Update audio_processing module
Corresponds to upstream commit 524e9b043e7e86fd72353b987c9d5f6a1ebf83e1 Update notes: * Pull in third party license file * Replace .gypi files with BUILD.gn to keep track of what changes upstream * Bunch of new filse pulled in as dependencies * Won't build yet due to changes needed on top of these
This commit is contained in:
Arun Raghavan
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/*
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* Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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//
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// Implements core class for intelligibility enhancer.
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//
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// Details of the model and algorithm can be found in the original paper:
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// http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6882788
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//
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#include "webrtc/modules/audio_processing/intelligibility/intelligibility_enhancer.h"
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#include <math.h>
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#include <stdlib.h>
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#include <algorithm>
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#include <numeric>
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#include "webrtc/base/checks.h"
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#include "webrtc/common_audio/include/audio_util.h"
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#include "webrtc/common_audio/window_generator.h"
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namespace webrtc {
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namespace {
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const size_t kErbResolution = 2;
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const int kWindowSizeMs = 2;
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const int kChunkSizeMs = 10; // Size provided by APM.
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const float kClipFreq = 200.0f;
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const float kConfigRho = 0.02f; // Default production and interpretation SNR.
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const float kKbdAlpha = 1.5f;
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const float kLambdaBot = -1.0f; // Extreme values in bisection
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const float kLambdaTop = -10e-18f; // search for lamda.
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} // namespace
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using std::complex;
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using std::max;
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using std::min;
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using VarianceType = intelligibility::VarianceArray::StepType;
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IntelligibilityEnhancer::TransformCallback::TransformCallback(
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IntelligibilityEnhancer* parent,
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IntelligibilityEnhancer::AudioSource source)
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: parent_(parent), source_(source) {
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}
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void IntelligibilityEnhancer::TransformCallback::ProcessAudioBlock(
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const complex<float>* const* in_block,
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int in_channels,
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size_t frames,
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int /* out_channels */,
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complex<float>* const* out_block) {
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RTC_DCHECK_EQ(parent_->freqs_, frames);
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for (int i = 0; i < in_channels; ++i) {
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parent_->DispatchAudio(source_, in_block[i], out_block[i]);
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}
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}
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IntelligibilityEnhancer::IntelligibilityEnhancer()
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: IntelligibilityEnhancer(IntelligibilityEnhancer::Config()) {
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}
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IntelligibilityEnhancer::IntelligibilityEnhancer(const Config& config)
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: freqs_(RealFourier::ComplexLength(
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RealFourier::FftOrder(config.sample_rate_hz * kWindowSizeMs / 1000))),
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window_size_(static_cast<size_t>(1 << RealFourier::FftOrder(freqs_))),
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chunk_length_(
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static_cast<size_t>(config.sample_rate_hz * kChunkSizeMs / 1000)),
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bank_size_(GetBankSize(config.sample_rate_hz, kErbResolution)),
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sample_rate_hz_(config.sample_rate_hz),
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erb_resolution_(kErbResolution),
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num_capture_channels_(config.num_capture_channels),
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num_render_channels_(config.num_render_channels),
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analysis_rate_(config.analysis_rate),
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active_(true),
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clear_variance_(freqs_,
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config.var_type,
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config.var_window_size,
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config.var_decay_rate),
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noise_variance_(freqs_,
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config.var_type,
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config.var_window_size,
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config.var_decay_rate),
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filtered_clear_var_(new float[bank_size_]),
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filtered_noise_var_(new float[bank_size_]),
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filter_bank_(bank_size_),
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center_freqs_(new float[bank_size_]),
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rho_(new float[bank_size_]),
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gains_eq_(new float[bank_size_]),
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gain_applier_(freqs_, config.gain_change_limit),
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temp_render_out_buffer_(chunk_length_, num_render_channels_),
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temp_capture_out_buffer_(chunk_length_, num_capture_channels_),
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kbd_window_(new float[window_size_]),
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render_callback_(this, AudioSource::kRenderStream),
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capture_callback_(this, AudioSource::kCaptureStream),
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block_count_(0),
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analysis_step_(0) {
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RTC_DCHECK_LE(config.rho, 1.0f);
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CreateErbBank();
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// Assumes all rho equal.
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for (size_t i = 0; i < bank_size_; ++i) {
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rho_[i] = config.rho * config.rho;
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}
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float freqs_khz = kClipFreq / 1000.0f;
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size_t erb_index = static_cast<size_t>(ceilf(
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11.17f * logf((freqs_khz + 0.312f) / (freqs_khz + 14.6575f)) + 43.0f));
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start_freq_ = std::max(static_cast<size_t>(1), erb_index * erb_resolution_);
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WindowGenerator::KaiserBesselDerived(kKbdAlpha, window_size_,
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kbd_window_.get());
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render_mangler_.reset(new LappedTransform(
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num_render_channels_, num_render_channels_, chunk_length_,
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kbd_window_.get(), window_size_, window_size_ / 2, &render_callback_));
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capture_mangler_.reset(new LappedTransform(
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num_capture_channels_, num_capture_channels_, chunk_length_,
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kbd_window_.get(), window_size_, window_size_ / 2, &capture_callback_));
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}
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void IntelligibilityEnhancer::ProcessRenderAudio(float* const* audio,
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int sample_rate_hz,
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int num_channels) {
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RTC_CHECK_EQ(sample_rate_hz_, sample_rate_hz);
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RTC_CHECK_EQ(num_render_channels_, num_channels);
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if (active_) {
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render_mangler_->ProcessChunk(audio, temp_render_out_buffer_.channels());
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}
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if (active_) {
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for (int i = 0; i < num_render_channels_; ++i) {
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memcpy(audio[i], temp_render_out_buffer_.channels()[i],
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chunk_length_ * sizeof(**audio));
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}
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}
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}
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void IntelligibilityEnhancer::AnalyzeCaptureAudio(float* const* audio,
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int sample_rate_hz,
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int num_channels) {
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RTC_CHECK_EQ(sample_rate_hz_, sample_rate_hz);
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RTC_CHECK_EQ(num_capture_channels_, num_channels);
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capture_mangler_->ProcessChunk(audio, temp_capture_out_buffer_.channels());
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}
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void IntelligibilityEnhancer::DispatchAudio(
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IntelligibilityEnhancer::AudioSource source,
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const complex<float>* in_block,
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complex<float>* out_block) {
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switch (source) {
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case kRenderStream:
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ProcessClearBlock(in_block, out_block);
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break;
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case kCaptureStream:
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ProcessNoiseBlock(in_block, out_block);
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break;
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}
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}
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void IntelligibilityEnhancer::ProcessClearBlock(const complex<float>* in_block,
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complex<float>* out_block) {
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if (block_count_ < 2) {
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memset(out_block, 0, freqs_ * sizeof(*out_block));
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++block_count_;
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return;
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}
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// TODO(ekm): Use VAD to |Step| and |AnalyzeClearBlock| only if necessary.
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if (true) {
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clear_variance_.Step(in_block, false);
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if (block_count_ % analysis_rate_ == analysis_rate_ - 1) {
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const float power_target = std::accumulate(
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clear_variance_.variance(), clear_variance_.variance() + freqs_, 0.f);
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AnalyzeClearBlock(power_target);
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++analysis_step_;
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}
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++block_count_;
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}
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if (active_) {
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gain_applier_.Apply(in_block, out_block);
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}
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}
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void IntelligibilityEnhancer::AnalyzeClearBlock(float power_target) {
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FilterVariance(clear_variance_.variance(), filtered_clear_var_.get());
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FilterVariance(noise_variance_.variance(), filtered_noise_var_.get());
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SolveForGainsGivenLambda(kLambdaTop, start_freq_, gains_eq_.get());
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const float power_top =
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DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
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SolveForGainsGivenLambda(kLambdaBot, start_freq_, gains_eq_.get());
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const float power_bot =
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DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
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if (power_target >= power_bot && power_target <= power_top) {
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SolveForLambda(power_target, power_bot, power_top);
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UpdateErbGains();
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} // Else experiencing variance underflow, so do nothing.
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}
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void IntelligibilityEnhancer::SolveForLambda(float power_target,
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float power_bot,
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float power_top) {
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const float kConvergeThresh = 0.001f; // TODO(ekmeyerson): Find best values
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const int kMaxIters = 100; // for these, based on experiments.
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const float reciprocal_power_target = 1.f / power_target;
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float lambda_bot = kLambdaBot;
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float lambda_top = kLambdaTop;
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float power_ratio = 2.0f; // Ratio of achieved power to target power.
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int iters = 0;
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while (std::fabs(power_ratio - 1.0f) > kConvergeThresh &&
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iters <= kMaxIters) {
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const float lambda = lambda_bot + (lambda_top - lambda_bot) / 2.0f;
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SolveForGainsGivenLambda(lambda, start_freq_, gains_eq_.get());
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const float power =
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DotProduct(gains_eq_.get(), filtered_clear_var_.get(), bank_size_);
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if (power < power_target) {
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lambda_bot = lambda;
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} else {
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lambda_top = lambda;
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}
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power_ratio = std::fabs(power * reciprocal_power_target);
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++iters;
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}
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}
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void IntelligibilityEnhancer::UpdateErbGains() {
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// (ERB gain) = filterbank' * (freq gain)
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float* gains = gain_applier_.target();
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for (size_t i = 0; i < freqs_; ++i) {
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gains[i] = 0.0f;
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for (size_t j = 0; j < bank_size_; ++j) {
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gains[i] = fmaf(filter_bank_[j][i], gains_eq_[j], gains[i]);
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}
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}
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}
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void IntelligibilityEnhancer::ProcessNoiseBlock(const complex<float>* in_block,
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complex<float>* /*out_block*/) {
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noise_variance_.Step(in_block);
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}
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size_t IntelligibilityEnhancer::GetBankSize(int sample_rate,
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size_t erb_resolution) {
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float freq_limit = sample_rate / 2000.0f;
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size_t erb_scale = static_cast<size_t>(ceilf(
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11.17f * logf((freq_limit + 0.312f) / (freq_limit + 14.6575f)) + 43.0f));
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return erb_scale * erb_resolution;
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}
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void IntelligibilityEnhancer::CreateErbBank() {
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size_t lf = 1, rf = 4;
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for (size_t i = 0; i < bank_size_; ++i) {
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float abs_temp = fabsf((i + 1.0f) / static_cast<float>(erb_resolution_));
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center_freqs_[i] = 676170.4f / (47.06538f - expf(0.08950404f * abs_temp));
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center_freqs_[i] -= 14678.49f;
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}
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float last_center_freq = center_freqs_[bank_size_ - 1];
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for (size_t i = 0; i < bank_size_; ++i) {
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center_freqs_[i] *= 0.5f * sample_rate_hz_ / last_center_freq;
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}
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for (size_t i = 0; i < bank_size_; ++i) {
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filter_bank_[i].resize(freqs_);
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}
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for (size_t i = 1; i <= bank_size_; ++i) {
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size_t lll, ll, rr, rrr;
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static const size_t kOne = 1; // Avoids repeated static_cast<>s below.
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lll = static_cast<size_t>(round(
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center_freqs_[max(kOne, i - lf) - 1] * freqs_ /
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(0.5f * sample_rate_hz_)));
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ll = static_cast<size_t>(round(
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center_freqs_[max(kOne, i) - 1] * freqs_ / (0.5f * sample_rate_hz_)));
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lll = min(freqs_, max(lll, kOne)) - 1;
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ll = min(freqs_, max(ll, kOne)) - 1;
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rrr = static_cast<size_t>(round(
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center_freqs_[min(bank_size_, i + rf) - 1] * freqs_ /
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(0.5f * sample_rate_hz_)));
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rr = static_cast<size_t>(round(
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center_freqs_[min(bank_size_, i + 1) - 1] * freqs_ /
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(0.5f * sample_rate_hz_)));
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rrr = min(freqs_, max(rrr, kOne)) - 1;
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rr = min(freqs_, max(rr, kOne)) - 1;
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float step, element;
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step = 1.0f / (ll - lll);
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element = 0.0f;
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for (size_t j = lll; j <= ll; ++j) {
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filter_bank_[i - 1][j] = element;
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element += step;
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}
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step = 1.0f / (rrr - rr);
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element = 1.0f;
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for (size_t j = rr; j <= rrr; ++j) {
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filter_bank_[i - 1][j] = element;
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element -= step;
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}
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for (size_t j = ll; j <= rr; ++j) {
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filter_bank_[i - 1][j] = 1.0f;
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}
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}
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float sum;
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for (size_t i = 0; i < freqs_; ++i) {
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sum = 0.0f;
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for (size_t j = 0; j < bank_size_; ++j) {
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sum += filter_bank_[j][i];
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}
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for (size_t j = 0; j < bank_size_; ++j) {
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filter_bank_[j][i] /= sum;
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}
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}
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}
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void IntelligibilityEnhancer::SolveForGainsGivenLambda(float lambda,
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size_t start_freq,
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float* sols) {
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bool quadratic = (kConfigRho < 1.0f);
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const float* var_x0 = filtered_clear_var_.get();
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const float* var_n0 = filtered_noise_var_.get();
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for (size_t n = 0; n < start_freq; ++n) {
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sols[n] = 1.0f;
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}
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// Analytic solution for optimal gains. See paper for derivation.
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for (size_t n = start_freq - 1; n < bank_size_; ++n) {
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float alpha0, beta0, gamma0;
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gamma0 = 0.5f * rho_[n] * var_x0[n] * var_n0[n] +
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lambda * var_x0[n] * var_n0[n] * var_n0[n];
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beta0 = lambda * var_x0[n] * (2 - rho_[n]) * var_x0[n] * var_n0[n];
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if (quadratic) {
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alpha0 = lambda * var_x0[n] * (1 - rho_[n]) * var_x0[n] * var_x0[n];
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sols[n] =
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(-beta0 - sqrtf(beta0 * beta0 - 4 * alpha0 * gamma0)) / (2 * alpha0);
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} else {
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sols[n] = -gamma0 / beta0;
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}
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sols[n] = fmax(0, sols[n]);
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}
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}
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void IntelligibilityEnhancer::FilterVariance(const float* var, float* result) {
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RTC_DCHECK_GT(freqs_, 0u);
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for (size_t i = 0; i < bank_size_; ++i) {
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result[i] = DotProduct(&filter_bank_[i][0], var, freqs_);
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}
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}
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float IntelligibilityEnhancer::DotProduct(const float* a,
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const float* b,
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size_t length) {
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float ret = 0.0f;
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for (size_t i = 0; i < length; ++i) {
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ret = fmaf(a[i], b[i], ret);
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}
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return ret;
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}
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bool IntelligibilityEnhancer::active() const {
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return active_;
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}
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} // namespace webrtc
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@ -0,0 +1,182 @@
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/*
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* Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
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||||
*
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||||
* Use of this source code is governed by a BSD-style license
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||||
* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
|
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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//
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// Specifies core class for intelligbility enhancement.
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//
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#ifndef WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
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#define WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
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#include <complex>
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#include <vector>
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#include "webrtc/base/scoped_ptr.h"
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#include "webrtc/common_audio/lapped_transform.h"
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#include "webrtc/common_audio/channel_buffer.h"
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#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
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namespace webrtc {
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// Speech intelligibility enhancement module. Reads render and capture
|
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// audio streams and modifies the render stream with a set of gains per
|
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// frequency bin to enhance speech against the noise background.
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// Note: assumes speech and noise streams are already separated.
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class IntelligibilityEnhancer {
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public:
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struct Config {
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// |var_*| are parameters for the VarianceArray constructor for the
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// clear speech stream.
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// TODO(bercic): the |var_*|, |*_rate| and |gain_limit| parameters should
|
||||
// probably go away once fine tuning is done.
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Config()
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||||
: sample_rate_hz(16000),
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||||
num_capture_channels(1),
|
||||
num_render_channels(1),
|
||||
var_type(intelligibility::VarianceArray::kStepDecaying),
|
||||
var_decay_rate(0.9f),
|
||||
var_window_size(10),
|
||||
analysis_rate(800),
|
||||
gain_change_limit(0.1f),
|
||||
rho(0.02f) {}
|
||||
int sample_rate_hz;
|
||||
int num_capture_channels;
|
||||
int num_render_channels;
|
||||
intelligibility::VarianceArray::StepType var_type;
|
||||
float var_decay_rate;
|
||||
size_t var_window_size;
|
||||
int analysis_rate;
|
||||
float gain_change_limit;
|
||||
float rho;
|
||||
};
|
||||
|
||||
explicit IntelligibilityEnhancer(const Config& config);
|
||||
IntelligibilityEnhancer(); // Initialize with default config.
|
||||
|
||||
// Reads and processes chunk of noise stream in time domain.
|
||||
void AnalyzeCaptureAudio(float* const* audio,
|
||||
int sample_rate_hz,
|
||||
int num_channels);
|
||||
|
||||
// Reads chunk of speech in time domain and updates with modified signal.
|
||||
void ProcessRenderAudio(float* const* audio,
|
||||
int sample_rate_hz,
|
||||
int num_channels);
|
||||
bool active() const;
|
||||
|
||||
private:
|
||||
enum AudioSource {
|
||||
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|.
|
||||
void ProcessAudioBlock(const std::complex<float>* const* in_block,
|
||||
int in_channels,
|
||||
size_t frames,
|
||||
int out_channels,
|
||||
std::complex<float>* const* out_block) override;
|
||||
|
||||
private:
|
||||
IntelligibilityEnhancer* parent_;
|
||||
AudioSource source_;
|
||||
};
|
||||
friend class TransformCallback;
|
||||
FRIEND_TEST_ALL_PREFIXES(IntelligibilityEnhancerTest, TestErbCreation);
|
||||
FRIEND_TEST_ALL_PREFIXES(IntelligibilityEnhancerTest, TestSolveForGains);
|
||||
|
||||
// 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);
|
||||
|
||||
// Bisection search for optimal |lambda|.
|
||||
void SolveForLambda(float power_target, float power_bot, float power_top);
|
||||
|
||||
// Transforms freq gains to ERB gains.
|
||||
void UpdateErbGains();
|
||||
|
||||
// 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 size_t GetBankSize(int sample_rate, size_t erb_resolution);
|
||||
|
||||
// Initializes ERB filterbank.
|
||||
void CreateErbBank();
|
||||
|
||||
// Analytically solves quadratic for optimal gains given |lambda|.
|
||||
// Negative gains are set to 0. Stores the results in |sols|.
|
||||
void SolveForGainsGivenLambda(float lambda, size_t 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, size_t length);
|
||||
|
||||
const size_t freqs_; // Num frequencies in frequency domain.
|
||||
const size_t window_size_; // Window size in samples; also the block size.
|
||||
const size_t chunk_length_; // Chunk size in samples.
|
||||
const size_t bank_size_; // Num ERB filters.
|
||||
const int sample_rate_hz_;
|
||||
const int erb_resolution_;
|
||||
const int num_capture_channels_;
|
||||
const int num_render_channels_;
|
||||
const int analysis_rate_; // Num blocks before gains recalculated.
|
||||
|
||||
const bool active_; // Whether render gains are being updated.
|
||||
// TODO(ekm): Add logic for updating |active_|.
|
||||
|
||||
intelligibility::VarianceArray clear_variance_;
|
||||
intelligibility::VarianceArray noise_variance_;
|
||||
rtc::scoped_ptr<float[]> filtered_clear_var_;
|
||||
rtc::scoped_ptr<float[]> filtered_noise_var_;
|
||||
std::vector<std::vector<float>> filter_bank_;
|
||||
rtc::scoped_ptr<float[]> center_freqs_;
|
||||
size_t start_freq_;
|
||||
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 buffers used to reassemble blocked chunks before overwriting
|
||||
// the original input array with modifications.
|
||||
ChannelBuffer<float> temp_render_out_buffer_;
|
||||
ChannelBuffer<float> temp_capture_out_buffer_;
|
||||
|
||||
rtc::scoped_ptr<float[]> kbd_window_;
|
||||
TransformCallback render_callback_;
|
||||
TransformCallback capture_callback_;
|
||||
rtc::scoped_ptr<LappedTransform> render_mangler_;
|
||||
rtc::scoped_ptr<LappedTransform> capture_mangler_;
|
||||
int block_count_;
|
||||
int analysis_step_;
|
||||
};
|
||||
|
||||
} // namespace webrtc
|
||||
|
||||
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_ENHANCER_H_
|
||||
@ -0,0 +1,314 @@
|
||||
/*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
//
|
||||
// Implements helper functions and classes for intelligibility enhancement.
|
||||
//
|
||||
|
||||
#include "webrtc/modules/audio_processing/intelligibility/intelligibility_utils.h"
|
||||
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
#include <algorithm>
|
||||
|
||||
using std::complex;
|
||||
using std::min;
|
||||
|
||||
namespace webrtc {
|
||||
|
||||
namespace intelligibility {
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
float AddDitherIfZero(float value) {
|
||||
return value == 0.f ? std::rand() * 0.01f / RAND_MAX : value;
|
||||
}
|
||||
|
||||
complex<float> zerofudge(complex<float> c) {
|
||||
return complex<float>(AddDitherIfZero(c.real()), AddDitherIfZero(c.imag()));
|
||||
}
|
||||
|
||||
complex<float> NewMean(complex<float> mean, complex<float> data, size_t count) {
|
||||
return mean + (data - mean) / static_cast<float>(count);
|
||||
}
|
||||
|
||||
void AddToMean(complex<float> data, size_t count, complex<float>* mean) {
|
||||
(*mean) = NewMean(*mean, data, count);
|
||||
}
|
||||
|
||||
|
||||
static const size_t kWindowBlockSize = 10;
|
||||
|
||||
VarianceArray::VarianceArray(size_t num_freqs,
|
||||
StepType type,
|
||||
size_t window_size,
|
||||
float decay)
|
||||
: running_mean_(new complex<float>[num_freqs]()),
|
||||
running_mean_sq_(new complex<float>[num_freqs]()),
|
||||
sub_running_mean_(new complex<float>[num_freqs]()),
|
||||
sub_running_mean_sq_(new complex<float>[num_freqs]()),
|
||||
variance_(new float[num_freqs]()),
|
||||
conj_sum_(new float[num_freqs]()),
|
||||
num_freqs_(num_freqs),
|
||||
window_size_(window_size),
|
||||
decay_(decay),
|
||||
history_cursor_(0),
|
||||
count_(0),
|
||||
array_mean_(0.0f),
|
||||
buffer_full_(false) {
|
||||
history_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
|
||||
for (size_t i = 0; i < num_freqs_; ++i) {
|
||||
history_[i].reset(new complex<float>[window_size_]());
|
||||
}
|
||||
subhistory_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
|
||||
for (size_t i = 0; i < num_freqs_; ++i) {
|
||||
subhistory_[i].reset(new complex<float>[window_size_]());
|
||||
}
|
||||
subhistory_sq_.reset(new rtc::scoped_ptr<complex<float>[]>[num_freqs_]());
|
||||
for (size_t i = 0; i < num_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;
|
||||
case kStepBlockBasedMovingAverage:
|
||||
step_func_ = &VarianceArray::BlockBasedMovingAverage;
|
||||
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 (size_t i = 0; i < num_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);
|
||||
}
|
||||
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 (size_t i = 0; i < num_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] = (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*/) {
|
||||
size_t num = min(count_ + 1, window_size_);
|
||||
array_mean_ = 0.0f;
|
||||
for (size_t i = 0; i < num_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 (size_t 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*/) {
|
||||
size_t blocks = min(window_size_, history_cursor_ + 1);
|
||||
for (size_t i = 0; i < num_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 (size_t j = 0; j < min(window_size_, history_cursor_); ++j) {
|
||||
AddToMean(subhistory_[i][j], j + 1, &running_mean_[i]);
|
||||
AddToMean(subhistory_sq_[i][j], j + 1, &running_mean_sq_[i]);
|
||||
}
|
||||
++history_cursor_;
|
||||
}
|
||||
}
|
||||
++count_;
|
||||
if (count_ == kWindowBlockSize) {
|
||||
count_ = 0;
|
||||
}
|
||||
}
|
||||
|
||||
// Recomputes variances for each window from scratch based on previous window.
|
||||
void VarianceArray::BlockBasedMovingAverage(const std::complex<float>* data,
|
||||
bool /*dummy*/) {
|
||||
// TODO(ekmeyerson) To mitigate potential divergence, add counter so that
|
||||
// after every so often sums are computed scratch by summing over all
|
||||
// elements instead of subtracting oldest and adding newest.
|
||||
for (size_t i = 0; i < num_freqs_; ++i) {
|
||||
sub_running_mean_[i] += data[i];
|
||||
sub_running_mean_sq_[i] += data[i] * std::conj(data[i]);
|
||||
}
|
||||
++count_;
|
||||
|
||||
// TODO(ekmeyerson) Make kWindowBlockSize nonconstant to allow
|
||||
// experimentation with different block size,window size pairs.
|
||||
if (count_ >= kWindowBlockSize) {
|
||||
count_ = 0;
|
||||
|
||||
for (size_t i = 0; i < num_freqs_; ++i) {
|
||||
running_mean_[i] -= subhistory_[i][history_cursor_];
|
||||
running_mean_sq_[i] -= subhistory_sq_[i][history_cursor_];
|
||||
|
||||
float scale = 1.f / kWindowBlockSize;
|
||||
subhistory_[i][history_cursor_] = sub_running_mean_[i] * scale;
|
||||
subhistory_sq_[i][history_cursor_] = sub_running_mean_sq_[i] * scale;
|
||||
|
||||
sub_running_mean_[i] = std::complex<float>(0.0f, 0.0f);
|
||||
sub_running_mean_sq_[i] = std::complex<float>(0.0f, 0.0f);
|
||||
|
||||
running_mean_[i] += subhistory_[i][history_cursor_];
|
||||
running_mean_sq_[i] += subhistory_sq_[i][history_cursor_];
|
||||
|
||||
scale = 1.f / (buffer_full_ ? window_size_ : history_cursor_ + 1);
|
||||
variance_[i] = std::real(running_mean_sq_[i] * scale -
|
||||
running_mean_[i] * scale *
|
||||
std::conj(running_mean_[i]) * scale);
|
||||
}
|
||||
|
||||
++history_cursor_;
|
||||
if (history_cursor_ >= window_size_) {
|
||||
buffer_full_ = true;
|
||||
history_cursor_ = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void VarianceArray::Clear() {
|
||||
memset(running_mean_.get(), 0, sizeof(*running_mean_.get()) * num_freqs_);
|
||||
memset(running_mean_sq_.get(), 0,
|
||||
sizeof(*running_mean_sq_.get()) * num_freqs_);
|
||||
memset(variance_.get(), 0, sizeof(*variance_.get()) * num_freqs_);
|
||||
memset(conj_sum_.get(), 0, sizeof(*conj_sum_.get()) * num_freqs_);
|
||||
history_cursor_ = 0;
|
||||
count_ = 0;
|
||||
array_mean_ = 0.0f;
|
||||
}
|
||||
|
||||
void VarianceArray::ApplyScale(float scale) {
|
||||
array_mean_ = 0.0f;
|
||||
for (size_t i = 0; i < num_freqs_; ++i) {
|
||||
variance_[i] *= scale * scale;
|
||||
array_mean_ += (variance_[i] - array_mean_) / (i + 1);
|
||||
}
|
||||
}
|
||||
|
||||
GainApplier::GainApplier(size_t freqs, float change_limit)
|
||||
: num_freqs_(freqs),
|
||||
change_limit_(change_limit),
|
||||
target_(new float[freqs]()),
|
||||
current_(new float[freqs]()) {
|
||||
for (size_t 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 (size_t i = 0; i < num_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
|
||||
@ -0,0 +1,160 @@
|
||||
/*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
//
|
||||
// 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/base/scoped_ptr.h"
|
||||
|
||||
namespace webrtc {
|
||||
|
||||
namespace intelligibility {
|
||||
|
||||
// Return |current| changed towards |target|, with the change being at most
|
||||
// |limit|.
|
||||
float UpdateFactor(float target, float current, float limit);
|
||||
|
||||
// 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.
|
||||
std::complex<float> zerofudge(std::complex<float> c);
|
||||
|
||||
// Incremental mean computation. Return the mean of the series with the
|
||||
// mean |mean| with added |data|.
|
||||
std::complex<float> NewMean(std::complex<float> mean,
|
||||
std::complex<float> data,
|
||||
size_t count);
|
||||
|
||||
// Updates |mean| with added |data|;
|
||||
void AddToMean(std::complex<float> data,
|
||||
size_t count,
|
||||
std::complex<float>* mean);
|
||||
|
||||
// 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,
|
||||
kStepBlockBasedMovingAverage
|
||||
};
|
||||
|
||||
// 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(size_t freqs, StepType type, size_t 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);
|
||||
void BlockBasedMovingAverage(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.
|
||||
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.
|
||||
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.
|
||||
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.
|
||||
rtc::scoped_ptr<float[]> variance_;
|
||||
rtc::scoped_ptr<float[]> conj_sum_;
|
||||
|
||||
const size_t num_freqs_;
|
||||
const size_t window_size_;
|
||||
const float decay_;
|
||||
size_t history_cursor_;
|
||||
size_t count_;
|
||||
float array_mean_;
|
||||
bool buffer_full_;
|
||||
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(size_t 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 size_t num_freqs_;
|
||||
const float change_limit_;
|
||||
rtc::scoped_ptr<float[]> target_;
|
||||
rtc::scoped_ptr<float[]> current_;
|
||||
};
|
||||
|
||||
} // namespace intelligibility
|
||||
|
||||
} // namespace webrtc
|
||||
|
||||
#endif // WEBRTC_MODULES_AUDIO_PROCESSING_INTELLIGIBILITY_INTELLIGIBILITY_UTILS_H_
|
||||
Reference in New Issue
Block a user