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/*
* Copyright (c) 2013 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/transient/transient_suppressor.h"
#include <math.h>
#include <string.h>
#include <cmath>
#include <complex>
#include <deque>
#include <set>
#include "common_audio/fft4g.h"
#include "common_audio/include/audio_util.h"
#include "common_audio/signal_processing/include/signal_processing_library.h"
#include "modules/audio_processing/ns/windows_private.h"
#include "modules/audio_processing/transient/common.h"
#include "modules/audio_processing/transient/transient_detector.h"
#include "rtc_base/checks.h"
#include "rtc_base/logging.h"
#include "typedefs.h" // NOLINT(build/include)
namespace webrtc {
static const float kMeanIIRCoefficient = 0.5f;
static const float kVoiceThreshold = 0.02f;
// TODO(aluebs): Check if these values work also for 48kHz.
static const size_t kMinVoiceBin = 3;
static const size_t kMaxVoiceBin = 60;
namespace {
float ComplexMagnitude(float a, float b) {
return std::abs(a) + std::abs(b);
}
} // namespace
TransientSuppressor::TransientSuppressor()
: data_length_(0),
detection_length_(0),
analysis_length_(0),
buffer_delay_(0),
complex_analysis_length_(0),
num_channels_(0),
window_(NULL),
detector_smoothed_(0.f),
keypress_counter_(0),
chunks_since_keypress_(0),
detection_enabled_(false),
suppression_enabled_(false),
use_hard_restoration_(false),
chunks_since_voice_change_(0),
seed_(182),
using_reference_(false) {
}
TransientSuppressor::~TransientSuppressor() {}
int TransientSuppressor::Initialize(int sample_rate_hz,
int detection_rate_hz,
int num_channels) {
switch (sample_rate_hz) {
case ts::kSampleRate8kHz:
analysis_length_ = 128u;
window_ = kBlocks80w128;
break;
case ts::kSampleRate16kHz:
analysis_length_ = 256u;
window_ = kBlocks160w256;
break;
case ts::kSampleRate32kHz:
analysis_length_ = 512u;
window_ = kBlocks320w512;
break;
case ts::kSampleRate48kHz:
analysis_length_ = 1024u;
window_ = kBlocks480w1024;
break;
default:
return -1;
}
if (detection_rate_hz != ts::kSampleRate8kHz &&
detection_rate_hz != ts::kSampleRate16kHz &&
detection_rate_hz != ts::kSampleRate32kHz &&
detection_rate_hz != ts::kSampleRate48kHz) {
return -1;
}
if (num_channels <= 0) {
return -1;
}
detector_.reset(new TransientDetector(detection_rate_hz));
data_length_ = sample_rate_hz * ts::kChunkSizeMs / 1000;
if (data_length_ > analysis_length_) {
RTC_NOTREACHED();
return -1;
}
buffer_delay_ = analysis_length_ - data_length_;
complex_analysis_length_ = analysis_length_ / 2 + 1;
RTC_DCHECK_GE(complex_analysis_length_, kMaxVoiceBin);
num_channels_ = num_channels;
in_buffer_.reset(new float[analysis_length_ * num_channels_]);
memset(in_buffer_.get(),
0,
analysis_length_ * num_channels_ * sizeof(in_buffer_[0]));
detection_length_ = detection_rate_hz * ts::kChunkSizeMs / 1000;
detection_buffer_.reset(new float[detection_length_]);
memset(detection_buffer_.get(),
0,
detection_length_ * sizeof(detection_buffer_[0]));
out_buffer_.reset(new float[analysis_length_ * num_channels_]);
memset(out_buffer_.get(),
0,
analysis_length_ * num_channels_ * sizeof(out_buffer_[0]));
// ip[0] must be zero to trigger initialization using rdft().
size_t ip_length = 2 + sqrtf(analysis_length_);
ip_.reset(new size_t[ip_length]());
memset(ip_.get(), 0, ip_length * sizeof(ip_[0]));
wfft_.reset(new float[complex_analysis_length_ - 1]);
memset(wfft_.get(), 0, (complex_analysis_length_ - 1) * sizeof(wfft_[0]));
spectral_mean_.reset(new float[complex_analysis_length_ * num_channels_]);
memset(spectral_mean_.get(),
0,
complex_analysis_length_ * num_channels_ * sizeof(spectral_mean_[0]));
fft_buffer_.reset(new float[analysis_length_ + 2]);
memset(fft_buffer_.get(), 0, (analysis_length_ + 2) * sizeof(fft_buffer_[0]));
magnitudes_.reset(new float[complex_analysis_length_]);
memset(magnitudes_.get(),
0,
complex_analysis_length_ * sizeof(magnitudes_[0]));
mean_factor_.reset(new float[complex_analysis_length_]);
static const float kFactorHeight = 10.f;
static const float kLowSlope = 1.f;
static const float kHighSlope = 0.3f;
for (size_t i = 0; i < complex_analysis_length_; ++i) {
mean_factor_[i] =
kFactorHeight /
(1.f + exp(kLowSlope * static_cast<int>(i - kMinVoiceBin))) +
kFactorHeight /
(1.f + exp(kHighSlope * static_cast<int>(kMaxVoiceBin - i)));
}
detector_smoothed_ = 0.f;
keypress_counter_ = 0;
chunks_since_keypress_ = 0;
detection_enabled_ = false;
suppression_enabled_ = false;
use_hard_restoration_ = false;
chunks_since_voice_change_ = 0;
seed_ = 182;
using_reference_ = false;
return 0;
}
int TransientSuppressor::Suppress(float* data,
size_t data_length,
int num_channels,
const float* detection_data,
size_t detection_length,
const float* reference_data,
size_t reference_length,
float voice_probability,
bool key_pressed) {
if (!data || data_length != data_length_ || num_channels != num_channels_ ||
detection_length != detection_length_ || voice_probability < 0 ||
voice_probability > 1) {
return -1;
}
UpdateKeypress(key_pressed);
UpdateBuffers(data);
int result = 0;
if (detection_enabled_) {
UpdateRestoration(voice_probability);
if (!detection_data) {
// Use the input data of the first channel if special detection data is
// not supplied.
detection_data = &in_buffer_[buffer_delay_];
}
float detector_result = detector_->Detect(
detection_data, detection_length, reference_data, reference_length);
if (detector_result < 0) {
return -1;
}
using_reference_ = detector_->using_reference();
// |detector_smoothed_| follows the |detector_result| when this last one is
// increasing, but has an exponential decaying tail to be able to suppress
// the ringing of keyclicks.
float smooth_factor = using_reference_ ? 0.6 : 0.1;
detector_smoothed_ = detector_result >= detector_smoothed_
? detector_result
: smooth_factor * detector_smoothed_ +
(1 - smooth_factor) * detector_result;
for (int i = 0; i < num_channels_; ++i) {
Suppress(&in_buffer_[i * analysis_length_],
&spectral_mean_[i * complex_analysis_length_],
&out_buffer_[i * analysis_length_]);
}
}
// If the suppression isn't enabled, we use the in buffer to delay the signal
// appropriately. This also gives time for the out buffer to be refreshed with
// new data between detection and suppression getting enabled.
for (int i = 0; i < num_channels_; ++i) {
memcpy(&data[i * data_length_],
suppression_enabled_ ? &out_buffer_[i * analysis_length_]
: &in_buffer_[i * analysis_length_],
data_length_ * sizeof(*data));
}
return result;
}
// This should only be called when detection is enabled. UpdateBuffers() must
// have been called. At return, |out_buffer_| will be filled with the
// processed output.
void TransientSuppressor::Suppress(float* in_ptr,
float* spectral_mean,
float* out_ptr) {
// Go to frequency domain.
for (size_t i = 0; i < analysis_length_; ++i) {
// TODO(aluebs): Rename windows
fft_buffer_[i] = in_ptr[i] * window_[i];
}
WebRtc_rdft(analysis_length_, 1, fft_buffer_.get(), ip_.get(), wfft_.get());
// Since WebRtc_rdft puts R[n/2] in fft_buffer_[1], we move it to the end
// for convenience.
fft_buffer_[analysis_length_] = fft_buffer_[1];
fft_buffer_[analysis_length_ + 1] = 0.f;
fft_buffer_[1] = 0.f;
for (size_t i = 0; i < complex_analysis_length_; ++i) {
magnitudes_[i] = ComplexMagnitude(fft_buffer_[i * 2],
fft_buffer_[i * 2 + 1]);
}
// Restore audio if necessary.
if (suppression_enabled_) {
if (use_hard_restoration_) {
HardRestoration(spectral_mean);
} else {
SoftRestoration(spectral_mean);
}
}
// Update the spectral mean.
for (size_t i = 0; i < complex_analysis_length_; ++i) {
spectral_mean[i] = (1 - kMeanIIRCoefficient) * spectral_mean[i] +
kMeanIIRCoefficient * magnitudes_[i];
}
// Back to time domain.
// Put R[n/2] back in fft_buffer_[1].
fft_buffer_[1] = fft_buffer_[analysis_length_];
WebRtc_rdft(analysis_length_,
-1,
fft_buffer_.get(),
ip_.get(),
wfft_.get());
const float fft_scaling = 2.f / analysis_length_;
for (size_t i = 0; i < analysis_length_; ++i) {
out_ptr[i] += fft_buffer_[i] * window_[i] * fft_scaling;
}
}
void TransientSuppressor::UpdateKeypress(bool key_pressed) {
const int kKeypressPenalty = 1000 / ts::kChunkSizeMs;
const int kIsTypingThreshold = 1000 / ts::kChunkSizeMs;
const int kChunksUntilNotTyping = 4000 / ts::kChunkSizeMs; // 4 seconds.
if (key_pressed) {
keypress_counter_ += kKeypressPenalty;
chunks_since_keypress_ = 0;
detection_enabled_ = true;
}
keypress_counter_ = std::max(0, keypress_counter_ - 1);
if (keypress_counter_ > kIsTypingThreshold) {
if (!suppression_enabled_) {
LOG(LS_INFO) << "[ts] Transient suppression is now enabled.";
}
suppression_enabled_ = true;
keypress_counter_ = 0;
}
if (detection_enabled_ &&
++chunks_since_keypress_ > kChunksUntilNotTyping) {
if (suppression_enabled_) {
LOG(LS_INFO) << "[ts] Transient suppression is now disabled.";
}
detection_enabled_ = false;
suppression_enabled_ = false;
keypress_counter_ = 0;
}
}
void TransientSuppressor::UpdateRestoration(float voice_probability) {
const int kHardRestorationOffsetDelay = 3;
const int kHardRestorationOnsetDelay = 80;
bool not_voiced = voice_probability < kVoiceThreshold;
if (not_voiced == use_hard_restoration_) {
chunks_since_voice_change_ = 0;
} else {
++chunks_since_voice_change_;
if ((use_hard_restoration_ &&
chunks_since_voice_change_ > kHardRestorationOffsetDelay) ||
(!use_hard_restoration_ &&
chunks_since_voice_change_ > kHardRestorationOnsetDelay)) {
use_hard_restoration_ = not_voiced;
chunks_since_voice_change_ = 0;
}
}
}
// Shift buffers to make way for new data. Must be called after
// |detection_enabled_| is updated by UpdateKeypress().
void TransientSuppressor::UpdateBuffers(float* data) {
// TODO(aluebs): Change to ring buffer.
memmove(in_buffer_.get(),
&in_buffer_[data_length_],
(buffer_delay_ + (num_channels_ - 1) * analysis_length_) *
sizeof(in_buffer_[0]));
// Copy new chunk to buffer.
for (int i = 0; i < num_channels_; ++i) {
memcpy(&in_buffer_[buffer_delay_ + i * analysis_length_],
&data[i * data_length_],
data_length_ * sizeof(*data));
}
if (detection_enabled_) {
// Shift previous chunk in out buffer.
memmove(out_buffer_.get(),
&out_buffer_[data_length_],
(buffer_delay_ + (num_channels_ - 1) * analysis_length_) *
sizeof(out_buffer_[0]));
// Initialize new chunk in out buffer.
for (int i = 0; i < num_channels_; ++i) {
memset(&out_buffer_[buffer_delay_ + i * analysis_length_],
0,
data_length_ * sizeof(out_buffer_[0]));
}
}
}
// Restores the unvoiced signal if a click is present.
// Attenuates by a certain factor every peak in the |fft_buffer_| that exceeds
// the spectral mean. The attenuation depends on |detector_smoothed_|.
// If a restoration takes place, the |magnitudes_| are updated to the new value.
void TransientSuppressor::HardRestoration(float* spectral_mean) {
const float detector_result =
1.f - pow(1.f - detector_smoothed_, using_reference_ ? 200.f : 50.f);
// To restore, we get the peaks in the spectrum. If higher than the previous
// spectral mean we adjust them.
for (size_t i = 0; i < complex_analysis_length_; ++i) {
if (magnitudes_[i] > spectral_mean[i] && magnitudes_[i] > 0) {
// RandU() generates values on [0, int16::max()]
const float phase = 2 * ts::kPi * WebRtcSpl_RandU(&seed_) /
std::numeric_limits<int16_t>::max();
const float scaled_mean = detector_result * spectral_mean[i];
fft_buffer_[i * 2] = (1 - detector_result) * fft_buffer_[i * 2] +
scaled_mean * cosf(phase);
fft_buffer_[i * 2 + 1] = (1 - detector_result) * fft_buffer_[i * 2 + 1] +
scaled_mean * sinf(phase);
magnitudes_[i] = magnitudes_[i] -
detector_result * (magnitudes_[i] - spectral_mean[i]);
}
}
}
// Restores the voiced signal if a click is present.
// Attenuates by a certain factor every peak in the |fft_buffer_| that exceeds
// the spectral mean and that is lower than some function of the current block
// frequency mean. The attenuation depends on |detector_smoothed_|.
// If a restoration takes place, the |magnitudes_| are updated to the new value.
void TransientSuppressor::SoftRestoration(float* spectral_mean) {
// Get the spectral magnitude mean of the current block.
float block_frequency_mean = 0;
for (size_t i = kMinVoiceBin; i < kMaxVoiceBin; ++i) {
block_frequency_mean += magnitudes_[i];
}
block_frequency_mean /= (kMaxVoiceBin - kMinVoiceBin);
// To restore, we get the peaks in the spectrum. If higher than the
// previous spectral mean and lower than a factor of the block mean
// we adjust them. The factor is a double sigmoid that has a minimum in the
// voice frequency range (300Hz - 3kHz).
for (size_t i = 0; i < complex_analysis_length_; ++i) {
if (magnitudes_[i] > spectral_mean[i] && magnitudes_[i] > 0 &&
(using_reference_ ||
magnitudes_[i] < block_frequency_mean * mean_factor_[i])) {
const float new_magnitude =
magnitudes_[i] -
detector_smoothed_ * (magnitudes_[i] - spectral_mean[i]);
const float magnitude_ratio = new_magnitude / magnitudes_[i];
fft_buffer_[i * 2] *= magnitude_ratio;
fft_buffer_[i * 2 + 1] *= magnitude_ratio;
magnitudes_[i] = new_magnitude;
}
}
}
} // namespace webrtc