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/*
* Copyright (c) 2013 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "webrtc/modules/audio_processing/transient/transient_detector.h"
#include <assert.h>
#include <float.h>
#include <math.h>
#include <string.h>
#include "webrtc/modules/audio_processing/transient/common.h"
#include "webrtc/modules/audio_processing/transient/daubechies_8_wavelet_coeffs.h"
#include "webrtc/modules/audio_processing/transient/moving_moments.h"
#include "webrtc/modules/audio_processing/transient/wpd_tree.h"
namespace webrtc {
static const int kTransientLengthMs = 30;
static const int kChunksAtStartupLeftToDelete =
kTransientLengthMs / ts::kChunkSizeMs;
static const float kDetectThreshold = 16.f;
TransientDetector::TransientDetector(int sample_rate_hz)
: samples_per_chunk_(sample_rate_hz * ts::kChunkSizeMs / 1000),
last_first_moment_(),
last_second_moment_(),
chunks_at_startup_left_to_delete_(kChunksAtStartupLeftToDelete),
reference_energy_(1.f),
using_reference_(false) {
assert(sample_rate_hz == ts::kSampleRate8kHz ||
sample_rate_hz == ts::kSampleRate16kHz ||
sample_rate_hz == ts::kSampleRate32kHz ||
sample_rate_hz == ts::kSampleRate48kHz);
int samples_per_transient = sample_rate_hz * kTransientLengthMs / 1000;
// Adjustment to avoid data loss while downsampling, making
// |samples_per_chunk_| and |samples_per_transient| always divisible by
// |kLeaves|.
samples_per_chunk_ -= samples_per_chunk_ % kLeaves;
samples_per_transient -= samples_per_transient % kLeaves;
tree_leaves_data_length_ = samples_per_chunk_ / kLeaves;
wpd_tree_.reset(new WPDTree(samples_per_chunk_,
kDaubechies8HighPassCoefficients,
kDaubechies8LowPassCoefficients,
kDaubechies8CoefficientsLength,
kLevels));
for (size_t i = 0; i < kLeaves; ++i) {
moving_moments_[i].reset(
new MovingMoments(samples_per_transient / kLeaves));
}
first_moments_.reset(new float[tree_leaves_data_length_]);
second_moments_.reset(new float[tree_leaves_data_length_]);
for (int i = 0; i < kChunksAtStartupLeftToDelete; ++i) {
previous_results_.push_back(0.f);
}
}
TransientDetector::~TransientDetector() {}
float TransientDetector::Detect(const float* data,
size_t data_length,
const float* reference_data,
size_t reference_length) {
assert(data && data_length == samples_per_chunk_);
// TODO(aluebs): Check if these errors can logically happen and if not assert
// on them.
if (wpd_tree_->Update(data, samples_per_chunk_) != 0) {
return -1.f;
}
float result = 0.f;
for (size_t i = 0; i < kLeaves; ++i) {
WPDNode* leaf = wpd_tree_->NodeAt(kLevels, i);
moving_moments_[i]->CalculateMoments(leaf->data(),
tree_leaves_data_length_,
first_moments_.get(),
second_moments_.get());
// Add value delayed (Use the last moments from the last call to Detect).
float unbiased_data = leaf->data()[0] - last_first_moment_[i];
result +=
unbiased_data * unbiased_data / (last_second_moment_[i] + FLT_MIN);
// Add new values.
for (size_t j = 1; j < tree_leaves_data_length_; ++j) {
unbiased_data = leaf->data()[j] - first_moments_[j - 1];
result +=
unbiased_data * unbiased_data / (second_moments_[j - 1] + FLT_MIN);
}
last_first_moment_[i] = first_moments_[tree_leaves_data_length_ - 1];
last_second_moment_[i] = second_moments_[tree_leaves_data_length_ - 1];
}
result /= tree_leaves_data_length_;
result *= ReferenceDetectionValue(reference_data, reference_length);
if (chunks_at_startup_left_to_delete_ > 0) {
chunks_at_startup_left_to_delete_--;
result = 0.f;
}
if (result >= kDetectThreshold) {
result = 1.f;
} else {
// Get proportional value.
// Proportion achieved with a squared raised cosine function with domain
// [0, kDetectThreshold) and image [0, 1), it's always increasing.
const float horizontal_scaling = ts::kPi / kDetectThreshold;
const float kHorizontalShift = ts::kPi;
const float kVerticalScaling = 0.5f;
const float kVerticalShift = 1.f;
result = (cos(result * horizontal_scaling + kHorizontalShift)
+ kVerticalShift) * kVerticalScaling;
result *= result;
}
previous_results_.pop_front();
previous_results_.push_back(result);
// In the current implementation we return the max of the current result and
// the previous results, so the high results have a width equals to
// |transient_length|.
return *std::max_element(previous_results_.begin(), previous_results_.end());
}
// Looks for the highest slope and compares it with the previous ones.
// An exponential transformation takes this to the [0, 1] range. This value is
// multiplied by the detection result to avoid false positives.
float TransientDetector::ReferenceDetectionValue(const float* data,
size_t length) {
if (data == NULL) {
using_reference_ = false;
return 1.f;
}
static const float kEnergyRatioThreshold = 0.2f;
static const float kReferenceNonLinearity = 20.f;
static const float kMemory = 0.99f;
float reference_energy = 0.f;
for (size_t i = 1; i < length; ++i) {
reference_energy += data[i] * data[i];
}
if (reference_energy == 0.f) {
using_reference_ = false;
return 1.f;
}
assert(reference_energy_ != 0);
float result = 1.f / (1.f + exp(kReferenceNonLinearity *
(kEnergyRatioThreshold -
reference_energy / reference_energy_)));
reference_energy_ =
kMemory * reference_energy_ + (1.f - kMemory) * reference_energy;
using_reference_ = true;
return result;
}
} // namespace webrtc
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