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Allow intelligibility to compile in apm

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- Added files to gyp and BUILD
- Made minor fixes to get everything to compile
    and intelligibility_proc to run
- Added comments
- Auto-reformatting

Original cl is at: https://webrtc-codereview.appspot.com/57579004/

TBR=aluebs@webrtc.org

Review URL: https://codereview.webrtc.org/1182323005.

Cr-Commit-Position: refs/heads/master@{#9454}
This commit is contained in:
ekm 2015-06-16 18:57:32 -07:00
parent 01c9b012e9
commit b7553dfdbb
8 changed files with 337 additions and 206 deletions
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4
webrtc/modules/audio_processing/BUILD.gn
View File

@ -89,6 +89,10 @@ source_set("audio_processing") {
"high_pass_filter_impl.cc",
"high_pass_filter_impl.h",
"include/audio_processing.h",
"intelligibility/intelligibility_enhancer.cc",
"intelligibility/intelligibility_enhancer.h",
"intelligibility/intelligibility_utils.cc",
"intelligibility/intelligibility_utils.h",
"level_estimator_impl.cc",
"level_estimator_impl.h",
"noise_suppression_impl.cc",

4
webrtc/modules/audio_processing/audio_processing.gypi
View File

@ -99,6 +99,10 @@
'high_pass_filter_impl.cc',
'high_pass_filter_impl.h',
'include/audio_processing.h',
'intelligibility/intelligibility_enhancer.cc',
'intelligibility/intelligibility_enhancer.h',
'intelligibility/intelligibility_utils.cc',
'intelligibility/intelligibility_utils.h',
'level_estimator_impl.cc',
'level_estimator_impl.h',
'noise_suppression_impl.cc',

13
webrtc/modules/audio_processing/audio_processing_tests.gypi
View File

@ -59,6 +59,19 @@
'beamformer/nonlinear_beamformer_test.cc',
],
}, # nonlinear_beamformer_test
{
'target_name': 'intelligibility_proc',
'type': 'executable',
'dependencies': [
'audioproc_test_utils',
'<(DEPTH)/third_party/gflags/gflags.gyp:gflags',
'<(DEPTH)/testing/gtest.gyp:gtest',
'<(webrtc_root)/modules/modules.gyp:audio_processing',
],
'sources': [
'intelligibility/intelligibility_proc.cc',
],
}, # intelligibility_proc
],
'conditions': [
['enable_protobuf==1', {

180
webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.cc
View File

@ -8,6 +8,13 @@
* be found in the AUTHORS file in the root of the source tree.
*/
//
// Implements core class for intelligibility enhancer.
//
// Details of the model and algorithm can be found in the original paper:
// http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6882788
//
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
#include <cmath>
@ -27,13 +34,16 @@ 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::kChunkSizeMs = 10; // Size provided by APM.
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;
// To disable gain update smoothing, set gain limit to be VERY high.
// TODO(ekmeyerson): Add option to disable gain smoothing altogether
// to avoid the extra computation.
const float IntelligibilityEnhancer::kGainChangeLimit = 0.0125f;
using VarianceType = intelligibility::VarianceArray::StepType;
@ -41,12 +51,14 @@ using VarianceType = intelligibility::VarianceArray::StepType;
IntelligibilityEnhancer::TransformCallback::TransformCallback(
IntelligibilityEnhancer* parent,
IntelligibilityEnhancer::AudioSource source)
: parent_(parent),
source_(source) {}
: parent_(parent), source_(source) {
}
void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
const complex<float>* const* in_block,
int in_channels, int frames, int /* out_channels */,
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) {
@ -57,13 +69,14 @@ void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
int sample_rate_hz,
int channels,
int cv_type, float cv_alpha,
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))),
: 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)),
@ -72,7 +85,9 @@ IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
channels_(channels),
analysis_rate_(analysis_rate),
variance_rate_(variance_rate),
clear_variance_(freqs_, static_cast<VarianceType>(cv_type), cv_win,
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_]),
@ -83,58 +98,51 @@ IntelligibilityEnhancer::IntelligibilityEnhancer(int erb_resolution,
gains_eq_(new float[bank_size_]),
gain_applier_(freqs_, gain_limit),
temp_out_buffer_(nullptr),
input_audio_(new float*[channels]),
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_high_(WebRtcVad_Create()),
vad_low_(WebRtcVad_Create()),
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_set_mode(vad_high_, 0); // High likelihood of speech.
WebRtcVad_Init(vad_low_);
WebRtcVad_set_mode(vad_low_, 3); // low likelihood of speech
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_));
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;
temp_out_buffer_[i] =
reinterpret_cast<float*>(temp_out_buffer_ + channels_) +
chunk_length_ * i;
}
// Assumes all rho equal.
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));
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_));
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() {
@ -150,7 +158,9 @@ void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio) {
has_voice_low_ = WebRtcVad_Process(vad_low_, sample_rate_hz_,
vad_tmp_buffer_.get(), chunk_length_) == 1;
// Process and enhance chunk of |audio|
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_));
@ -161,21 +171,25 @@ 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
// TODO(bercic): The VAD was always detecting voice in the noise stream,
// no matter what the aggressiveness, so it was temporarily disabled here.
#if 0
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");
#endif
//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) {
const complex<float>* in_block,
complex<float>* out_block) {
switch (source) {
case kRenderStream:
ProcessClearBlock(in_block, out_block);
@ -196,6 +210,9 @@ void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
return;
}
// For now, always assumes enhancement is necessary.
// TODO(ekmeyerson): Change to only enhance if necessary,
// based on experiments with different cutoffs.
if (has_voice_low_ || true) {
clear_variance_.Step(in_block, false);
power_target = std::accumulate(clear_variance_.variance(),
@ -221,23 +238,25 @@ 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 */
// Bisection search for optimal |lambda|
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_);
SolveForGainsGivenLambda(lambda_top, start_freq_, gains_eq_.get());
power_top =
DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
SolveForGainsGivenLambda(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;
float power_ratio = 2.0f; // Ratio of achieved power to target power.
const float kConvergeThresh = 0.001f; // TODO(ekmeyerson): Find best values
const int kMaxIters = 100; // for these, based on experiments.
int iters = 0;
while (fabs(power_ratio - 1.0f) > 0.001f && iters <= 100) {
while (fabs(power_ratio - 1.0f) > kConvergeThresh && iters <= kMaxIters) {
lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
SolveEquation14(lambda, start_freq_, gains_eq_.get());
SolveForGainsGivenLambda(lambda, start_freq_, gains_eq_.get());
power = DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
if (power < power_target) {
lambda_bot = lambda;
@ -248,7 +267,7 @@ void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
++iters;
}
/* b = filterbank' * b */
// (ERB gain) = filterbank' * (freq gain)
float* gains = gain_applier_.target();
for (int i = 0; i < freqs_; ++i) {
gains[i] = 0.0f;
@ -265,8 +284,8 @@ void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* 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);
int erb_scale = ceilf(
11.17f * logf((freq_limit + 0.312f) / (freq_limit + 14.6575f)) + 43.0f);
return erb_scale * erb_resolution;
}
@ -283,29 +302,29 @@ void IntelligibilityEnhancer::CreateErbBank() {
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_));
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;
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_));
(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;
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_));
(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;
rr = min(freqs_, max(rr, 1)) - 1;
float step, element;
@ -338,8 +357,9 @@ void IntelligibilityEnhancer::CreateErbBank() {
}
}
void IntelligibilityEnhancer::SolveEquation14(float lambda, int start_freq,
float* sols) {
void IntelligibilityEnhancer::SolveForGainsGivenLambda(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();
@ -347,15 +367,17 @@ void IntelligibilityEnhancer::SolveEquation14(float lambda, int start_freq,
for (int n = 0; n < start_freq; ++n) {
sols[n] = 1.0f;
}
// Analytic solution for optimal gains. See paper for derivation.
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];
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);
sols[n] =
(-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0)) / (2 * alpha0);
} else {
sols[n] = -gamma0 / beta0;
}
@ -369,8 +391,9 @@ void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
}
}
float IntelligibilityEnhancer::DotProduct(const float* a, const float* b,
int length) {
float IntelligibilityEnhancer::DotProduct(const float* a,
const float* b,
int length) {
float ret = 0.0f;
for (int i = 0; i < length; ++i) {
@ -380,4 +403,3 @@ float IntelligibilityEnhancer::DotProduct(const float* a, const float* b,
}
} // namespace webrtc

109
webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h
View File

@ -8,14 +8,18 @@
* be found in the AUTHORS file in the root of the source tree.
*/
//
// Specifies core class for intelligbility enhancement.
//
#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
#include <complex>
#include "webrtc/base/scoped_ptr.h"
#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;
@ -25,6 +29,7 @@ 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.
// Note: assumes speech and noise streams are already separated.
class IntelligibilityEnhancer {
public:
// Construct a new instance with the given filter bank resolution,
@ -33,30 +38,43 @@ class IntelligibilityEnhancer {
// 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.
// clear 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,
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);
// Reads and processes chunk of noise stream in time domain.
void ProcessCaptureAudio(float* const* audio);
// Reads chunk of speech in time domain and updates with modified signal.
void ProcessRenderAudio(float* const* audio);
private:
enum AudioSource {
kRenderStream = 0,
kCaptureStream,
kRenderStream = 0, // Clear speech stream.
kCaptureStream, // Noise stream.
};
// Provides access point to the frequency domain.
class TransformCallback : public LappedTransform::Callback {
public:
TransformCallback(IntelligibilityEnhancer* parent, AudioSource source);
// All in frequency domain, receives input |in_block|, applies
// intelligibility enhancement, and writes result to |out_block|.
virtual void ProcessAudioBlock(const std::complex<float>* const* in_block,
int in_channels, int frames,
int in_channels,
int frames,
int out_channels,
std::complex<float>* const* out_block);
@ -66,72 +84,95 @@ class IntelligibilityEnhancer {
};
friend class TransformCallback;
void DispatchAudio(AudioSource source, const std::complex<float>* in_block,
// Sends streams to ProcessClearBlock or ProcessNoiseBlock based on source.
void DispatchAudio(AudioSource source,
const std::complex<float>* in_block,
std::complex<float>* out_block);
// Updates variance computation and analysis with |in_block_|,
// and writes modified speech to |out_block|.
void ProcessClearBlock(const std::complex<float>* in_block,
std::complex<float>* out_block);
// Computes and sets modified gains.
void AnalyzeClearBlock(float power_target);
// Updates variance calculation for noise input with |in_block|.
void ProcessNoiseBlock(const std::complex<float>* in_block,
std::complex<float>* out_block);
// Returns number of ERB filters.
static int GetBankSize(int sample_rate, int erb_resolution);
// Initializes ERB filterbank.
void CreateErbBank();
void SolveEquation14(float lambda, int start_freq, float* sols);
// Analytically solves quadratic for optimal gains given |lambda|.
// Negative gains are set to 0. Stores the results in |sols|.
void SolveForGainsGivenLambda(float lambda, int start_freq, float* sols);
// Computes variance across ERB filters from freq variance |var|.
// Stores in |result|.
void FilterVariance(const float* var, float* result);
// Returns dot product of vectors specified by size |length| arrays |a|,|b|.
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 int kAnalyzeRate; // Default for |analysis_rate_|.
static const int kVarianceRate; // Default for |variance_rate_|.
static const float kClipFreq;
static const float kConfigRho;
static const float kConfigRho; // Default production and interpretation SNR.
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 freqs_; // Num frequencies in frequency domain.
const int window_size_; // Window size in samples; also the block size.
const int chunk_length_; // Chunk size in samples.
const int bank_size_; // Num ERB filters.
const int sample_rate_hz_;
const int erb_resolution_;
const int channels_;
const int analysis_rate_;
const int variance_rate_;
const int channels_; // Num channels.
const int analysis_rate_; // Num blocks before gains recalculated.
const int variance_rate_; // Num recalculations before history is cleared.
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_;
rtc::scoped_ptr<float[]> filtered_clear_var_;
rtc::scoped_ptr<float[]> filtered_noise_var_;
float** filter_bank_; // TODO(ekmeyerson): Switch to using ChannelBuffer.
rtc::scoped_ptr<float[]> center_freqs_;
int start_freq_;
scoped_ptr<float[]> rho_;
scoped_ptr<float[]> gains_eq_;
rtc::scoped_ptr<float[]> rho_; // Production and interpretation SNR.
// for each ERB band.
rtc::scoped_ptr<float[]> gains_eq_; // Pre-filter modified gains.
intelligibility::GainApplier gain_applier_;
// Destination buffer used to reassemble blocked chunks before overwriting
// the original input array with modifications.
// TODO(ekmeyerson): Switch to using ChannelBuffer.
float** temp_out_buffer_;
scoped_ptr<float*[]> input_audio_;
scoped_ptr<float[]> kbd_window_;
rtc::scoped_ptr<float* []> input_audio_;
rtc::scoped_ptr<float[]> kbd_window_;
TransformCallback render_callback_;
TransformCallback capture_callback_;
scoped_ptr<LappedTransform> render_mangler_;
scoped_ptr<LappedTransform> capture_mangler_;
rtc::scoped_ptr<LappedTransform> render_mangler_;
rtc::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.
// Note: VAD currently does not affect anything in IntelligibilityEnhancer.
VadInst* vad_high_;
VadInst* vad_low_;
scoped_ptr<int16_t[]> vad_tmp_buffer_;
bool has_voice_low_;
rtc::scoped_ptr<int16_t[]> vad_tmp_buffer_;
bool has_voice_low_; // Whether voice detected in speech stream.
};
} // namespace webrtc
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_

114
webrtc/modules/audio_processing/intelligibility/intelligibility_proc.cc
View File

@ -8,6 +8,12 @@
* be found in the AUTHORS file in the root of the source tree.
*/
//
// Command line tool for speech intelligibility enhancement. Provides for
// running and testing intelligibility_enhancer as an independent process.
// Use --help for options.
//
#include <arpa/inet.h>
#include <fcntl.h>
#include <stdint.h>
@ -24,53 +30,71 @@
#include <complex>
#include "gflags/gflags.h"
#include "testing/gtest/include/gtest/gtest.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"
// PCM data simulating streams
const int16_t* in_ipcm;
int16_t* out_ipcm;
const int16_t* noise_ipcm;
float* in_fpcm;
float* out_fpcm;
float* noise_fpcm;
// Current locations in streams
float* noise_cursor;
float* clear_cursor;
int samples;
int fragment_size;
int samples; // Number of samples in input PCM file
int fragment_size; // Number of samples to process at a time
// to simulate APM stream processing
using std::complex;
namespace webrtc {
using webrtc::RealFourier;
using webrtc::IntelligibilityEnhancer;
DEFINE_int32(clear_type, webrtc::intelligibility::VarianceArray::kStepInfinite,
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,
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,
DEFINE_int32(sample_rate,
16000,
"Audio sample rate used in the input and output files.");
DEFINE_int32(ana_rate, 800,
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_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 "
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.
// Constant IntelligibilityEnhancer constructor parameters.
const int kErbResolution = 2;
const int kNumChannels = 1;
// Converts output stream to Sun AU format and writes to file descriptor |fd|.
// Can be used to pipe output directly into aplay.
// TODO(ekmeyerson): Modify to write WAV instead.
void writeau(int fd) {
uint32_t thing;
@ -92,12 +116,14 @@ void writeau(int fd) {
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");
// void function for gtest
void void_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();
@ -107,10 +133,15 @@ int main(int argc, char* argv[]) {
int in_fd, out_fd, noise_fd;
FILE* aplay_file = nullptr;
fragment_size = FLAGS_sample_rate / 100;
// Load settings and set up PCMs.
fragment_size = FLAGS_sample_rate / 100; // Mirror real time APM chunk size.
// Duplicates chunk_length_ in
// IntelligibilityEnhancer.
ASSERT_EQ(stat(in_name, &in_stat), 0) << "Empty speech input.";
ASSERT_EQ(stat(noise_name, &noise_stat), 0) << "Empty noise input.";
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);
@ -123,10 +154,10 @@ int main(int argc, char* argv[]) {
}
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));
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];
@ -136,18 +167,17 @@ int main(int argc, char* argv[]) {
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);
// Run intelligibility enhancement.
IntelligibilityEnhancer enh(
kErbResolution,
FLAGS_sample_rate,
kNumChannels,
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.
// through the enhancer. Repeat indefinitely if FLAGS_repeat is set.
do {
noise_cursor = noise_fpcm;
clear_cursor = in_fpcm;
@ -181,7 +211,11 @@ int main(int argc, char* argv[]) {
close(out_fd);
}
close(in_fd);
return 0;
}
} // namespace webrtc
int main(int argc, char* argv[]) {
webrtc::void_main(argc, argv);
return 0;
}

75
webrtc/modules/audio_processing/intelligibility/intelligibility_utils.cc
View File

@ -8,6 +8,10 @@
* be found in the AUTHORS file in the root of the source tree.
*/
//
// Implements helper functions and classes for intelligibility enhancement.
//
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
#include <algorithm>
@ -40,10 +44,13 @@ inline bool cplxnormal(complex<float> c) {
// 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}
};
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;
@ -54,8 +61,9 @@ inline complex<float> zerofudge(complex<float> 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) {
inline complex<float> NewMean(complex<float> mean,
complex<float> data,
int count) {
return mean + (data - mean) / static_cast<float>(count);
}
@ -73,7 +81,9 @@ namespace intelligibility {
static const int kWindowBlockSize = 10;
VarianceArray::VarianceArray(int freqs, StepType type, int window_size,
VarianceArray::VarianceArray(int freqs,
StepType type,
int window_size,
float decay)
: running_mean_(new complex<float>[freqs]()),
running_mean_sq_(new complex<float>[freqs]()),
@ -87,15 +97,15 @@ VarianceArray::VarianceArray(int freqs, StepType type, int window_size,
history_cursor_(0),
count_(0),
array_mean_(0.0f) {
history_.reset(new scoped_ptr<complex<float>[]>[freqs_]());
history_.reset(new rtc::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_]());
subhistory_.reset(new rtc::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_]());
subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[freqs_]());
for (int i = 0; i < freqs_; ++i) {
subhistory_sq_[i].reset(new complex<float>[window_size_]());
}
@ -131,13 +141,15 @@ void VarianceArray::InfiniteStep(const complex<float>* data, bool skip_fudge) {
} 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();
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();
// variance_[i] -= fudge[fudge_index].real();
}
}
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
@ -161,11 +173,13 @@ void VarianceArray::DecayStep(const complex<float>* data, bool /*dummy*/) {
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();
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);
@ -186,15 +200,15 @@ void VarianceArray::WindowedStep(const complex<float>* data, bool /*dummy*/) {
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_]);
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();
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);
@ -217,11 +231,11 @@ void VarianceArray::BlockedStep(const complex<float>* data, bool /*dummy*/) {
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();
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);
@ -284,4 +298,3 @@ void GainApplier::Apply(const complex<float>* in_block,
} // namespace intelligibility
} // namespace webrtc

44
webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h
View File

@ -8,12 +8,16 @@
* be found in the AUTHORS file in the root of the source tree.
*/
//
// Specifies helper classes for intelligibility enhancement.
//
#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"
#include "webrtc/base/scoped_ptr.h"
namespace webrtc {
@ -63,14 +67,10 @@ class VarianceArray {
void ApplyScale(float scale);
// The current set of variances.
const float* variance() const {
return variance_.get();
}
const float* variance() const { return variance_.get(); }
// The mean value of the current set of variances.
float array_mean() const {
return array_mean_;
}
float array_mean() const { return array_mean_; }
private:
void InfiniteStep(const std::complex<float>* data, bool dummy);
@ -78,23 +78,26 @@ class VarianceArray {
void WindowedStep(const std::complex<float>* data, bool dummy);
void BlockedStep(const std::complex<float>* data, bool dummy);
// TODO(ekmeyerson): Switch the following running means
// and histories from rtc::scoped_ptr to std::vector.
// The current average X and X^2.
scoped_ptr<std::complex<float>[]> running_mean_;
scoped_ptr<std::complex<float>[]> running_mean_sq_;
rtc::scoped_ptr<std::complex<float>[]> running_mean_;
rtc::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_;
rtc::scoped_ptr<std::complex<float>[]> sub_running_mean_;
rtc::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_;
rtc::scoped_ptr<rtc::scoped_ptr<std::complex<float>[]>[]> history_;
rtc::scoped_ptr<rtc::scoped_ptr<std::complex<float>[]>[]> subhistory_;
rtc::scoped_ptr<rtc::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_;
rtc::scoped_ptr<float[]> variance_;
rtc::scoped_ptr<float[]> conj_sum_;
const int freqs_;
const int window_size_;
@ -118,15 +121,13 @@ class GainApplier {
std::complex<float>* out_block);
// Return the current target gain set. Modify this array to set the targets.
float* target() const {
return target_.get();
}
float* target() const { return target_.get(); }
private:
const int freqs_;
const float change_limit_;
scoped_ptr<float[]> target_;
scoped_ptr<float[]> current_;
rtc::scoped_ptr<float[]> target_;
rtc::scoped_ptr<float[]> current_;
};
} // namespace intelligibility
@ -134,4 +135,3 @@ class GainApplier {
} // namespace webrtc
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_