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/*
* Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE file in the root of the source
* tree. An additional intellectual property rights grant can be found
* in the file PATENTS. All contributing project authors may
* be found in the AUTHORS file in the root of the source tree.
*/
#include "modules/audio_processing/agc2/rnn_vad/spectral_features.h"
#include <algorithm>
#include <cmath>
#include <limits>
#include <numeric>
#include "modules/audio_processing/agc2/rnn_vad/spectral_features_internal.h"
#include "rtc_base/checks.h"
namespace webrtc {
namespace rnn_vad {
namespace {
constexpr float kSilenceThreshold = 0.04f;
// Computes the new spectral difference stats and pushes them into the passed
// symmetric matrix buffer.
void UpdateSpectralDifferenceStats(
rtc::ArrayView<const float, kNumBands> new_spectral_coeffs,
const RingBuffer<float, kNumBands, kSpectralCoeffsHistorySize>& ring_buf,
SymmetricMatrixBuffer<float, kSpectralCoeffsHistorySize>* sym_matrix_buf) {
RTC_DCHECK(sym_matrix_buf);
// Compute the new spectral distance stats.
std::array<float, kSpectralCoeffsHistorySize - 1> distances;
for (size_t i = 0; i < kSpectralCoeffsHistorySize - 1; ++i) {
const size_t delay = i + 1;
auto old_spectral_coeffs = ring_buf.GetArrayView(delay);
distances[i] = 0.f;
for (size_t k = 0; k < kNumBands; ++k) {
const float c = new_spectral_coeffs[k] - old_spectral_coeffs[k];
distances[i] += c * c;
}
}
// Push the new spectral distance stats into the symmetric matrix buffer.
sym_matrix_buf->Push(distances);
}
} // namespace
SpectralFeaturesView::SpectralFeaturesView(
rtc::ArrayView<float, kNumBands - kNumLowerBands> coeffs,
rtc::ArrayView<float, kNumLowerBands> average,
rtc::ArrayView<float, kNumLowerBands> first_derivative,
rtc::ArrayView<float, kNumLowerBands> second_derivative,
rtc::ArrayView<float, kNumLowerBands> cross_correlations,
float* variability)
: coeffs(coeffs),
average(average),
first_derivative(first_derivative),
second_derivative(second_derivative),
cross_correlations(cross_correlations),
variability(variability) {}
SpectralFeaturesView::SpectralFeaturesView(const SpectralFeaturesView&) =
default;
SpectralFeaturesView::~SpectralFeaturesView() = default;
SpectralFeaturesExtractor::SpectralFeaturesExtractor()
: fft_(),
reference_frame_fft_(kFrameSize20ms24kHz),
lagged_frame_fft_(kFrameSize20ms24kHz),
band_boundaries_(
ComputeBandBoundaryIndexes(kSampleRate24kHz, kFrameSize20ms24kHz)),
dct_table_(ComputeDctTable()) {}
SpectralFeaturesExtractor::~SpectralFeaturesExtractor() = default;
void SpectralFeaturesExtractor::Reset() {
spectral_coeffs_ring_buf_.Reset();
spectral_diffs_buf_.Reset();
}
bool SpectralFeaturesExtractor::CheckSilenceComputeFeatures(
rtc::ArrayView<const float, kFrameSize20ms24kHz> reference_frame,
rtc::ArrayView<const float, kFrameSize20ms24kHz> lagged_frame,
SpectralFeaturesView spectral_features) {
// Analyze reference frame.
fft_.ForwardFft(reference_frame, reference_frame_fft_);
ComputeBandEnergies(reference_frame_fft_, band_boundaries_,
reference_frame_energy_coeffs_);
// Check if the reference frame has silence.
const float tot_energy =
std::accumulate(reference_frame_energy_coeffs_.begin(),
reference_frame_energy_coeffs_.end(), 0.f);
if (tot_energy < kSilenceThreshold)
return true;
// Analyze lagged frame.
fft_.ForwardFft(lagged_frame, lagged_frame_fft_);
ComputeBandEnergies(lagged_frame_fft_, band_boundaries_,
lagged_frame_energy_coeffs_);
// Log of the band energies for the reference frame.
std::array<float, kNumBands> log_band_energy_coeffs;
ComputeLogBandEnergiesCoefficients(reference_frame_energy_coeffs_,
log_band_energy_coeffs);
// Decorrelate band-wise log energy coefficients via DCT.
std::array<float, kNumBands> log_band_energy_coeffs_decorrelated;
ComputeDct(log_band_energy_coeffs, dct_table_,
log_band_energy_coeffs_decorrelated);
// Normalize (based on training set stats).
log_band_energy_coeffs_decorrelated[0] -= 12;
log_band_energy_coeffs_decorrelated[1] -= 4;
// Update the ring buffer and the spectral difference stats.
spectral_coeffs_ring_buf_.Push(log_band_energy_coeffs_decorrelated);
UpdateSpectralDifferenceStats(log_band_energy_coeffs_decorrelated,
spectral_coeffs_ring_buf_,
&spectral_diffs_buf_);
// Write the higher bands spectral coefficients.
auto coeffs_src = spectral_coeffs_ring_buf_.GetArrayView(0);
RTC_DCHECK_EQ(coeffs_src.size() - kNumLowerBands,
spectral_features.coeffs.size());
std::copy(coeffs_src.begin() + kNumLowerBands, coeffs_src.end(),
spectral_features.coeffs.begin());
// Compute and write remaining features.
ComputeAvgAndDerivatives(spectral_features.average,
spectral_features.first_derivative,
spectral_features.second_derivative);
ComputeCrossCorrelation(spectral_features.cross_correlations);
RTC_DCHECK(spectral_features.variability);
*(spectral_features.variability) = ComputeVariability();
return false;
}
void SpectralFeaturesExtractor::ComputeAvgAndDerivatives(
rtc::ArrayView<float, kNumLowerBands> average,
rtc::ArrayView<float, kNumLowerBands> first_derivative,
rtc::ArrayView<float, kNumLowerBands> second_derivative) {
auto curr = spectral_coeffs_ring_buf_.GetArrayView(0);
auto prev1 = spectral_coeffs_ring_buf_.GetArrayView(1);
auto prev2 = spectral_coeffs_ring_buf_.GetArrayView(2);
RTC_DCHECK_EQ(average.size(), first_derivative.size());
RTC_DCHECK_EQ(first_derivative.size(), second_derivative.size());
RTC_DCHECK_LE(average.size(), curr.size());
for (size_t i = 0; i < average.size(); ++i) {
// Average, kernel: [1, 1, 1].
average[i] = curr[i] + prev1[i] + prev2[i];
// First derivative, kernel: [1, 0, - 1].
first_derivative[i] = curr[i] - prev2[i];
// Second derivative, Laplacian kernel: [1, -2, 1].
second_derivative[i] = curr[i] - 2 * prev1[i] + prev2[i];
}
}
void SpectralFeaturesExtractor::ComputeCrossCorrelation(
rtc::ArrayView<float, kNumLowerBands> cross_correlations) {
const auto& x = reference_frame_fft_;
const auto& y = lagged_frame_fft_;
auto cross_corr = [x, y](const size_t freq_bin_index) -> float {
return (x[freq_bin_index].real() * y[freq_bin_index].real() +
x[freq_bin_index].imag() * y[freq_bin_index].imag());
};
std::array<float, kNumBands> cross_corr_coeffs;
constexpr size_t kNumFftPoints = kFrameSize20ms24kHz / 2 + 1;
ComputeBandCoefficients(cross_corr, band_boundaries_, kNumFftPoints - 1,
cross_corr_coeffs);
// Normalize.
for (size_t i = 0; i < cross_corr_coeffs.size(); ++i) {
cross_corr_coeffs[i] =
cross_corr_coeffs[i] /
std::sqrt(0.001f + reference_frame_energy_coeffs_[i] *
lagged_frame_energy_coeffs_[i]);
}
// Decorrelate.
ComputeDct(cross_corr_coeffs, dct_table_, cross_correlations);
// Normalize (based on training set stats).
cross_correlations[0] -= 1.3f;
cross_correlations[1] -= 0.9f;
}
float SpectralFeaturesExtractor::ComputeVariability() {
// Compute spectral variability score.
float spec_variability = 0.f;
for (size_t delay1 = 0; delay1 < kSpectralCoeffsHistorySize; ++delay1) {
float min_dist = std::numeric_limits<float>::max();
for (size_t delay2 = 0; delay2 < kSpectralCoeffsHistorySize; ++delay2) {
if (delay1 == delay2) // The distance would be 0.
continue;
min_dist =
std::min(min_dist, spectral_diffs_buf_.GetValue(delay1, delay2));
}
spec_variability += min_dist;
}
// Normalize (based on training set stats).
return spec_variability / kSpectralCoeffsHistorySize - 2.1f;
}
} // namespace rnn_vad
} // namespace webrtc