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Initial SIE commit: migrating existing code

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Moved exact existing intelligibility enhancement implementation into new
repository for reference when making further changes.

Note: this cl does not add these files to any gyp.

Original cl is at https://webrtc-codereview.appspot.com/52719004/ .

TBR=aluebs@webrtc.org

Review URL: https://codereview.webrtc.org/1177953006.

Cr-Commit-Position: refs/heads/master@{#9441}
This commit is contained in:
ekm
2015-06-15 13:02:24 -07:00
parent fe23090c61
commit 030249dd24
5 changed files with 1131 additions and 0 deletions
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webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc Normal file
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/*
* Copyright (c) 2014 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 "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
#include <cmath>
#include <cstdlib>
#include <algorithm>
#include "webrtc/base/checks.h"
#include "webrtc/common_audio/vad/include/webrtc_vad.h"
#include "webrtc/common_audio/window_generator.h"
using std::complex;
using std::max;
using std::min;
namespace webrtc {
const int IntelligibilityEnhancer::kErbResolution = 2;
const int IntelligibilityEnhancer::kWindowSizeMs = 2;
// The size of the chunk provided by APM, in milliseconds.
const int IntelligibilityEnhancer::kChunkSizeMs = 10;
const int IntelligibilityEnhancer::kAnalyzeRate = 800;
const int IntelligibilityEnhancer::kVarianceRate = 2;
const float IntelligibilityEnhancer::kClipFreq = 200.0f;
const float IntelligibilityEnhancer::kConfigRho = 0.02f;
const float IntelligibilityEnhancer::kKbdAlpha = 1.5f;
const float IntelligibilityEnhancer::kGainChangeLimit = 0.0125f;
using VarianceType = intelligibility::VarianceArray::StepType;
IntelligibilityEnhancer::TransformCallback::TransformCallback(
IntelligibilityEnhancer* parent,
IntelligibilityEnhancer::AudioSource source)
: parent_(parent),
source_(source) {}
void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
const complex<float>* const* in_block,
int in_channels, int frames, int /* out_channels */,
complex<float>* const* out_block) {
DCHECK_EQ(parent_->freqs_, frames);
for (int i = 0; i < in_channels; ++i) {
parent_->DispatchAudio(source_, in_block[i], out_block[i]);
}
}
IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
int sample_rate_hz,
int channels,
int cv_type, float cv_alpha,
int cv_win,
int analysis_rate,
int variance_rate,
float gain_limit)
: freqs_(RealFourier::ComplexLength(RealFourier::FftOrder(
sample_rate_hz * kWindowSizeMs / 1000))),
window_size_(1 << RealFourier::FftOrder(freqs_)),
chunk_length_(sample_rate_hz * kChunkSizeMs / 1000),
bank_size_(GetBankSize(sample_rate_hz, erb_resolution)),
sample_rate_hz_(sample_rate_hz),
erb_resolution_(erb_resolution),
channels_(channels),
analysis_rate_(analysis_rate),
variance_rate_(variance_rate),
clear_variance_(freqs_, static_cast<VarianceType>(cv_type), cv_win,
cv_alpha),
noise_variance_(freqs_, VarianceType::kStepInfinite, 475, 0.01f),
filtered_clear_var_(new float[bank_size_]),
filtered_noise_var_(new float[bank_size_]),
filter_bank_(nullptr),
center_freqs_(new float[bank_size_]),
rho_(new float[bank_size_]),
gains_eq_(new float[bank_size_]),
gain_applier_(freqs_, gain_limit),
temp_out_buffer_(nullptr),
input_audio_(new float*[channels]),
kbd_window_(new float[window_size_]),
render_callback_(this, AudioSource::kRenderStream),
capture_callback_(this, AudioSource::kCaptureStream),
block_count_(0),
analysis_step_(0),
vad_high_(nullptr),
vad_low_(nullptr),
vad_tmp_buffer_(new int16_t[chunk_length_]) {
DCHECK_LE(kConfigRho, 1.0f);
CreateErbBank();
WebRtcVad_Create(&vad_high_);
WebRtcVad_Init(vad_high_);
WebRtcVad_set_mode(vad_high_, 0); // high likelihood of speech
WebRtcVad_Create(&vad_low_);
WebRtcVad_Init(vad_low_);
WebRtcVad_set_mode(vad_low_, 3); // low likelihood of speech
temp_out_buffer_ = static_cast<float**>(malloc(
sizeof(*temp_out_buffer_) * channels_ +
sizeof(**temp_out_buffer_) * chunk_length_ * channels_));
for (int i = 0; i < channels_; ++i) {
temp_out_buffer_[i] = reinterpret_cast<float*>(temp_out_buffer_ + channels_)
+ chunk_length_ * i;
}
for (int i = 0; i < bank_size_; ++i) {
rho_[i] = kConfigRho * kConfigRho;
}
float freqs_khz = kClipFreq / 1000.0f;
int erb_index = static_cast<int>(ceilf(11.17f * logf((freqs_khz + 0.312f) /
(freqs_khz + 14.6575f))
+ 43.0f));
start_freq_ = max(1, erb_index * kErbResolution);
WindowGenerator::KaiserBesselDerived(kKbdAlpha, window_size_,
kbd_window_.get());
render_mangler_.reset(new LappedTransform(channels_, channels_,
chunk_length_,
kbd_window_.get(),
window_size_,
window_size_ / 2,
&render_callback_));
capture_mangler_.reset(new LappedTransform(channels_, channels_,
chunk_length_,
kbd_window_.get(),
window_size_,
window_size_ / 2,
&capture_callback_));
}
IntelligibilityEnhancer::~IntelligibilityEnhancer() {
WebRtcVad_Free(vad_low_);
WebRtcVad_Free(vad_high_);
free(filter_bank_);
}
void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio) {
for (int i = 0; i < chunk_length_; ++i) {
vad_tmp_buffer_[i] = (int16_t)audio[0][i];
}
has_voice_low_ = WebRtcVad_Process(vad_low_, sample_rate_hz_,
vad_tmp_buffer_.get(), chunk_length_) == 1;
render_mangler_->ProcessChunk(audio, temp_out_buffer_);
for (int i = 0; i < channels_; ++i) {
memcpy(audio[i], temp_out_buffer_[i],
chunk_length_ * sizeof(**temp_out_buffer_));
}
}
void IntelligibilityEnhancer::ProcessCaptureAudio(float* const* audio) {
for (int i = 0; i < chunk_length_; ++i) {
vad_tmp_buffer_[i] = (int16_t)audio[0][i];
}
// TODO(bercic): the VAD was always detecting voice in the noise stream,
// no matter what the aggressiveness, so it was temporarily disabled here
//if (WebRtcVad_Process(vad_high_, sample_rate_hz_, vad_tmp_buffer_.get(),
// chunk_length_) == 1) {
// printf("capture HAS speech\n");
// return;
//}
//printf("capture NO speech\n");
capture_mangler_->ProcessChunk(audio, temp_out_buffer_);
}
void IntelligibilityEnhancer::DispatchAudio(
IntelligibilityEnhancer::AudioSource source,
const complex<float>* in_block, complex<float>* out_block) {
switch (source) {
case kRenderStream:
ProcessClearBlock(in_block, out_block);
break;
case kCaptureStream:
ProcessNoiseBlock(in_block, out_block);
break;
}
}
void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
complex<float>* out_block) {
float power_target;
if (block_count_ < 2) {
memset(out_block, 0, freqs_ * sizeof(*out_block));
++block_count_;
return;
}
if (has_voice_low_ || true) {
clear_variance_.Step(in_block, false);
power_target = std::accumulate(clear_variance_.variance(),
clear_variance_.variance() + freqs_, 0.0f);
if (block_count_ % analysis_rate_ == analysis_rate_ - 1) {
AnalyzeClearBlock(power_target);
++analysis_step_;
if (analysis_step_ == variance_rate_) {
analysis_step_ = 0;
clear_variance_.Clear();
noise_variance_.Clear();
}
}
++block_count_;
}
/* efidata(n,:) = sqrt(b(n)) * fidata(n,:) */
gain_applier_.Apply(in_block, out_block);
}
void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
FilterVariance(clear_variance_.variance(), filtered_clear_var_.get());
FilterVariance(noise_variance_.variance(), filtered_noise_var_.get());
/* lambda binary search */
float lambda_bot = -1.0f, lambda_top = -10e-18f, lambda;
float power_bot, power_top, power;
SolveEquation14(lambda_top, start_freq_, gains_eq_.get());
power_top = DotProduct(gains_eq_.get(), filtered_clear_var_.get(),
bank_size_);
SolveEquation14(lambda_bot, start_freq_, gains_eq_.get());
power_bot = DotProduct(gains_eq_.get(), filtered_clear_var_.get(),
bank_size_);
DCHECK(power_target >= power_bot && power_target <= power_top);
float power_ratio = 2.0f;
int iters = 0;
while (fabs(power_ratio - 1.0f) > 0.001f && iters <= 100) {
lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
SolveEquation14(lambda, start_freq_, gains_eq_.get());
power = DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
if (power < power_target) {
lambda_bot = lambda;
} else {
lambda_top = lambda;
}
power_ratio = fabs(power / power_target);
++iters;
}
/* b = filterbank' * b */
float* gains = gain_applier_.target();
for (int i = 0; i < freqs_; ++i) {
gains[i] = 0.0f;
for (int j = 0; j < bank_size_; ++j) {
gains[i] = fmaf(filter_bank_[j][i], gains_eq_[j], gains[i]);
}
}
}
void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* in_block,
complex<float>* /*out_block*/) {
noise_variance_.Step(in_block);
}
int IntelligibilityEnhancer::GetBankSize(int sample_rate, int erb_resolution) {
float freq_limit = sample_rate / 2000.0f;
int erb_scale = ceilf(11.17f * logf((freq_limit + 0.312f) /
(freq_limit + 14.6575f)) + 43.0f);
return erb_scale * erb_resolution;
}
void IntelligibilityEnhancer::CreateErbBank() {
int lf = 1, rf = 4;
for (int i = 0; i < bank_size_; ++i) {
float abs_temp = fabsf((i + 1.0f) / static_cast<float>(erb_resolution_));
center_freqs_[i] = 676170.4f / (47.06538f - expf(0.08950404f * abs_temp));
center_freqs_[i] -= 14678.49f;
}
float last_center_freq = center_freqs_[bank_size_ - 1];
for (int i = 0; i < bank_size_; ++i) {
center_freqs_[i] *= 0.5f * sample_rate_hz_ / last_center_freq;
}
filter_bank_ = static_cast<float**>(malloc(
sizeof(*filter_bank_) * bank_size_ +
sizeof(**filter_bank_) * freqs_ * bank_size_));
for (int i = 0; i < bank_size_; ++i) {
filter_bank_[i] = reinterpret_cast<float*>(filter_bank_ + bank_size_) +
freqs_ * i;
}
for (int i = 1; i <= bank_size_; ++i) {
int lll, ll, rr, rrr;
lll = round(center_freqs_[max(1, i - lf) - 1] * freqs_ /
(0.5f * sample_rate_hz_));
ll = round(center_freqs_[max(1, i ) - 1] * freqs_ /
(0.5f * sample_rate_hz_));
lll = min(freqs_, max(lll, 1)) - 1;
ll = min(freqs_, max(ll, 1)) - 1;
rrr = round(center_freqs_[min(bank_size_, i + rf) - 1] * freqs_ /
(0.5f * sample_rate_hz_));
rr = round(center_freqs_[min(bank_size_, i + 1) - 1] * freqs_ /
(0.5f * sample_rate_hz_));
rrr = min(freqs_, max(rrr, 1)) - 1;
rr = min(freqs_, max(rr, 1)) - 1;
float step, element;
step = 1.0f / (ll - lll);
element = 0.0f;
for (int j = lll; j <= ll; ++j) {
filter_bank_[i - 1][j] = element;
element += step;
}
step = 1.0f / (rrr - rr);
element = 1.0f;
for (int j = rr; j <= rrr; ++j) {
filter_bank_[i - 1][j] = element;
element -= step;
}
for (int j = ll; j <= rr; ++j) {
filter_bank_[i - 1][j] = 1.0f;
}
}
float sum;
for (int i = 0; i < freqs_; ++i) {
sum = 0.0f;
for (int j = 0; j < bank_size_; ++j) {
sum += filter_bank_[j][i];
}
for (int j = 0; j < bank_size_; ++j) {
filter_bank_[j][i] /= sum;
}
}
}
void IntelligibilityEnhancer::SolveEquation14(float lambda, int start_freq,
float* sols) {
bool quadratic = (kConfigRho < 1.0f);
const float* var_x0 = filtered_clear_var_.get();
const float* var_n0 = filtered_noise_var_.get();
for (int n = 0; n < start_freq; ++n) {
sols[n] = 1.0f;
}
for (int n = start_freq - 1; n < bank_size_; ++n) {
float alpha0, beta0, gamma0;
gamma0 = 0.5f * rho_[n] * var_x0[n] * var_n0[n] +
lambda * var_x0[n] * var_n0[n] * var_n0[n];
beta0 = lambda * var_x0[n] * (2 - rho_[n]) * var_x0[n] * var_n0[n];
if (quadratic) {
alpha0 = lambda * var_x0[n] * (1 - rho_[n]) * var_x0[n] * var_x0[n];
sols[n] = (-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0))
/ (2 * alpha0);
} else {
sols[n] = -gamma0 / beta0;
}
sols[n] = fmax(0, sols[n]);
}
}
void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
for (int i = 0; i < bank_size_; ++i) {
result[i] = DotProduct(filter_bank_[i], var, freqs_);
}
}
float IntelligibilityEnhancer::DotProduct(const float* a, const float* b,
int length) {
float ret = 0.0f;
for (int i = 0; i < length; ++i) {
ret = fmaf(a[i], b[i], ret);
}
return ret;
}
} // namespace webrtc

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/*
* Copyright (c) 2014 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.
*/
#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
#include <complex>
#include "webrtc/common_audio/lapped_transform.h"
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
#include "webrtc/system_wrappers/interface/scoped_ptr.h"
struct WebRtcVadInst;
typedef struct WebRtcVadInst VadInst;
namespace webrtc {
// Speech intelligibility enhancement module. Reads render and capture
// audio streams and modifies the render stream with a set of gains per
// frequency bin to enhance speech against the noise background.
class IntelligibilityEnhancer {
public:
// Construct a new instance with the given filter bank resolution,
// sampling rate, number of channels and analysis rates.
// |analysis_rate| sets the number of input blocks (containing speech!)
// to elapse before a new gain computation is made. |variance_rate| specifies
// the number of gain recomputations after which the variances are reset.
// |cv_*| are parameters for the VarianceArray constructor for the
// lear speech stream.
// TODO(bercic): the |cv_*|, |*_rate| and |gain_limit| parameters should
// probably go away once fine tuning is done. They override the internal
// constants in the class (kGainChangeLimit, kAnalyzeRate, kVarianceRate).
IntelligibilityEnhancer(int erb_resolution, int sample_rate_hz, int channels,
int cv_type, float cv_alpha, int cv_win,
int analysis_rate, int variance_rate,
float gain_limit);
~IntelligibilityEnhancer();
void ProcessRenderAudio(float* const* audio);
void ProcessCaptureAudio(float* const* audio);
private:
enum AudioSource {
kRenderStream = 0,
kCaptureStream,
};
class TransformCallback : public LappedTransform::Callback {
public:
TransformCallback(IntelligibilityEnhancer* parent, AudioSource source);
virtual void ProcessAudioBlock(const std::complex<float>* const* in_block,
int in_channels, int frames,
int out_channels,
std::complex<float>* const* out_block);
private:
IntelligibilityEnhancer* parent_;
AudioSource source_;
};
friend class TransformCallback;
void DispatchAudio(AudioSource source, const std::complex<float>* in_block,
std::complex<float>* out_block);
void ProcessClearBlock(const std::complex<float>* in_block,
std::complex<float>* out_block);
void AnalyzeClearBlock(float power_target);
void ProcessNoiseBlock(const std::complex<float>* in_block,
std::complex<float>* out_block);
static int GetBankSize(int sample_rate, int erb_resolution);
void CreateErbBank();
void SolveEquation14(float lambda, int start_freq, float* sols);
void FilterVariance(const float* var, float* result);
static float DotProduct(const float* a, const float* b, int length);
static const int kErbResolution;
static const int kWindowSizeMs;
static const int kChunkSizeMs;
static const int kAnalyzeRate;
static const int kVarianceRate;
static const float kClipFreq;
static const float kConfigRho;
static const float kKbdAlpha;
static const float kGainChangeLimit;
const int freqs_;
const int window_size_; // window size in samples; also the block size
const int chunk_length_; // chunk size in samples
const int bank_size_;
const int sample_rate_hz_;
const int erb_resolution_;
const int channels_;
const int analysis_rate_;
const int variance_rate_;
intelligibility::VarianceArray clear_variance_;
intelligibility::VarianceArray noise_variance_;
scoped_ptr<float[]> filtered_clear_var_;
scoped_ptr<float[]> filtered_noise_var_;
float** filter_bank_;
scoped_ptr<float[]> center_freqs_;
int start_freq_;
scoped_ptr<float[]> rho_;
scoped_ptr<float[]> gains_eq_;
intelligibility::GainApplier gain_applier_;
// Destination buffer used to reassemble blocked chunks before overwriting
// the original input array with modifications.
float** temp_out_buffer_;
scoped_ptr<float*[]> input_audio_;
scoped_ptr<float[]> kbd_window_;
TransformCallback render_callback_;
TransformCallback capture_callback_;
scoped_ptr<LappedTransform> render_mangler_;
scoped_ptr<LappedTransform> capture_mangler_;
int block_count_;
int analysis_step_;
// TODO(bercic): Quick stopgap measure for voice detection in the clear
// and noise streams.
VadInst* vad_high_;
VadInst* vad_low_;
scoped_ptr<int16_t[]> vad_tmp_buffer_;
bool has_voice_low_;
};
} // namespace webrtc
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_

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/*
* Copyright (c) 2014 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 <arpa/inet.h>
#include <fcntl.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <sys/mman.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <unistd.h>
#include <fenv.h>
#include <limits>
#include <complex>
#include "gflags/gflags.h"
#include "webrtc/base/checks.h"
#include "webrtc/common_audio/real_fourier.h"
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
#include "webrtc/system_wrappers/interface/critical_section_wrapper.h"
#include "webrtc/system_wrappers/interface/scoped_ptr.h"
const int16_t* in_ipcm;
int16_t* out_ipcm;
const int16_t* noise_ipcm;
float* in_fpcm;
float* out_fpcm;
float* noise_fpcm;
float* noise_cursor;
float* clear_cursor;
int samples;
int fragment_size;
using std::complex;
using webrtc::RealFourier;
using webrtc::IntelligibilityEnhancer;
DEFINE_int32(clear_type, webrtc::intelligibility::VarianceArray::kStepInfinite,
"Variance algorithm for clear data.");
DEFINE_double(clear_alpha, 0.9,
"Variance decay factor for clear data.");
DEFINE_int32(clear_window, 475,
"Window size for windowed variance for clear data.");
DEFINE_int32(sample_rate, 16000,
"Audio sample rate used in the input and output files.");
DEFINE_int32(ana_rate, 800,
"Analysis rate; gains recalculated every N blocks.");
DEFINE_int32(var_rate, 2,
"Variance clear rate; history is forgotten every N gain recalculations.");
DEFINE_double(gain_limit, 1000.0, "Maximum gain change in one block.");
DEFINE_bool(repeat, false, "Repeat input file ad nauseam.");
DEFINE_string(clear_file, "speech.pcm", "Input file with clear speech.");
DEFINE_string(noise_file, "noise.pcm", "Input file with noise data.");
DEFINE_string(out_file, "proc_enhanced.pcm", "Enhanced output. Use '-' to "
"pipe through aplay internally.");
// Write an Sun AU-formatted audio chunk into file descriptor |fd|. Can be used
// to pipe the audio stream directly into aplay.
void writeau(int fd) {
uint32_t thing;
write(fd, ".snd", 4);
thing = htonl(24);
write(fd, &thing, sizeof(thing));
thing = htonl(0xffffffff);
write(fd, &thing, sizeof(thing));
thing = htonl(3);
write(fd, &thing, sizeof(thing));
thing = htonl(FLAGS_sample_rate);
write(fd, &thing, sizeof(thing));
thing = htonl(1);
write(fd, &thing, sizeof(thing));
for (int i = 0; i < samples; ++i) {
out_ipcm[i] = htons(out_ipcm[i]);
}
write(fd, out_ipcm, sizeof(*out_ipcm) * samples);
}
int main(int argc, char* argv[]) {
google::SetUsageMessage("\n\nVariance algorithm types are:\n"
" 0 - infinite/normal,\n"
" 1 - exponentially decaying,\n"
" 2 - rolling window.\n"
"\nInput files must be little-endian 16-bit signed raw PCM.\n");
google::ParseCommandLineFlags(&argc, &argv, true);
const char* in_name = FLAGS_clear_file.c_str();
const char* out_name = FLAGS_out_file.c_str();
const char* noise_name = FLAGS_noise_file.c_str();
struct stat in_stat, noise_stat;
int in_fd, out_fd, noise_fd;
FILE* aplay_file = nullptr;
fragment_size = FLAGS_sample_rate / 100;
stat(in_name, &in_stat);
stat(noise_name, &noise_stat);
samples = in_stat.st_size / sizeof(*in_ipcm);
in_fd = open(in_name, O_RDONLY);
if (!strcmp(out_name, "-")) {
aplay_file = popen("aplay -t au", "w");
out_fd = fileno(aplay_file);
} else {
out_fd = open(out_name, O_WRONLY | O_CREAT | O_TRUNC,
S_IRUSR | S_IWUSR | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH);
}
noise_fd = open(noise_name, O_RDONLY);
in_ipcm = static_cast<int16_t*>(mmap(nullptr, in_stat.st_size, PROT_READ,
MAP_PRIVATE, in_fd, 0));
noise_ipcm = static_cast<int16_t*>(mmap(nullptr, noise_stat.st_size,
PROT_READ, MAP_PRIVATE, noise_fd, 0));
out_ipcm = new int16_t[samples];
out_fpcm = new float[samples];
in_fpcm = new float[samples];
noise_fpcm = new float[samples];
for (int i = 0; i < samples; ++i) {
noise_fpcm[i] = noise_ipcm[i % (noise_stat.st_size / sizeof(*noise_ipcm))];
}
//feenableexcept(FE_INVALID | FE_OVERFLOW);
IntelligibilityEnhancer enh(2,
FLAGS_sample_rate, 1,
FLAGS_clear_type,
static_cast<float>(FLAGS_clear_alpha),
FLAGS_clear_window,
FLAGS_ana_rate,
FLAGS_var_rate,
FLAGS_gain_limit);
// Slice the input into smaller chunks, as the APM would do, and feed them
// into the enhancer. Repeat indefinitely if FLAGS_repeat is set.
do {
noise_cursor = noise_fpcm;
clear_cursor = in_fpcm;
for (int i = 0; i < samples; ++i) {
in_fpcm[i] = in_ipcm[i];
}
for (int i = 0; i < samples; i += fragment_size) {
enh.ProcessCaptureAudio(&noise_cursor);
enh.ProcessRenderAudio(&clear_cursor);
clear_cursor += fragment_size;
noise_cursor += fragment_size;
}
for (int i = 0; i < samples; ++i) {
out_ipcm[i] = static_cast<float>(in_fpcm[i]);
}
if (!strcmp(out_name, "-")) {
writeau(out_fd);
} else {
write(out_fd, out_ipcm, samples * sizeof(*out_ipcm));
}
} while (FLAGS_repeat);
munmap(const_cast<int16_t*>(noise_ipcm), noise_stat.st_size);
munmap(const_cast<int16_t*>(in_ipcm), in_stat.st_size);
close(noise_fd);
if (aplay_file) {
pclose(aplay_file);
} else {
close(out_fd);
}
close(in_fd);
return 0;
}

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/*
* Copyright (c) 2014 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 "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
#include <algorithm>
#include <cmath>
#include <cstring>
using std::complex;
namespace {
// Return |current| changed towards |target|, with the change being at most
// |limit|.
inline float UpdateFactor(float target, float current, float limit) {
float delta = fabsf(target - current);
float sign = copysign(1.0f, target - current);
return current + sign * fminf(delta, limit);
}
// std::isfinite for complex numbers.
inline bool cplxfinite(complex<float> c) {
return std::isfinite(c.real()) && std::isfinite(c.imag());
}
// std::isnormal for complex numbers.
inline bool cplxnormal(complex<float> c) {
return std::isnormal(c.real()) && std::isnormal(c.imag());
}
// Apply a small fudge to degenerate complex values. The numbers in the array
// were chosen randomly, so that even a series of all zeroes has some small
// variability.
inline complex<float> zerofudge(complex<float> c) {
const static complex<float> fudge[7] = {
{0.001f, 0.002f}, {0.008f, 0.001f}, {0.003f, 0.008f}, {0.0006f, 0.0009f},
{0.001f, 0.004f}, {0.003f, 0.004f}, {0.002f, 0.009f}
};
static int fudge_index = 0;
if (cplxfinite(c) && !cplxnormal(c)) {
fudge_index = (fudge_index + 1) % 7;
return c + fudge[fudge_index];
}
return c;
}
// Incremental mean computation. Return the mean of the series with the
// mean |mean| with added |data|.
inline complex<float> NewMean(complex<float> mean, complex<float> data,
int count) {
return mean + (data - mean) / static_cast<float>(count);
}
inline void AddToMean(complex<float> data, int count, complex<float>* mean) {
(*mean) = NewMean(*mean, data, count);
}
} // namespace
using std::min;
namespace webrtc {
namespace intelligibility {
static const int kWindowBlockSize = 10;
VarianceArray::VarianceArray(int freqs, StepType type, int window_size,
float decay)
: running_mean_(new complex<float>[freqs]()),
running_mean_sq_(new complex<float>[freqs]()),
sub_running_mean_(new complex<float>[freqs]()),
sub_running_mean_sq_(new complex<float>[freqs]()),
variance_(new float[freqs]()),
conj_sum_(new float[freqs]()),
freqs_(freqs),
window_size_(window_size),
decay_(decay),
history_cursor_(0),
count_(0),
array_mean_(0.0f) {
history_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
for (int i = 0; i < freqs_; ++i) {
history_[i].reset(new complex<float>[window_size_]());
}
subhistory_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
for (int i = 0; i < freqs_; ++i) {
subhistory_[i].reset(new complex<float>[window_size_]());
}
subhistory_sq_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
for (int i = 0; i < freqs_; ++i) {
subhistory_sq_[i].reset(new complex<float>[window_size_]());
}
switch (type) {
case kStepInfinite:
step_func_ = &VarianceArray::InfiniteStep;
break;
case kStepDecaying:
step_func_ = &VarianceArray::DecayStep;
break;
case kStepWindowed:
step_func_ = &VarianceArray::WindowedStep;
break;
case kStepBlocked:
step_func_ = &VarianceArray::BlockedStep;
break;
}
}
// Compute the variance with Welford's algorithm, adding some fudge to
// the input in case of all-zeroes.
void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
array_mean_ = 0.0f;
++count_;
for (int i = 0; i < freqs_; ++i) {
complex<float> sample = data[i];
if (!skip_fudge) {
sample = zerofudge(sample);
}
if (count_ == 1) {
running_mean_[i] = sample;
variance_[i] = 0.0f;
} else {
float old_sum = conj_sum_[i];
complex<float> old_mean = running_mean_[i];
running_mean_[i] = old_mean + (sample - old_mean) /
static_cast<float>(count_);
conj_sum_[i] = (old_sum + std::conj(sample - old_mean) *
(sample - running_mean_[i])).real();
variance_[i] = conj_sum_[i] / (count_ - 1); // + fudge[fudge_index].real();
if (skip_fudge && false) {
//variance_[i] -= fudge[fudge_index].real();
}
}
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
}
}
// Compute the variance from the beginning, with exponential decaying of the
// series data.
void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
array_mean_ = 0.0f;
++count_;
for (int i = 0; i < freqs_; ++i) {
complex<float> sample = data[i];
sample = zerofudge(sample);
if (count_ == 1) {
running_mean_[i] = sample;
running_mean_sq_[i] = sample * std::conj(sample);
variance_[i] = 0.0f;
} else {
complex<float> prev = running_mean_[i];
complex<float> prev2 = running_mean_sq_[i];
running_mean_[i] = decay_ * prev + (1.0f - decay_) * sample;
running_mean_sq_[i] = decay_ * prev2 +
(1.0f - decay_) * sample * std::conj(sample);
//variance_[i] = decay_ * variance_[i] + (1.0f - decay_) * (
// (sample - running_mean_[i]) * std::conj(sample - running_mean_[i])).real();
variance_[i] = (running_mean_sq_[i] - running_mean_[i] * std::conj(running_mean_[i])).real();
}
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
}
}
// Windowed variance computation. On each step, the variances for the
// window are recomputed from scratch, using Welford's algorithm.
void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
int num = min(count_ + 1, window_size_);
array_mean_ = 0.0f;
for (int i = 0; i < freqs_; ++i) {
complex<float> mean;
float conj_sum = 0.0f;
history_[i][history_cursor_] = data[i];
mean = history_[i][history_cursor_];
variance_[i] = 0.0f;
for (int j = 1; j < num; ++j) {
complex<float> sample = zerofudge(
history_[i][(history_cursor_ + j) % window_size_]);
sample = history_[i][(history_cursor_ + j) % window_size_];
float old_sum = conj_sum;
complex<float> old_mean = mean;
mean = old_mean + (sample - old_mean) / static_cast<float>(j + 1);
conj_sum = (old_sum + std::conj(sample - old_mean) *
(sample - mean)).real();
variance_[i] = conj_sum / (j);
}
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
}
history_cursor_ = (history_cursor_ + 1) % window_size_;
++count_;
}
// Variance with a window of blocks. Within each block, the variances are
// recomputed from scratch at every stp, using |Var(X) = E(X^2) - E^2(X)|.
// Once a block is filled with kWindowBlockSize samples, it is added to the
// history window and a new block is started. The variances for the window
// are recomputed from scratch at each of these transitions.
void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
int blocks = min(window_size_, history_cursor_);
for (int i = 0; i < freqs_; ++i) {
AddToMean(data[i], count_ + 1, &sub_running_mean_[i]);
AddToMean(data[i] * std::conj(data[i]), count_ + 1,
&sub_running_mean_sq_[i]);
subhistory_[i][history_cursor_ % window_size_] = sub_running_mean_[i];
subhistory_sq_[i][history_cursor_ % window_size_] = sub_running_mean_sq_[i];
variance_[i] = (NewMean(running_mean_sq_[i], sub_running_mean_sq_[i],
blocks) -
NewMean(running_mean_[i], sub_running_mean_[i], blocks) *
std::conj(NewMean(running_mean_[i], sub_running_mean_[i],
blocks))).real();
if (count_ == kWindowBlockSize - 1) {
sub_running_mean_[i] = complex<float>(0.0f, 0.0f);
sub_running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
running_mean_[i] = complex<float>(0.0f, 0.0f);
running_mean_sq_[i] = complex<float>(0.0f, 0.0f);
for (int j = 0; j < min(window_size_, history_cursor_); ++j) {
AddToMean(subhistory_[i][j], j, &running_mean_[i]);
AddToMean(subhistory_sq_[i][j], j, &running_mean_sq_[i]);
}
++history_cursor_;
}
}
++count_;
if (count_ == kWindowBlockSize) {
count_ = 0;
}
}
void VarianceArray::Clear() {
memset(running_mean_.get(), 0, sizeof(*running_mean_.get()) * freqs_);
memset(running_mean_sq_.get(), 0, sizeof(*running_mean_sq_.get()) * freqs_);
memset(variance_.get(), 0, sizeof(*variance_.get()) * freqs_);
memset(conj_sum_.get(), 0, sizeof(*conj_sum_.get()) * freqs_);
history_cursor_ = 0;
count_ = 0;
array_mean_ = 0.0f;
}
void VarianceArray::ApplyScale(float scale) {
array_mean_ = 0.0f;
for (int i = 0; i < freqs_; ++i) {
variance_[i] *= scale * scale;
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
}
}
GainApplier::GainApplier(int freqs, float change_limit)
: freqs_(freqs),
change_limit_(change_limit),
target_(new float[freqs]()),
current_(new float[freqs]()) {
for (int i = 0; i < freqs; ++i) {
target_[i] = 1.0f;
current_[i] = 1.0f;
}
}
void GainApplier::Apply(const complex<float>* in_block,
complex<float>* out_block) {
for (int i = 0; i < freqs_; ++i) {
float factor = sqrtf(fabsf(current_[i]));
if (!std::isnormal(factor)) {
factor = 1.0f;
}
out_block[i] = factor * in_block[i];
current_[i] = UpdateFactor(target_[i], current_[i], change_limit_);
}
}
} // namespace intelligibility
} // namespace webrtc

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/*
* Copyright (c) 2014 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.
*/
#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
#include <complex>
#include "webrtc/system_wrappers/interface/scoped_ptr.h"
namespace webrtc {
namespace intelligibility {
// Internal helper for computing the variances of a stream of arrays.
// The result is an array of variances per position: the i-th variance
// is the variance of the stream of data on the i-th positions in the
// input arrays.
// There are four methods of computation:
// * kStepInfinite computes variances from the beginning onwards
// * kStepDecaying uses a recursive exponential decay formula with a
// settable forgetting factor
// * kStepWindowed computes variances within a moving window
// * kStepBlocked is similar to kStepWindowed, but history is kept
// as a rolling window of blocks: multiple input elements are used for
// one block and the history then consists of the variances of these blocks
// with the same effect as kStepWindowed, but less storage, so the window
// can be longer
class VarianceArray {
public:
enum StepType {
kStepInfinite = 0,
kStepDecaying,
kStepWindowed,
kStepBlocked
};
// Construct an instance for the given input array length (|freqs|) and
// computation algorithm (|type|), with the appropriate parameters.
// |window_size| is the number of samples for kStepWindowed and
// the number of blocks for kStepBlocked. |decay| is the forgetting factor
// for kStepDecaying.
VarianceArray(int freqs, StepType type, int window_size, float decay);
// Add a new data point to the series and compute the new variances.
// TODO(bercic) |skip_fudge| is a flag for kStepWindowed and kStepDecaying,
// whether they should skip adding some small dummy values to the input
// to prevent problems with all-zero inputs. Can probably be removed.
void Step(const std::complex<float>* data, bool skip_fudge = false) {
(this->*step_func_)(data, skip_fudge);
}
// Reset variances to zero and forget all history.
void Clear();
// Scale the input data by |scale|. Effectively multiply variances
// by |scale^2|.
void ApplyScale(float scale);
// The current set of variances.
const float* variance() const {
return variance_.get();
}
// The mean value of the current set of variances.
float array_mean() const {
return array_mean_;
}
private:
void InfiniteStep(const std::complex<float>* data, bool dummy);
void DecayStep(const std::complex<float>* data, bool dummy);
void WindowedStep(const std::complex<float>* data, bool dummy);
void BlockedStep(const std::complex<float>* data, bool dummy);
// The current average X and X^2.
scoped_ptr<std::complex<float>[]> running_mean_;
scoped_ptr<std::complex<float>[]> running_mean_sq_;
// Average X and X^2 for the current block in kStepBlocked.
scoped_ptr<std::complex<float>[]> sub_running_mean_;
scoped_ptr<std::complex<float>[]> sub_running_mean_sq_;
// Sample history for the rolling window in kStepWindowed and block-wise
// histories for kStepBlocked.
scoped_ptr<scoped_ptr<std::complex<float>[]>[]> history_;
scoped_ptr<scoped_ptr<std::complex<float>[]>[]> subhistory_;
scoped_ptr<scoped_ptr<std::complex<float>[]>[]> subhistory_sq_;
// The current set of variances and sums for Welford's algorithm.
scoped_ptr<float[]> variance_;
scoped_ptr<float[]> conj_sum_;
const int freqs_;
const int window_size_;
const float decay_;
int history_cursor_;
int count_;
float array_mean_;
void (VarianceArray::*step_func_)(const std::complex<float>*, bool);
};
// Helper class for smoothing gain changes. On each applicatiion step, the
// currently used gains are changed towards a set of settable target gains,
// constrained by a limit on the magnitude of the changes.
class GainApplier {
public:
GainApplier(int freqs, float change_limit);
// Copy |in_block| to |out_block|, multiplied by the current set of gains,
// and step the current set of gains towards the target set.
void Apply(const std::complex<float>* in_block,
std::complex<float>* out_block);
// Return the current target gain set. Modify this array to set the targets.
float* target() const {
return target_.get();
}
private:
const int freqs_;
const float change_limit_;
scoped_ptr<float[]> target_;
scoped_ptr<float[]> current_;
};
} // namespace intelligibility
} // namespace webrtc
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_