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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