tor-browser

noise_suppressor.cc (22353B)

---

/*
*  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 <algorithm>
#include <array>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <memory>

#include "api/array_view.h"
#include "modules/audio_processing/audio_buffer.h"
#include "modules/audio_processing/ns/fast_math.h"
#include "modules/audio_processing/ns/ns_common.h"
#include "modules/audio_processing/ns/ns_config.h"
#include "modules/audio_processing/ns/suppression_params.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(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(ArrayView<const float, kNsFrameSize> frame,
                      ArrayView<float, kFftSize - kNsFrameSize> old_data,
                      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(ArrayView<const float, kFftSize> extended_frame,
                  ArrayView<float, kOverlapSize> overlap_memory,
                  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(ArrayView<const float, kNsFrameSize> frame,
                ArrayView<float, kFftSize - kNsFrameSize> delay_buffer,
                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(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(
   ArrayView<const float, kNsFrameSize> frame,
   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(
   ArrayView<const float, kFftSize> real,
   ArrayView<const float, kFftSize> imag,
   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(ArrayView<const float, kFftSizeBy2Plus1> filter,
               ArrayView<const float> prev_signal_spectrum,
               ArrayView<const float> signal_spectrum,
               ArrayView<const float> prev_noise_spectrum,
               ArrayView<const float> noise_spectrum,
               ArrayView<float> prior_snr,
               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,
   ArrayView<const float, kFftSizeBy2Plus1> filter,
   ArrayView<const float> speech_probability,
   ArrayView<const float, kFftSizeBy2Plus1> prev_analysis_signal_spectrum,
   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(
   ArrayView<float, kFftSizeBy2Plus1> filter) const {
 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) {
   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) {
   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];
   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;
 ArrayView<FilterBankState> filter_bank_states(filter_bank_states_stack.data(),
                                               num_channels_);
 std::array<float, kMaxNumChannelsOnStack> upper_band_gains_stack;
 ArrayView<float> upper_band_gains(upper_band_gains_stack.data(),
                                   num_channels_);
 std::array<float, kMaxNumChannelsOnStack> energies_before_filtering_stack;
 ArrayView<float> energies_before_filtering(
     energies_before_filtering_stack.data(), num_channels_);
 std::array<float, kMaxNumChannelsOnStack> gain_adjustments_stack;
 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 = ArrayView<FilterBankState>(
       filter_bank_states_heap_.data(), num_channels_);
   upper_band_gains =
       ArrayView<float>(upper_band_gains_heap_.data(), num_channels_);
   energies_before_filtering =
       ArrayView<float>(energies_before_filtering_heap_.data(), num_channels_);
   gain_adjustments =
       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.
   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;
 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) {
   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.
       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) {
     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