x[i] = kBlocks160w256FirstHalf[k] * x[i];
+ }
+}
+
+// Extends a frame with previous data.
+void FormExtendedFrame(rtc::ArrayView<const float, kNsFrameSize> frame,
+ rtc::ArrayView<float, kFftSize - kNsFrameSize> old_data,
+ rtc::ArrayView<float, kFftSize> extended_frame) {
+ std::copy(old_data.begin(), old_data.end(), extended_frame.begin());
+ std::copy(frame.begin(), frame.end(),
+ extended_frame.begin() + old_data.size());
+ std::copy(extended_frame.end() - old_data.size(), extended_frame.end(),
+ old_data.begin());
+}
+
+// Uses overlap-and-add to produce an output frame.
+void OverlapAndAdd(rtc::ArrayView<const float, kFftSize> extended_frame,
+ rtc::ArrayView<float, kOverlapSize> overlap_memory,
+ rtc::ArrayView<float, kNsFrameSize> output_frame) {
+ for (size_t i = 0; i < kOverlapSize; ++i) {
+ output_frame[i] = overlap_memory[i] + extended_frame[i];
+ }
+ std::copy(extended_frame.begin() + kOverlapSize,
+ extended_frame.begin() + kNsFrameSize,
+ output_frame.begin() + kOverlapSize);
+ std::copy(extended_frame.begin() + kNsFrameSize, extended_frame.end(),
+ overlap_memory.begin());
+}
+
+// Produces a delayed frame.
+void DelaySignal(rtc::ArrayView<const float, kNsFrameSize> frame,
+ rtc::ArrayView<float, kFftSize - kNsFrameSize> delay_buffer,
+ rtc::ArrayView<float, kNsFrameSize> delayed_frame) {
+ constexpr size_t kSamplesFromFrame = kNsFrameSize - (kFftSize - kNsFrameSize);
+ std::copy(delay_buffer.begin(), delay_buffer.end(), delayed_frame.begin());
+ std::copy(frame.begin(), frame.begin() + kSamplesFromFrame,
+ delayed_frame.begin() + delay_buffer.size());
+
+ std::copy(frame.begin() + kSamplesFromFrame, frame.end(),
+ delay_buffer.begin());
+}
+
+// Computes the energy of an extended frame.
+float ComputeEnergyOfExtendedFrame(rtc::ArrayView<const float, kFftSize> x) {
+ float energy = 0.f;
+ for (float x_k : x) {
+ energy += x_k * x_k;
+ }
+
+ return energy;
+}
+
+// Computes the energy of an extended frame based on its subcomponents.
+float ComputeEnergyOfExtendedFrame(
+ rtc::ArrayView<const float, kNsFrameSize> frame,
+ rtc::ArrayView<float, kFftSize - kNsFrameSize> old_data) {
+ float energy = 0.f;
+ for (float v : old_data) {
+ energy += v * v;
+ }
+ for (float v : frame) {
+ energy += v * v;
+ }
+
+ return energy;
+}
+
+// Computes the magnitude spectrum based on an FFT output.
+void ComputeMagnitudeSpectrum(
+ rtc::ArrayView<const float, kFftSize> real,
+ rtc::ArrayView<const float, kFftSize> imag,
+ rtc::ArrayView<float, kFftSizeBy2Plus1> signal_spectrum) {
+ signal_spectrum[0] = fabsf(real[0]) + 1.f;
+ signal_spectrum[kFftSizeBy2Plus1 - 1] =
+ fabsf(real[kFftSizeBy2Plus1 - 1]) + 1.f;
+
+ for (size_t i = 1; i < kFftSizeBy2Plus1 - 1; ++i) {
+ signal_spectrum[i] =
+ SqrtFastApproximation(real[i] * real[i] + imag[i] * imag[i]) + 1.f;
+ }
+}
+
+// Compute prior and post SNR.
+void ComputeSnr(rtc::ArrayView<const float, kFftSizeBy2Plus1> filter,
+ rtc::ArrayView<const float> prev_signal_spectrum,
+ rtc::ArrayView<const float> signal_spectrum,
+ rtc::ArrayView<const float> prev_noise_spectrum,
+ rtc::ArrayView<const float> noise_spectrum,
+ rtc::ArrayView<float> prior_snr,
+ rtc::ArrayView<float> post_snr) {
+ for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
+ // Previous post SNR.
+ // Previous estimate: based on previous frame with gain filter.
+ float prev_estimate = prev_signal_spectrum[i] /
+ (prev_noise_spectrum[i] + 0.0001f) * filter[i];
+ // Post SNR.
+ if (signal_spectrum[i] > noise_spectrum[i]) {
+ post_snr[i] = signal_spectrum[i] / (noise_spectrum[i] + 0.0001f) - 1.f;
+ } else {
+ post_snr[i] = 0.f;
+ }
+ // The directed decision estimate of the prior SNR is a sum the current and
+ // previous estimates.
+ prior_snr[i] = 0.98f * prev_estimate + (1.f - 0.98f) * post_snr[i];
+ }
+}
+
+// Computes the attenuating gain for the noise suppression of the upper bands.
+float ComputeUpperBandsGain(
+ float minimum_attenuating_gain,
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> filter,
+ rtc::ArrayView<const float> speech_probability,
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> prev_analysis_signal_spectrum,
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> signal_spectrum) {
+ // Average speech prob and filter gain for the end of the lowest band.
+ constexpr int kNumAvgBins = 32;
+ constexpr float kOneByNumAvgBins = 1.f / kNumAvgBins;
+
+ float avg_prob_speech = 0.f;
+ float avg_filter_gain = 0.f;
+ for (size_t i = kFftSizeBy2Plus1 - kNumAvgBins - 1; i < kFftSizeBy2Plus1 - 1;
+ i++) {
+ avg_prob_speech += speech_probability[i];
+ avg_filter_gain += filter[i];
+ }
+ avg_prob_speech = avg_prob_speech * kOneByNumAvgBins;
+ avg_filter_gain = avg_filter_gain * kOneByNumAvgBins;
+
+ // If the speech was suppressed by a component between Analyze and Process, an
+ // example being by an AEC, it should not be considered speech for the purpose
+ // of high band suppression. To that end, the speech probability is scaled
+ // accordingly.
+ float sum_analysis_spectrum = 0.f;
+ float sum_processing_spectrum = 0.f;
+ for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
+ sum_analysis_spectrum += prev_analysis_signal_spectrum[i];
+ sum_processing_spectrum += signal_spectrum[i];
+ }
+
+ // The magnitude spectrum computation enforces the spectrum to be strictly
+ // positive.
+ RTC_DCHECK_GT(sum_analysis_spectrum, 0.f);
+ avg_prob_speech *= sum_processing_spectrum / sum_analysis_spectrum;
+
+ // Compute gain based on speech probability.
+ float gain =
+ 0.5f * (1.f + static_cast<float>(tanh(2.f * avg_prob_speech - 1.f)));
+
+ // Combine gain with low band gain.
+ if (avg_prob_speech >= 0.5f) {
+ gain = 0.25f * gain + 0.75f * avg_filter_gain;
+ } else {
+ gain = 0.5f * gain + 0.5f * avg_filter_gain;
+ }
+
+ // Make sure gain is within flooring range.
+ return std::min(std::max(gain, minimum_attenuating_gain), 1.f);
+}
+
+} // namespace
+
+NoiseSuppressor::ChannelState::ChannelState(
+ const SuppressionParams& suppression_params,
+ size_t num_bands)
+ : wiener_filter(suppression_params),
+ noise_estimator(suppression_params),
+ process_delay_memory(num_bands > 1 ? num_bands - 1 : 0) {
+ analyze_analysis_memory.fill(0.f);
+ prev_analysis_signal_spectrum.fill(1.f);
+ process_analysis_memory.fill(0.f);
+ process_synthesis_memory.fill(0.f);
+ for (auto& d : process_delay_memory) {
+ d.fill(0.f);
+ }
+}
+
+NoiseSuppressor::NoiseSuppressor(const NsConfig& config,
+ size_t sample_rate_hz,
+ size_t num_channels)
+ : num_bands_(NumBandsForRate(sample_rate_hz)),
+ num_channels_(num_channels),
+ suppression_params_(config.target_level),
+ filter_bank_states_heap_(NumChannelsOnHeap(num_channels_)),
+ upper_band_gains_heap_(NumChannelsOnHeap(num_channels_)),
+ energies_before_filtering_heap_(NumChannelsOnHeap(num_channels_)),
+ gain_adjustments_heap_(NumChannelsOnHeap(num_channels_)),
+ channels_(num_channels_) {
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ channels_[ch] =
+ std::make_unique<ChannelState>(suppression_params_, num_bands_);
+ }
+}
+
+void NoiseSuppressor::AggregateWienerFilters(
+ rtc::ArrayView<float, kFftSizeBy2Plus1> filter) const {
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> filter0 =
+ channels_[0]->wiener_filter.get_filter();
+ std::copy(filter0.begin(), filter0.end(), filter.begin());
+
+ for (size_t ch = 1; ch < num_channels_; ++ch) {
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> filter_ch =
+ channels_[ch]->wiener_filter.get_filter();
+
+ for (size_t k = 0; k < kFftSizeBy2Plus1; ++k) {
+ filter[k] = std::min(filter[k], filter_ch[k]);
+ }
+ }
+}
+
+void NoiseSuppressor::Analyze(const AudioBuffer& audio) {
+ // Prepare the noise estimator for the analysis stage.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ channels_[ch]->noise_estimator.PrepareAnalysis();
+ }
+
+ // Check for zero frames.
+ bool zero_frame = true;
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ rtc::ArrayView<const float, kNsFrameSize> y_band0(
+ &audio.split_bands_const(ch)[0][0], kNsFrameSize);
+ float energy = ComputeEnergyOfExtendedFrame(
+ y_band0, channels_[ch]->analyze_analysis_memory);
+ if (energy > 0.f) {
+ zero_frame = false;
+ break;
+ }
+ }
+
+ if (zero_frame) {
+ // We want to avoid updating statistics in this case:
+ // Updating feature statistics when we have zeros only will cause
+ // thresholds to move towards zero signal situations. This in turn has the
+ // effect that once the signal is "turned on" (non-zero values) everything
+ // will be treated as speech and there is no noise suppression effect.
+ // Depending on the duration of the inactive signal it takes a
+ // considerable amount of time for the system to learn what is noise and
+ // what is speech.
+ return;
+ }
+
+ // Only update analysis counter for frames that are properly analyzed.
+ if (++num_analyzed_frames_ < 0) {
+ num_analyzed_frames_ = 0;
+ }
+
+ // Analyze all channels.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ std::unique_ptr<ChannelState>& ch_p = channels_[ch];
+ rtc::ArrayView<const float, kNsFrameSize> y_band0(
+ &audio.split_bands_const(ch)[0][0], kNsFrameSize);
+
+ // Form an extended frame and apply analysis filter bank windowing.
+ std::array<float, kFftSize> extended_frame;
+ FormExtendedFrame(y_band0, ch_p->analyze_analysis_memory, extended_frame);
+ ApplyFilterBankWindow(extended_frame);
+
+ // Compute the magnitude spectrum.
+ std::array<float, kFftSize> real;
+ std::array<float, kFftSize> imag;
+ fft_.Fft(extended_frame, real, imag);
+
+ std::array<float, kFftSizeBy2Plus1> signal_spectrum;
+ ComputeMagnitudeSpectrum(real, imag, signal_spectrum);
+
+ // Compute energies.
+ float signal_energy = 0.f;
+ for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
+ signal_energy += real[i] * real[i] + imag[i] * imag[i];
+ }
+ signal_energy /= kFftSizeBy2Plus1;
+
+ float signal_spectral_sum = 0.f;
+ for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
+ signal_spectral_sum += signal_spectrum[i];
+ }
+
+ // Estimate the noise spectra and the probability estimates of speech
+ // presence.
+ ch_p->noise_estimator.PreUpdate(num_analyzed_frames_, signal_spectrum,
+ signal_spectral_sum);
+
+ std::array<float, kFftSizeBy2Plus1> post_snr;
+ std::array<float, kFftSizeBy2Plus1> prior_snr;
+ ComputeSnr(ch_p->wiener_filter.get_filter(),
+ ch_p->prev_analysis_signal_spectrum, signal_spectrum,
+ ch_p->noise_estimator.get_prev_noise_spectrum(),
+ ch_p->noise_estimator.get_noise_spectrum(), prior_snr, post_snr);
+
+ ch_p->speech_probability_estimator.Update(
+ num_analyzed_frames_, prior_snr, post_snr,
+ ch_p->noise_estimator.get_conservative_noise_spectrum(),
+ signal_spectrum, signal_spectral_sum, signal_energy);
+
+ ch_p->noise_estimator.PostUpdate(
+ ch_p->speech_probability_estimator.get_probability(), signal_spectrum);
+
+ // Store the magnitude spectrum to make it avalilable for the process
+ // method.
+ std::copy(signal_spectrum.begin(), signal_spectrum.end(),
+ ch_p->prev_analysis_signal_spectrum.begin());
+ }
+}
+
+void NoiseSuppressor::Process(AudioBuffer* audio) {
+ // Select the space for storing data during the processing.
+ std::array<FilterBankState, kMaxNumChannelsOnStack> filter_bank_states_stack;
+ rtc::ArrayView<FilterBankState> filter_bank_states(
+ filter_bank_states_stack.data(), num_channels_);
+ std::array<float, kMaxNumChannelsOnStack> upper_band_gains_stack;
+ rtc::ArrayView<float> upper_band_gains(upper_band_gains_stack.data(),
+ num_channels_);
+ std::array<float, kMaxNumChannelsOnStack> energies_before_filtering_stack;
+ rtc::ArrayView<float> energies_before_filtering(
+ energies_before_filtering_stack.data(), num_channels_);
+ std::array<float, kMaxNumChannelsOnStack> gain_adjustments_stack;
+ rtc::ArrayView<float> gain_adjustments(gain_adjustments_stack.data(),
+ num_channels_);
+ if (NumChannelsOnHeap(num_channels_) > 0) {
+ // If the stack-allocated space is too small, use the heap for storing the
+ // data.
+ filter_bank_states = rtc::ArrayView<FilterBankState>(
+ filter_bank_states_heap_.data(), num_channels_);
+ upper_band_gains =
+ rtc::ArrayView<float>(upper_band_gains_heap_.data(), num_channels_);
+ energies_before_filtering = rtc::ArrayView<float>(
+ energies_before_filtering_heap_.data(), num_channels_);
+ gain_adjustments =
+ rtc::ArrayView<float>(gain_adjustments_heap_.data(), num_channels_);
+ }
+
+ // Compute the suppression filters for all channels.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ // Form an extended frame and apply analysis filter bank windowing.
+ rtc::ArrayView<float, kNsFrameSize> y_band0(&audio->split_bands(ch)[0][0],
+ kNsFrameSize);
+
+ FormExtendedFrame(y_band0, channels_[ch]->process_analysis_memory,
+ filter_bank_states[ch].extended_frame);
+
+ ApplyFilterBankWindow(filter_bank_states[ch].extended_frame);
+
+ energies_before_filtering[ch] =
+ ComputeEnergyOfExtendedFrame(filter_bank_states[ch].extended_frame);
+
+ // Perform filter bank analysis and compute the magnitude spectrum.
+ fft_.Fft(filter_bank_states[ch].extended_frame, filter_bank_states[ch].real,
+ filter_bank_states[ch].imag);
+
+ std::array<float, kFftSizeBy2Plus1> signal_spectrum;
+ ComputeMagnitudeSpectrum(filter_bank_states[ch].real,
+ filter_bank_states[ch].imag, signal_spectrum);
+
+ // Compute the frequency domain gain filter for noise attenuation.
+ channels_[ch]->wiener_filter.Update(
+ num_analyzed_frames_,
+ channels_[ch]->noise_estimator.get_noise_spectrum(),
+ channels_[ch]->noise_estimator.get_prev_noise_spectrum(),
+ channels_[ch]->noise_estimator.get_parametric_noise_spectrum(),
+ signal_spectrum);
+
+ if (num_bands_ > 1) {
+ // Compute the time-domain gain for attenuating the noise in the upper
+ // bands.
+
+ upper_band_gains[ch] = ComputeUpperBandsGain(
+ suppression_params_.minimum_attenuating_gain,
+ channels_[ch]->wiener_filter.get_filter(),
+ channels_[ch]->speech_probability_estimator.get_probability(),
+ channels_[ch]->prev_analysis_signal_spectrum, signal_spectrum);
+ }
+ }
+
+ // Only do the below processing if the output of the audio processing module
+ // is used.
+ if (!capture_output_used_) {
+ return;
+ }
+
+ // Aggregate the Wiener filters for all channels.
+ std::array<float, kFftSizeBy2Plus1> filter_data;
+ rtc::ArrayView<const float, kFftSizeBy2Plus1> filter = filter_data;
+ if (num_channels_ == 1) {
+ filter = channels_[0]->wiener_filter.get_filter();
+ } else {
+ AggregateWienerFilters(filter_data);
+ }
+
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ // Apply the filter to the lower band.
+ for (size_t i = 0; i < kFftSizeBy2Plus1; ++i) {
+ filter_bank_states[ch].real[i] *= filter[i];
+ filter_bank_states[ch].imag[i] *= filter[i];
+ }
+ }
+
+ // Perform filter bank synthesis
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ fft_.Ifft(filter_bank_states[ch].real, filter_bank_states[ch].imag,
+ filter_bank_states[ch].extended_frame);
+ }
+
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ const float energy_after_filtering =
+ ComputeEnergyOfExtendedFrame(filter_bank_states[ch].extended_frame);
+
+ // Apply synthesis window.
+ ApplyFilterBankWindow(filter_bank_states[ch].extended_frame);
+
+ // Compute the adjustment of the noise attenuation filter based on the
+ // effect of the attenuation.
+ gain_adjustments[ch] =
+ channels_[ch]->wiener_filter.ComputeOverallScalingFactor(
+ num_analyzed_frames_,
+ channels_[ch]->speech_probability_estimator.get_prior_probability(),
+ energies_before_filtering[ch], energy_after_filtering);
+ }
+
+ // Select and apply adjustment of the noise attenuation filter based on the
+ // effect of the attenuation.
+ float gain_adjustment = gain_adjustments[0];
+ for (size_t ch = 1; ch < num_channels_; ++ch) {
+ gain_adjustment = std::min(gain_adjustment, gain_adjustments[ch]);
+ }
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ for (size_t i = 0; i < kFftSize; ++i) {
+ filter_bank_states[ch].extended_frame[i] =
+ gain_adjustment * filter_bank_states[ch].extended_frame[i];
+ }
+ }
+
+ // Use overlap-and-add to form the output frame of the lowest band.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ rtc::ArrayView<float, kNsFrameSize> y_band0(&audio->split_bands(ch)[0][0],
+ kNsFrameSize);
+ OverlapAndAdd(filter_bank_states[ch].extended_frame,
+ channels_[ch]->process_synthesis_memory, y_band0);
+ }
+
+ if (num_bands_ > 1) {
+ // Select the noise attenuating gain to apply to the upper band.
+ float upper_band_gain = upper_band_gains[0];
+ for (size_t ch = 1; ch < num_channels_; ++ch) {
+ upper_band_gain = std::min(upper_band_gain, upper_band_gains[ch]);
+ }
+
+ // Process the upper bands.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ for (size_t b = 1; b < num_bands_; ++b) {
+ // Delay the upper bands to match the delay of the filterbank applied to
+ // the lowest band.
+ rtc::ArrayView<float, kNsFrameSize> y_band(
+ &audio->split_bands(ch)[b][0], kNsFrameSize);
+ std::array<float, kNsFrameSize> delayed_frame;
+ DelaySignal(y_band, channels_[ch]->process_delay_memory[b - 1],
+ delayed_frame);
+
+ // Apply the time-domain noise-attenuating gain.
+ for (size_t j = 0; j < kNsFrameSize; j++) {
+ y_band[j] = upper_band_gain * delayed_frame[j];
+ }
+ }
+ }
+ }
+
+ // Limit the output the allowed range.
+ for (size_t ch = 0; ch < num_channels_; ++ch) {
+ for (size_t b = 0; b < num_bands_; ++b) {
+ rtc::ArrayView<float, kNsFrameSize> y_band(&audio->split_bands(ch)[b][0],
+ kNsFrameSize);
+ for (size_t j = 0; j < kNsFrameSize; j++) {
+ y_band[j] = std::min(std::max(y_band[j], -32768.f), 32767.f);
+ }
+ }
+ }
+}
+
+} // namespace webrtc