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686 lines
27 KiB
C
686 lines
27 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_processing/utility/delay_estimator.h"
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#include <assert.h>
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#include <stdlib.h>
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#include <string.h>
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// Number of right shifts for scaling is linearly depending on number of bits in
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// the far-end binary spectrum.
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static const int kShiftsAtZero = 13; // Right shifts at zero binary spectrum.
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static const int kShiftsLinearSlope = 3;
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static const int32_t kProbabilityOffset = 1024; // 2 in Q9.
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static const int32_t kProbabilityLowerLimit = 8704; // 17 in Q9.
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static const int32_t kProbabilityMinSpread = 2816; // 5.5 in Q9.
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// Robust validation settings
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static const float kHistogramMax = 3000.f;
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static const float kLastHistogramMax = 250.f;
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static const float kMinHistogramThreshold = 1.5f;
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static const int kMinRequiredHits = 10;
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static const int kMaxHitsWhenPossiblyNonCausal = 10;
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static const int kMaxHitsWhenPossiblyCausal = 1000;
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static const float kQ14Scaling = 1.f / (1 << 14); // Scaling by 2^14 to get Q0.
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static const float kFractionSlope = 0.05f;
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static const float kMinFractionWhenPossiblyCausal = 0.5f;
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static const float kMinFractionWhenPossiblyNonCausal = 0.25f;
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// Counts and returns number of bits of a 32-bit word.
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static int BitCount(uint32_t u32) {
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uint32_t tmp = u32 - ((u32 >> 1) & 033333333333) -
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((u32 >> 2) & 011111111111);
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tmp = ((tmp + (tmp >> 3)) & 030707070707);
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tmp = (tmp + (tmp >> 6));
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tmp = (tmp + (tmp >> 12) + (tmp >> 24)) & 077;
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return ((int) tmp);
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}
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// Compares the |binary_vector| with all rows of the |binary_matrix| and counts
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// per row the number of times they have the same value.
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//
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// Inputs:
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// - binary_vector : binary "vector" stored in a long
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// - binary_matrix : binary "matrix" stored as a vector of long
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// - matrix_size : size of binary "matrix"
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//
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// Output:
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// - bit_counts : "Vector" stored as a long, containing for each
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// row the number of times the matrix row and the
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// input vector have the same value
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//
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static void BitCountComparison(uint32_t binary_vector,
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const uint32_t* binary_matrix,
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int matrix_size,
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int32_t* bit_counts) {
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int n = 0;
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// Compare |binary_vector| with all rows of the |binary_matrix|
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for (; n < matrix_size; n++) {
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bit_counts[n] = (int32_t) BitCount(binary_vector ^ binary_matrix[n]);
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}
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}
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// Collects necessary statistics for the HistogramBasedValidation(). This
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// function has to be called prior to calling HistogramBasedValidation(). The
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// statistics updated and used by the HistogramBasedValidation() are:
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// 1. the number of |candidate_hits|, which states for how long we have had the
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// same |candidate_delay|
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// 2. the |histogram| of candidate delays over time. This histogram is
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// weighted with respect to a reliability measure and time-varying to cope
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// with possible delay shifts.
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// For further description see commented code.
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//
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// Inputs:
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// - candidate_delay : The delay to validate.
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// - valley_depth_q14 : The cost function has a valley/minimum at the
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// |candidate_delay| location. |valley_depth_q14| is the
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// cost function difference between the minimum and
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// maximum locations. The value is in the Q14 domain.
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// - valley_level_q14 : Is the cost function value at the minimum, in Q14.
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static void UpdateRobustValidationStatistics(BinaryDelayEstimator* self,
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int candidate_delay,
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int32_t valley_depth_q14,
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int32_t valley_level_q14) {
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const float valley_depth = valley_depth_q14 * kQ14Scaling;
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float decrease_in_last_set = valley_depth;
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const int max_hits_for_slow_change = (candidate_delay < self->last_delay) ?
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kMaxHitsWhenPossiblyNonCausal : kMaxHitsWhenPossiblyCausal;
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int i = 0;
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assert(self->history_size == self->farend->history_size);
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// Reset |candidate_hits| if we have a new candidate.
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if (candidate_delay != self->last_candidate_delay) {
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self->candidate_hits = 0;
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self->last_candidate_delay = candidate_delay;
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}
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self->candidate_hits++;
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// The |histogram| is updated differently across the bins.
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// 1. The |candidate_delay| histogram bin is increased with the
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// |valley_depth|, which is a simple measure of how reliable the
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// |candidate_delay| is. The histogram is not increased above
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// |kHistogramMax|.
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self->histogram[candidate_delay] += valley_depth;
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if (self->histogram[candidate_delay] > kHistogramMax) {
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self->histogram[candidate_delay] = kHistogramMax;
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}
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// 2. The histogram bins in the neighborhood of |candidate_delay| are
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// unaffected. The neighborhood is defined as x + {-2, -1, 0, 1}.
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// 3. The histogram bins in the neighborhood of |last_delay| are decreased
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// with |decrease_in_last_set|. This value equals the difference between
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// the cost function values at the locations |candidate_delay| and
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// |last_delay| until we reach |max_hits_for_slow_change| consecutive hits
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// at the |candidate_delay|. If we exceed this amount of hits the
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// |candidate_delay| is a "potential" candidate and we start decreasing
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// these histogram bins more rapidly with |valley_depth|.
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if (self->candidate_hits < max_hits_for_slow_change) {
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decrease_in_last_set = (self->mean_bit_counts[self->compare_delay] -
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valley_level_q14) * kQ14Scaling;
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}
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// 4. All other bins are decreased with |valley_depth|.
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// TODO(bjornv): Investigate how to make this loop more efficient. Split up
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// the loop? Remove parts that doesn't add too much.
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for (i = 0; i < self->history_size; ++i) {
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int is_in_last_set = (i >= self->last_delay - 2) &&
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(i <= self->last_delay + 1) && (i != candidate_delay);
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int is_in_candidate_set = (i >= candidate_delay - 2) &&
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(i <= candidate_delay + 1);
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self->histogram[i] -= decrease_in_last_set * is_in_last_set +
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valley_depth * (!is_in_last_set && !is_in_candidate_set);
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// 5. No histogram bin can go below 0.
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if (self->histogram[i] < 0) {
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self->histogram[i] = 0;
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}
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}
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}
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// Validates the |candidate_delay|, estimated in WebRtc_ProcessBinarySpectrum(),
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// based on a mix of counting concurring hits with a modified histogram
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// of recent delay estimates. In brief a candidate is valid (returns 1) if it
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// is the most likely according to the histogram. There are a couple of
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// exceptions that are worth mentioning:
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// 1. If the |candidate_delay| < |last_delay| it can be that we are in a
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// non-causal state, breaking a possible echo control algorithm. Hence, we
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// open up for a quicker change by allowing the change even if the
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// |candidate_delay| is not the most likely one according to the histogram.
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// 2. There's a minimum number of hits (kMinRequiredHits) and the histogram
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// value has to reached a minimum (kMinHistogramThreshold) to be valid.
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// 3. The action is also depending on the filter length used for echo control.
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// If the delay difference is larger than what the filter can capture, we
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// also move quicker towards a change.
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// For further description see commented code.
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//
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// Input:
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// - candidate_delay : The delay to validate.
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//
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// Return value:
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// - is_histogram_valid : 1 - The |candidate_delay| is valid.
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// 0 - Otherwise.
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static int HistogramBasedValidation(const BinaryDelayEstimator* self,
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int candidate_delay) {
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float fraction = 1.f;
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float histogram_threshold = self->histogram[self->compare_delay];
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const int delay_difference = candidate_delay - self->last_delay;
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int is_histogram_valid = 0;
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// The histogram based validation of |candidate_delay| is done by comparing
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// the |histogram| at bin |candidate_delay| with a |histogram_threshold|.
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// This |histogram_threshold| equals a |fraction| of the |histogram| at bin
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// |last_delay|. The |fraction| is a piecewise linear function of the
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// |delay_difference| between the |candidate_delay| and the |last_delay|
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// allowing for a quicker move if
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// i) a potential echo control filter can not handle these large differences.
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// ii) keeping |last_delay| instead of updating to |candidate_delay| could
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// force an echo control into a non-causal state.
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// We further require the histogram to have reached a minimum value of
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// |kMinHistogramThreshold|. In addition, we also require the number of
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// |candidate_hits| to be more than |kMinRequiredHits| to remove spurious
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// values.
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// Calculate a comparison histogram value (|histogram_threshold|) that is
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// depending on the distance between the |candidate_delay| and |last_delay|.
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// TODO(bjornv): How much can we gain by turning the fraction calculation
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// into tables?
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if (delay_difference > self->allowed_offset) {
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fraction = 1.f - kFractionSlope * (delay_difference - self->allowed_offset);
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fraction = (fraction > kMinFractionWhenPossiblyCausal ? fraction :
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kMinFractionWhenPossiblyCausal);
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} else if (delay_difference < 0) {
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fraction = kMinFractionWhenPossiblyNonCausal -
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kFractionSlope * delay_difference;
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fraction = (fraction > 1.f ? 1.f : fraction);
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}
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histogram_threshold *= fraction;
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histogram_threshold = (histogram_threshold > kMinHistogramThreshold ?
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histogram_threshold : kMinHistogramThreshold);
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is_histogram_valid =
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(self->histogram[candidate_delay] >= histogram_threshold) &&
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(self->candidate_hits > kMinRequiredHits);
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return is_histogram_valid;
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}
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// Performs a robust validation of the |candidate_delay| estimated in
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// WebRtc_ProcessBinarySpectrum(). The algorithm takes the
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// |is_instantaneous_valid| and the |is_histogram_valid| and combines them
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// into a robust validation. The HistogramBasedValidation() has to be called
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// prior to this call.
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// For further description on how the combination is done, see commented code.
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//
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// Inputs:
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// - candidate_delay : The delay to validate.
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// - is_instantaneous_valid : The instantaneous validation performed in
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// WebRtc_ProcessBinarySpectrum().
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// - is_histogram_valid : The histogram based validation.
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//
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// Return value:
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// - is_robust : 1 - The candidate_delay is valid according to a
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// combination of the two inputs.
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// : 0 - Otherwise.
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static int RobustValidation(const BinaryDelayEstimator* self,
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int candidate_delay,
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int is_instantaneous_valid,
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int is_histogram_valid) {
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int is_robust = 0;
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// The final robust validation is based on the two algorithms; 1) the
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// |is_instantaneous_valid| and 2) the histogram based with result stored in
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// |is_histogram_valid|.
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// i) Before we actually have a valid estimate (|last_delay| == -2), we say
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// a candidate is valid if either algorithm states so
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// (|is_instantaneous_valid| OR |is_histogram_valid|).
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is_robust = (self->last_delay < 0) &&
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(is_instantaneous_valid || is_histogram_valid);
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// ii) Otherwise, we need both algorithms to be certain
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// (|is_instantaneous_valid| AND |is_histogram_valid|)
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is_robust |= is_instantaneous_valid && is_histogram_valid;
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// iii) With one exception, i.e., the histogram based algorithm can overrule
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// the instantaneous one if |is_histogram_valid| = 1 and the histogram
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// is significantly strong.
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is_robust |= is_histogram_valid &&
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(self->histogram[candidate_delay] > self->last_delay_histogram);
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return is_robust;
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}
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void WebRtc_FreeBinaryDelayEstimatorFarend(BinaryDelayEstimatorFarend* self) {
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if (self == NULL) {
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return;
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}
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free(self->binary_far_history);
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self->binary_far_history = NULL;
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free(self->far_bit_counts);
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self->far_bit_counts = NULL;
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free(self);
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}
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BinaryDelayEstimatorFarend* WebRtc_CreateBinaryDelayEstimatorFarend(
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int history_size) {
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BinaryDelayEstimatorFarend* self = NULL;
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if (history_size > 1) {
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// Sanity conditions fulfilled.
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self = malloc(sizeof(BinaryDelayEstimatorFarend));
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}
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if (self == NULL) {
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return NULL;
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}
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self->history_size = 0;
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self->binary_far_history = NULL;
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self->far_bit_counts = NULL;
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if (WebRtc_AllocateFarendBufferMemory(self, history_size) == 0) {
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WebRtc_FreeBinaryDelayEstimatorFarend(self);
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self = NULL;
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}
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return self;
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}
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int WebRtc_AllocateFarendBufferMemory(BinaryDelayEstimatorFarend* self,
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int history_size) {
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assert(self != NULL);
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// (Re-)Allocate memory for history buffers.
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self->binary_far_history =
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realloc(self->binary_far_history,
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history_size * sizeof(*self->binary_far_history));
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self->far_bit_counts = realloc(self->far_bit_counts,
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history_size * sizeof(*self->far_bit_counts));
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if ((self->binary_far_history == NULL) || (self->far_bit_counts == NULL)) {
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history_size = 0;
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}
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// Fill with zeros if we have expanded the buffers.
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if (history_size > self->history_size) {
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int size_diff = history_size - self->history_size;
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memset(&self->binary_far_history[self->history_size],
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0,
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sizeof(*self->binary_far_history) * size_diff);
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memset(&self->far_bit_counts[self->history_size],
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0,
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sizeof(*self->far_bit_counts) * size_diff);
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}
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self->history_size = history_size;
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return self->history_size;
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}
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void WebRtc_InitBinaryDelayEstimatorFarend(BinaryDelayEstimatorFarend* self) {
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assert(self != NULL);
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memset(self->binary_far_history, 0, sizeof(uint32_t) * self->history_size);
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memset(self->far_bit_counts, 0, sizeof(int) * self->history_size);
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}
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void WebRtc_SoftResetBinaryDelayEstimatorFarend(
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BinaryDelayEstimatorFarend* self, int delay_shift) {
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int abs_shift = abs(delay_shift);
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int shift_size = 0;
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int dest_index = 0;
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int src_index = 0;
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int padding_index = 0;
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assert(self != NULL);
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shift_size = self->history_size - abs_shift;
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assert(shift_size > 0);
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if (delay_shift == 0) {
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return;
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} else if (delay_shift > 0) {
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dest_index = abs_shift;
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} else if (delay_shift < 0) {
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src_index = abs_shift;
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padding_index = shift_size;
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}
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// Shift and zero pad buffers.
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memmove(&self->binary_far_history[dest_index],
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&self->binary_far_history[src_index],
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sizeof(*self->binary_far_history) * shift_size);
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memset(&self->binary_far_history[padding_index], 0,
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sizeof(*self->binary_far_history) * abs_shift);
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memmove(&self->far_bit_counts[dest_index],
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&self->far_bit_counts[src_index],
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sizeof(*self->far_bit_counts) * shift_size);
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memset(&self->far_bit_counts[padding_index], 0,
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sizeof(*self->far_bit_counts) * abs_shift);
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}
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void WebRtc_AddBinaryFarSpectrum(BinaryDelayEstimatorFarend* handle,
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uint32_t binary_far_spectrum) {
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assert(handle != NULL);
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// Shift binary spectrum history and insert current |binary_far_spectrum|.
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memmove(&(handle->binary_far_history[1]), &(handle->binary_far_history[0]),
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(handle->history_size - 1) * sizeof(uint32_t));
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handle->binary_far_history[0] = binary_far_spectrum;
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// Shift history of far-end binary spectrum bit counts and insert bit count
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// of current |binary_far_spectrum|.
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memmove(&(handle->far_bit_counts[1]), &(handle->far_bit_counts[0]),
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(handle->history_size - 1) * sizeof(int));
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handle->far_bit_counts[0] = BitCount(binary_far_spectrum);
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}
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void WebRtc_FreeBinaryDelayEstimator(BinaryDelayEstimator* self) {
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if (self == NULL) {
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return;
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}
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free(self->mean_bit_counts);
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self->mean_bit_counts = NULL;
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free(self->bit_counts);
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self->bit_counts = NULL;
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free(self->binary_near_history);
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self->binary_near_history = NULL;
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free(self->histogram);
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self->histogram = NULL;
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// BinaryDelayEstimator does not have ownership of |farend|, hence we do not
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// free the memory here. That should be handled separately by the user.
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self->farend = NULL;
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free(self);
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}
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BinaryDelayEstimator* WebRtc_CreateBinaryDelayEstimator(
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BinaryDelayEstimatorFarend* farend, int max_lookahead) {
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BinaryDelayEstimator* self = NULL;
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if ((farend != NULL) && (max_lookahead >= 0)) {
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// Sanity conditions fulfilled.
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self = malloc(sizeof(BinaryDelayEstimator));
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}
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if (self == NULL) {
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return NULL;
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}
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self->farend = farend;
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self->near_history_size = max_lookahead + 1;
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self->history_size = 0;
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self->robust_validation_enabled = 0; // Disabled by default.
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self->allowed_offset = 0;
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self->lookahead = max_lookahead;
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// Allocate memory for spectrum and history buffers.
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self->mean_bit_counts = NULL;
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self->bit_counts = NULL;
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self->histogram = NULL;
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self->binary_near_history =
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malloc((max_lookahead + 1) * sizeof(*self->binary_near_history));
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if (self->binary_near_history == NULL ||
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WebRtc_AllocateHistoryBufferMemory(self, farend->history_size) == 0) {
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WebRtc_FreeBinaryDelayEstimator(self);
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self = NULL;
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}
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return self;
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}
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int WebRtc_AllocateHistoryBufferMemory(BinaryDelayEstimator* self,
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int history_size) {
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BinaryDelayEstimatorFarend* far = self->farend;
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// (Re-)Allocate memory for spectrum and history buffers.
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if (history_size != far->history_size) {
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// Only update far-end buffers if we need.
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history_size = WebRtc_AllocateFarendBufferMemory(far, history_size);
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}
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// The extra array element in |mean_bit_counts| and |histogram| is a dummy
|
|
// element only used while |last_delay| == -2, i.e., before we have a valid
|
|
// estimate.
|
|
self->mean_bit_counts =
|
|
realloc(self->mean_bit_counts,
|
|
(history_size + 1) * sizeof(*self->mean_bit_counts));
|
|
self->bit_counts =
|
|
realloc(self->bit_counts, history_size * sizeof(*self->bit_counts));
|
|
self->histogram =
|
|
realloc(self->histogram, (history_size + 1) * sizeof(*self->histogram));
|
|
|
|
if ((self->mean_bit_counts == NULL) ||
|
|
(self->bit_counts == NULL) ||
|
|
(self->histogram == NULL)) {
|
|
history_size = 0;
|
|
}
|
|
// Fill with zeros if we have expanded the buffers.
|
|
if (history_size > self->history_size) {
|
|
int size_diff = history_size - self->history_size;
|
|
memset(&self->mean_bit_counts[self->history_size],
|
|
0,
|
|
sizeof(*self->mean_bit_counts) * size_diff);
|
|
memset(&self->bit_counts[self->history_size],
|
|
0,
|
|
sizeof(*self->bit_counts) * size_diff);
|
|
memset(&self->histogram[self->history_size],
|
|
0,
|
|
sizeof(*self->histogram) * size_diff);
|
|
}
|
|
self->history_size = history_size;
|
|
|
|
return self->history_size;
|
|
}
|
|
|
|
void WebRtc_InitBinaryDelayEstimator(BinaryDelayEstimator* self) {
|
|
int i = 0;
|
|
assert(self != NULL);
|
|
|
|
memset(self->bit_counts, 0, sizeof(int32_t) * self->history_size);
|
|
memset(self->binary_near_history,
|
|
0,
|
|
sizeof(uint32_t) * self->near_history_size);
|
|
for (i = 0; i <= self->history_size; ++i) {
|
|
self->mean_bit_counts[i] = (20 << 9); // 20 in Q9.
|
|
self->histogram[i] = 0.f;
|
|
}
|
|
self->minimum_probability = kMaxBitCountsQ9; // 32 in Q9.
|
|
self->last_delay_probability = (int) kMaxBitCountsQ9; // 32 in Q9.
|
|
|
|
// Default return value if we're unable to estimate. -1 is used for errors.
|
|
self->last_delay = -2;
|
|
|
|
self->last_candidate_delay = -2;
|
|
self->compare_delay = self->history_size;
|
|
self->candidate_hits = 0;
|
|
self->last_delay_histogram = 0.f;
|
|
}
|
|
|
|
int WebRtc_SoftResetBinaryDelayEstimator(BinaryDelayEstimator* self,
|
|
int delay_shift) {
|
|
int lookahead = 0;
|
|
assert(self != NULL);
|
|
lookahead = self->lookahead;
|
|
self->lookahead -= delay_shift;
|
|
if (self->lookahead < 0) {
|
|
self->lookahead = 0;
|
|
}
|
|
if (self->lookahead > self->near_history_size - 1) {
|
|
self->lookahead = self->near_history_size - 1;
|
|
}
|
|
return lookahead - self->lookahead;
|
|
}
|
|
|
|
int WebRtc_ProcessBinarySpectrum(BinaryDelayEstimator* self,
|
|
uint32_t binary_near_spectrum) {
|
|
int i = 0;
|
|
int candidate_delay = -1;
|
|
int valid_candidate = 0;
|
|
|
|
int32_t value_best_candidate = kMaxBitCountsQ9;
|
|
int32_t value_worst_candidate = 0;
|
|
int32_t valley_depth = 0;
|
|
|
|
assert(self != NULL);
|
|
if (self->farend->history_size != self->history_size) {
|
|
// Non matching history sizes.
|
|
return -1;
|
|
}
|
|
if (self->near_history_size > 1) {
|
|
// If we apply lookahead, shift near-end binary spectrum history. Insert
|
|
// current |binary_near_spectrum| and pull out the delayed one.
|
|
memmove(&(self->binary_near_history[1]), &(self->binary_near_history[0]),
|
|
(self->near_history_size - 1) * sizeof(uint32_t));
|
|
self->binary_near_history[0] = binary_near_spectrum;
|
|
binary_near_spectrum = self->binary_near_history[self->lookahead];
|
|
}
|
|
|
|
// Compare with delayed spectra and store the |bit_counts| for each delay.
|
|
BitCountComparison(binary_near_spectrum, self->farend->binary_far_history,
|
|
self->history_size, self->bit_counts);
|
|
|
|
// Update |mean_bit_counts|, which is the smoothed version of |bit_counts|.
|
|
for (i = 0; i < self->history_size; i++) {
|
|
// |bit_counts| is constrained to [0, 32], meaning we can smooth with a
|
|
// factor up to 2^26. We use Q9.
|
|
int32_t bit_count = (self->bit_counts[i] << 9); // Q9.
|
|
|
|
// Update |mean_bit_counts| only when far-end signal has something to
|
|
// contribute. If |far_bit_counts| is zero the far-end signal is weak and
|
|
// we likely have a poor echo condition, hence don't update.
|
|
if (self->farend->far_bit_counts[i] > 0) {
|
|
// Make number of right shifts piecewise linear w.r.t. |far_bit_counts|.
|
|
int shifts = kShiftsAtZero;
|
|
shifts -= (kShiftsLinearSlope * self->farend->far_bit_counts[i]) >> 4;
|
|
WebRtc_MeanEstimatorFix(bit_count, shifts, &(self->mean_bit_counts[i]));
|
|
}
|
|
}
|
|
|
|
// Find |candidate_delay|, |value_best_candidate| and |value_worst_candidate|
|
|
// of |mean_bit_counts|.
|
|
for (i = 0; i < self->history_size; i++) {
|
|
if (self->mean_bit_counts[i] < value_best_candidate) {
|
|
value_best_candidate = self->mean_bit_counts[i];
|
|
candidate_delay = i;
|
|
}
|
|
if (self->mean_bit_counts[i] > value_worst_candidate) {
|
|
value_worst_candidate = self->mean_bit_counts[i];
|
|
}
|
|
}
|
|
valley_depth = value_worst_candidate - value_best_candidate;
|
|
|
|
// The |value_best_candidate| is a good indicator on the probability of
|
|
// |candidate_delay| being an accurate delay (a small |value_best_candidate|
|
|
// means a good binary match). In the following sections we make a decision
|
|
// whether to update |last_delay| or not.
|
|
// 1) If the difference bit counts between the best and the worst delay
|
|
// candidates is too small we consider the situation to be unreliable and
|
|
// don't update |last_delay|.
|
|
// 2) If the situation is reliable we update |last_delay| if the value of the
|
|
// best candidate delay has a value less than
|
|
// i) an adaptive threshold |minimum_probability|, or
|
|
// ii) this corresponding value |last_delay_probability|, but updated at
|
|
// this time instant.
|
|
|
|
// Update |minimum_probability|.
|
|
if ((self->minimum_probability > kProbabilityLowerLimit) &&
|
|
(valley_depth > kProbabilityMinSpread)) {
|
|
// The "hard" threshold can't be lower than 17 (in Q9).
|
|
// The valley in the curve also has to be distinct, i.e., the
|
|
// difference between |value_worst_candidate| and |value_best_candidate| has
|
|
// to be large enough.
|
|
int32_t threshold = value_best_candidate + kProbabilityOffset;
|
|
if (threshold < kProbabilityLowerLimit) {
|
|
threshold = kProbabilityLowerLimit;
|
|
}
|
|
if (self->minimum_probability > threshold) {
|
|
self->minimum_probability = threshold;
|
|
}
|
|
}
|
|
// Update |last_delay_probability|.
|
|
// We use a Markov type model, i.e., a slowly increasing level over time.
|
|
self->last_delay_probability++;
|
|
// Validate |candidate_delay|. We have a reliable instantaneous delay
|
|
// estimate if
|
|
// 1) The valley is distinct enough (|valley_depth| > |kProbabilityOffset|)
|
|
// and
|
|
// 2) The depth of the valley is deep enough
|
|
// (|value_best_candidate| < |minimum_probability|)
|
|
// and deeper than the best estimate so far
|
|
// (|value_best_candidate| < |last_delay_probability|)
|
|
valid_candidate = ((valley_depth > kProbabilityOffset) &&
|
|
((value_best_candidate < self->minimum_probability) ||
|
|
(value_best_candidate < self->last_delay_probability)));
|
|
|
|
if (self->robust_validation_enabled) {
|
|
int is_histogram_valid = 0;
|
|
UpdateRobustValidationStatistics(self, candidate_delay, valley_depth,
|
|
value_best_candidate);
|
|
is_histogram_valid = HistogramBasedValidation(self, candidate_delay);
|
|
valid_candidate = RobustValidation(self, candidate_delay, valid_candidate,
|
|
is_histogram_valid);
|
|
|
|
}
|
|
if (valid_candidate) {
|
|
if (candidate_delay != self->last_delay) {
|
|
self->last_delay_histogram =
|
|
(self->histogram[candidate_delay] > kLastHistogramMax ?
|
|
kLastHistogramMax : self->histogram[candidate_delay]);
|
|
// Adjust the histogram if we made a change to |last_delay|, though it was
|
|
// not the most likely one according to the histogram.
|
|
if (self->histogram[candidate_delay] <
|
|
self->histogram[self->compare_delay]) {
|
|
self->histogram[self->compare_delay] = self->histogram[candidate_delay];
|
|
}
|
|
}
|
|
self->last_delay = candidate_delay;
|
|
if (value_best_candidate < self->last_delay_probability) {
|
|
self->last_delay_probability = value_best_candidate;
|
|
}
|
|
self->compare_delay = self->last_delay;
|
|
}
|
|
|
|
return self->last_delay;
|
|
}
|
|
|
|
int WebRtc_binary_last_delay(BinaryDelayEstimator* self) {
|
|
assert(self != NULL);
|
|
return self->last_delay;
|
|
}
|
|
|
|
float WebRtc_binary_last_delay_quality(BinaryDelayEstimator* self) {
|
|
float quality = 0;
|
|
assert(self != NULL);
|
|
|
|
if (self->robust_validation_enabled) {
|
|
// Simply a linear function of the histogram height at delay estimate.
|
|
quality = self->histogram[self->compare_delay] / kHistogramMax;
|
|
} else {
|
|
// Note that |last_delay_probability| states how deep the minimum of the
|
|
// cost function is, so it is rather an error probability.
|
|
quality = (float) (kMaxBitCountsQ9 - self->last_delay_probability) /
|
|
kMaxBitCountsQ9;
|
|
if (quality < 0) {
|
|
quality = 0;
|
|
}
|
|
}
|
|
return quality;
|
|
}
|
|
|
|
void WebRtc_MeanEstimatorFix(int32_t new_value,
|
|
int factor,
|
|
int32_t* mean_value) {
|
|
int32_t diff = new_value - *mean_value;
|
|
|
|
// mean_new = mean_value + ((new_value - mean_value) >> factor);
|
|
if (diff < 0) {
|
|
diff = -((-diff) >> factor);
|
|
} else {
|
|
diff = (diff >> factor);
|
|
}
|
|
*mean_value += diff;
|
|
}
|