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217 lines
8.5 KiB
C++
217 lines
8.5 KiB
C++
/*
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* Copyright (c) 2012 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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#include "webrtc/modules/audio_coding/neteq/time_stretch.h"
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#include <algorithm> // min, max
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#include "webrtc/common_audio/signal_processing/include/signal_processing_library.h"
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#include "webrtc/modules/audio_coding/neteq/background_noise.h"
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#include "webrtc/modules/audio_coding/neteq/dsp_helper.h"
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#include "webrtc/system_wrappers/interface/scoped_ptr.h"
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namespace webrtc {
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TimeStretch::ReturnCodes TimeStretch::Process(
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const int16_t* input,
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size_t input_len,
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AudioMultiVector* output,
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int16_t* length_change_samples) {
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// Pre-calculate common multiplication with |fs_mult_|.
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int fs_mult_120 = fs_mult_ * 120; // Corresponds to 15 ms.
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const int16_t* signal;
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scoped_ptr<int16_t[]> signal_array;
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size_t signal_len;
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if (num_channels_ == 1) {
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signal = input;
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signal_len = input_len;
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} else {
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// We want |signal| to be only the first channel of |input|, which is
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// interleaved. Thus, we take the first sample, skip forward |num_channels|
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// samples, and continue like that.
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signal_len = input_len / num_channels_;
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signal_array.reset(new int16_t[signal_len]);
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signal = signal_array.get();
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size_t j = master_channel_;
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for (size_t i = 0; i < signal_len; ++i) {
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signal_array[i] = input[j];
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j += num_channels_;
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}
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}
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// Find maximum absolute value of input signal.
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max_input_value_ = WebRtcSpl_MaxAbsValueW16(signal,
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static_cast<int>(signal_len));
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// Downsample to 4 kHz sample rate and calculate auto-correlation.
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DspHelper::DownsampleTo4kHz(signal, signal_len, kDownsampledLen,
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sample_rate_hz_, true /* compensate delay*/,
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downsampled_input_);
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AutoCorrelation();
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// Find the strongest correlation peak.
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static const int kNumPeaks = 1;
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int peak_index;
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int16_t peak_value;
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DspHelper::PeakDetection(auto_correlation_, kCorrelationLen, kNumPeaks,
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fs_mult_, &peak_index, &peak_value);
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// Assert that |peak_index| stays within boundaries.
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assert(peak_index >= 0);
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assert(peak_index <= (2 * kCorrelationLen - 1) * fs_mult_);
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// Compensate peak_index for displaced starting position. The displacement
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// happens in AutoCorrelation(). Here, |kMinLag| is in the down-sampled 4 kHz
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// domain, while the |peak_index| is in the original sample rate; hence, the
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// multiplication by fs_mult_ * 2.
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peak_index += kMinLag * fs_mult_ * 2;
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// Assert that |peak_index| stays within boundaries.
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assert(peak_index >= 20 * fs_mult_);
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assert(peak_index <= 20 * fs_mult_ + (2 * kCorrelationLen - 1) * fs_mult_);
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// Calculate scaling to ensure that |peak_index| samples can be square-summed
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// without overflowing.
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int scaling = 31 - WebRtcSpl_NormW32(max_input_value_ * max_input_value_) -
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WebRtcSpl_NormW32(peak_index);
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scaling = std::max(0, scaling);
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// |vec1| starts at 15 ms minus one pitch period.
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const int16_t* vec1 = &signal[fs_mult_120 - peak_index];
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// |vec2| start at 15 ms.
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const int16_t* vec2 = &signal[fs_mult_120];
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// Calculate energies for |vec1| and |vec2|, assuming they both contain
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// |peak_index| samples.
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int32_t vec1_energy =
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WebRtcSpl_DotProductWithScale(vec1, vec1, peak_index, scaling);
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int32_t vec2_energy =
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WebRtcSpl_DotProductWithScale(vec2, vec2, peak_index, scaling);
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// Calculate cross-correlation between |vec1| and |vec2|.
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int32_t cross_corr =
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WebRtcSpl_DotProductWithScale(vec1, vec2, peak_index, scaling);
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// Check if the signal seems to be active speech or not (simple VAD).
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bool active_speech = SpeechDetection(vec1_energy, vec2_energy, peak_index,
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scaling);
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int16_t best_correlation;
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if (!active_speech) {
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SetParametersForPassiveSpeech(signal_len, &best_correlation, &peak_index);
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} else {
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// Calculate correlation:
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// cross_corr / sqrt(vec1_energy * vec2_energy).
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// Start with calculating scale values.
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int energy1_scale = std::max(0, 16 - WebRtcSpl_NormW32(vec1_energy));
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int energy2_scale = std::max(0, 16 - WebRtcSpl_NormW32(vec2_energy));
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// Make sure total scaling is even (to simplify scale factor after sqrt).
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if ((energy1_scale + energy2_scale) & 1) {
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// The sum is odd.
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energy1_scale += 1;
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}
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// Scale energies to int16_t.
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int16_t vec1_energy_int16 =
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static_cast<int16_t>(vec1_energy >> energy1_scale);
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int16_t vec2_energy_int16 =
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static_cast<int16_t>(vec2_energy >> energy2_scale);
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// Calculate square-root of energy product.
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int16_t sqrt_energy_prod = WebRtcSpl_SqrtFloor(vec1_energy_int16 *
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vec2_energy_int16);
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// Calculate cross_corr / sqrt(en1*en2) in Q14.
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int temp_scale = 14 - (energy1_scale + energy2_scale) / 2;
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cross_corr = WEBRTC_SPL_SHIFT_W32(cross_corr, temp_scale);
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cross_corr = std::max(0, cross_corr); // Don't use if negative.
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best_correlation = WebRtcSpl_DivW32W16(cross_corr, sqrt_energy_prod);
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// Make sure |best_correlation| is no larger than 1 in Q14.
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best_correlation = std::min(static_cast<int16_t>(16384), best_correlation);
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}
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// Check accelerate criteria and stretch the signal.
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ReturnCodes return_value = CheckCriteriaAndStretch(
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input, input_len, peak_index, best_correlation, active_speech, output);
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switch (return_value) {
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case kSuccess:
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*length_change_samples = peak_index;
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break;
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case kSuccessLowEnergy:
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*length_change_samples = peak_index;
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break;
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case kNoStretch:
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case kError:
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*length_change_samples = 0;
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break;
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}
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return return_value;
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}
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void TimeStretch::AutoCorrelation() {
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// Set scaling factor for cross correlation to protect against overflow.
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int scaling = kLogCorrelationLen - WebRtcSpl_NormW32(
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max_input_value_ * max_input_value_);
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scaling = std::max(0, scaling);
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// Calculate correlation from lag kMinLag to lag kMaxLag in 4 kHz domain.
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int32_t auto_corr[kCorrelationLen];
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WebRtcSpl_CrossCorrelation(auto_corr, &downsampled_input_[kMaxLag],
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&downsampled_input_[kMaxLag - kMinLag],
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kCorrelationLen, kMaxLag - kMinLag, scaling, -1);
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// Normalize correlation to 14 bits and write to |auto_correlation_|.
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int32_t max_corr = WebRtcSpl_MaxAbsValueW32(auto_corr, kCorrelationLen);
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scaling = std::max(0, 17 - WebRtcSpl_NormW32(max_corr));
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WebRtcSpl_VectorBitShiftW32ToW16(auto_correlation_, kCorrelationLen,
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auto_corr, scaling);
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}
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bool TimeStretch::SpeechDetection(int32_t vec1_energy, int32_t vec2_energy,
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int peak_index, int scaling) const {
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// Check if the signal seems to be active speech or not (simple VAD).
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// If (vec1_energy + vec2_energy) / (2 * peak_index) <=
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// 8 * background_noise_energy, then we say that the signal contains no
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// active speech.
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// Rewrite the inequality as:
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// (vec1_energy + vec2_energy) / 16 <= peak_index * background_noise_energy.
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// The two sides of the inequality will be denoted |left_side| and
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// |right_side|.
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int32_t left_side = (vec1_energy + vec2_energy) / 16;
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int32_t right_side;
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if (background_noise_.initialized()) {
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right_side = background_noise_.Energy(master_channel_);
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} else {
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// If noise parameters have not been estimated, use a fixed threshold.
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right_side = 75000;
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}
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int right_scale = 16 - WebRtcSpl_NormW32(right_side);
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right_scale = std::max(0, right_scale);
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left_side = left_side >> right_scale;
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right_side = peak_index * (right_side >> right_scale);
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// Scale |left_side| properly before comparing with |right_side|.
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// (|scaling| is the scale factor before energy calculation, thus the scale
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// factor for the energy is 2 * scaling.)
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if (WebRtcSpl_NormW32(left_side) < 2 * scaling) {
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// Cannot scale only |left_side|, must scale |right_side| too.
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int temp_scale = WebRtcSpl_NormW32(left_side);
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left_side = left_side << temp_scale;
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right_side = right_side >> (2 * scaling - temp_scale);
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} else {
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left_side = left_side << 2 * scaling;
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}
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return left_side > right_side;
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}
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} // namespace webrtc
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