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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/lp_residual.h"
#include <algorithm>
#include <array>
#include <cmath>
#include <numeric>
#include "rtc_base/checks.h"
namespace webrtc {
namespace rnn_vad {
namespace {
// Computes cross-correlation coefficients between |x| and |y| and writes them
// in |x_corr|. The lag values are in {0, ..., max_lag - 1}, where max_lag
// equals the size of |x_corr|.
// The |x| and |y| sub-arrays used to compute a cross-correlation coefficients
// for a lag l have both size "size of |x| - l" - i.e., the longest sub-array is
// used. |x| and |y| must have the same size.
void ComputeCrossCorrelation(
rtc::ArrayView<const float> x,
rtc::ArrayView<const float> y,
rtc::ArrayView<float, kNumLpcCoefficients> x_corr) {
constexpr size_t max_lag = x_corr.size();
RTC_DCHECK_EQ(x.size(), y.size());
RTC_DCHECK_LT(max_lag, x.size());
for (size_t lag = 0; lag < max_lag; ++lag) {
x_corr[lag] =
std::inner_product(x.begin(), x.end() - lag, y.begin() + lag, 0.f);
}
}
// Applies denoising to the auto-correlation coefficients.
void DenoiseAutoCorrelation(
rtc::ArrayView<float, kNumLpcCoefficients> auto_corr) {
// Assume -40 dB white noise floor.
auto_corr[0] *= 1.0001f;
for (size_t i = 1; i < kNumLpcCoefficients; ++i) {
auto_corr[i] -= auto_corr[i] * (0.008f * i) * (0.008f * i);
}
}
// Computes the initial inverse filter coefficients given the auto-correlation
// coefficients of an input frame.
void ComputeInitialInverseFilterCoefficients(
rtc::ArrayView<const float, kNumLpcCoefficients> auto_corr,
rtc::ArrayView<float, kNumLpcCoefficients - 1> lpc_coeffs) {
float error = auto_corr[0];
for (size_t i = 0; i < kNumLpcCoefficients - 1; ++i) {
float reflection_coeff = 0.f;
for (size_t j = 0; j < i; ++j) {
reflection_coeff += lpc_coeffs[j] * auto_corr[i - j];
}
reflection_coeff += auto_corr[i + 1];
// Avoid division by numbers close to zero.
constexpr float kMinErrorMagnitude = 1e-6f;
if (std::fabs(error) < kMinErrorMagnitude) {
error = std::copysign(kMinErrorMagnitude, error);
}
reflection_coeff /= -error;
// Update LPC coefficients and total error.
lpc_coeffs[i] = reflection_coeff;
for (size_t j = 0; j<(i + 1)>> 1; ++j) {
const float tmp1 = lpc_coeffs[j];
const float tmp2 = lpc_coeffs[i - 1 - j];
lpc_coeffs[j] = tmp1 + reflection_coeff * tmp2;
lpc_coeffs[i - 1 - j] = tmp2 + reflection_coeff * tmp1;
}
error -= reflection_coeff * reflection_coeff * error;
if (error < 0.001f * auto_corr[0]) {
break;
}
}
}
} // namespace
void ComputeAndPostProcessLpcCoefficients(
rtc::ArrayView<const float> x,
rtc::ArrayView<float, kNumLpcCoefficients> lpc_coeffs) {
std::array<float, kNumLpcCoefficients> auto_corr;
ComputeCrossCorrelation(x, x, {auto_corr.data(), auto_corr.size()});
if (auto_corr[0] == 0.f) { // Empty frame.
std::fill(lpc_coeffs.begin(), lpc_coeffs.end(), 0);
return;
}
DenoiseAutoCorrelation({auto_corr.data(), auto_corr.size()});
std::array<float, kNumLpcCoefficients - 1> lpc_coeffs_pre{};
ComputeInitialInverseFilterCoefficients(auto_corr, lpc_coeffs_pre);
// LPC coefficients post-processing.
// TODO(bugs.webrtc.org/9076): Consider removing these steps.
float c1 = 1.f;
for (size_t i = 0; i < kNumLpcCoefficients - 1; ++i) {
c1 *= 0.9f;
lpc_coeffs_pre[i] *= c1;
}
const float c2 = 0.8f;
lpc_coeffs[0] = lpc_coeffs_pre[0] + c2;
lpc_coeffs[1] = lpc_coeffs_pre[1] + c2 * lpc_coeffs_pre[0];
lpc_coeffs[2] = lpc_coeffs_pre[2] + c2 * lpc_coeffs_pre[1];
lpc_coeffs[3] = lpc_coeffs_pre[3] + c2 * lpc_coeffs_pre[2];
lpc_coeffs[4] = c2 * lpc_coeffs_pre[3];
}
void ComputeLpResidual(
rtc::ArrayView<const float, kNumLpcCoefficients> lpc_coeffs,
rtc::ArrayView<const float> x,
rtc::ArrayView<float> y) {
RTC_DCHECK_LT(kNumLpcCoefficients, x.size());
RTC_DCHECK_EQ(x.size(), y.size());
std::array<float, kNumLpcCoefficients> input_chunk;
input_chunk.fill(0.f);
for (size_t i = 0; i < y.size(); ++i) {
const float sum = std::inner_product(input_chunk.begin(), input_chunk.end(),
lpc_coeffs.begin(), x[i]);
// Circular shift and add a new sample.
for (size_t j = kNumLpcCoefficients - 1; j > 0; --j)
input_chunk[j] = input_chunk[j - 1];
input_chunk[0] = x[i];
// Copy result.
y[i] = sum;
}
}
} // namespace rnn_vad
} // namespace webrtc
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