modules/audio_processing/ns/noise_suppressor.cc - src - Git at Google

 /*
  *  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
	/*
	* 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