/*
* Copyright (c) 2012 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/utility/delay_estimator.h"
#include <assert.h>
#include <stdlib.h>
#include <string.h>
// Number of right shifts for scaling is linearly depending on number of bits in
// the far-end binary spectrum.
static const int kShiftsAtZero = 13; // Right shifts at zero binary spectrum.
static const int kShiftsLinearSlope = 3;
static const int32_t kProbabilityOffset = 1024; // 2 in Q9.
static const int32_t kProbabilityLowerLimit = 8704; // 17 in Q9.
static const int32_t kProbabilityMinSpread = 2816; // 5.5 in Q9.
// Robust validation settings
static const float kHistogramMax = 3000.f;
static const float kLastHistogramMax = 250.f;
static const float kMinHistogramThreshold = 1.5f;
static const int kMinRequiredHits = 10;
static const int kMaxHitsWhenPossiblyNonCausal = 10;
static const int kMaxHitsWhenPossiblyCausal = 1000;
static const float kQ14Scaling = 1.f / (1 << 14); // Scaling by 2^14 to get Q0.
static const float kFractionSlope = 0.05f;
static const float kMinFractionWhenPossiblyCausal = 0.5f;
static const float kMinFractionWhenPossiblyNonCausal = 0.25f;
// Counts and returns number of bits of a 32-bit word.
static int BitCount(uint32_t u32) {
uint32_t tmp = u32 - ((u32 >> 1) & 033333333333) -
((u32 >> 2) & 011111111111);
tmp = ((tmp + (tmp >> 3)) & 030707070707);
tmp = (tmp + (tmp >> 6));
tmp = (tmp + (tmp >> 12) + (tmp >> 24)) & 077;
return ((int) tmp);
}
// Compares the |binary_vector| with all rows of the |binary_matrix| and counts
// per row the number of times they have the same value.
//
// Inputs:
// - binary_vector : binary "vector" stored in a long
// - binary_matrix : binary "matrix" stored as a vector of long
// - matrix_size : size of binary "matrix"
//
// Output:
// - bit_counts : "Vector" stored as a long, containing for each
// row the number of times the matrix row and the
// input vector have the same value
//
static void BitCountComparison(uint32_t binary_vector,
const uint32_t* binary_matrix,
int matrix_size,
int32_t* bit_counts) {
int n = 0;
// Compare |binary_vector| with all rows of the |binary_matrix|
for (; n < matrix_size; n++) {
bit_counts[n] = (int32_t) BitCount(binary_vector ^ binary_matrix[n]);
}
}
// Collects necessary statistics for the HistogramBasedValidation(). This
// function has to be called prior to calling HistogramBasedValidation(). The
// statistics updated and used by the HistogramBasedValidation() are:
// 1. the number of |candidate_hits|, which states for how long we have had the
// same |candidate_delay|
// 2. the |histogram| of candidate delays over time. This histogram is
// weighted with respect to a reliability measure and time-varying to cope
// with possible delay shifts.
// For further description see commented code.
//
// Inputs:
// - candidate_delay : The delay to validate.
// - valley_depth_q14 : The cost function has a valley/minimum at the
// |candidate_delay| location. |valley_depth_q14| is the
// cost function difference between the minimum and
// maximum locations. The value is in the Q14 domain.
// - valley_level_q14 : Is the cost function value at the minimum, in Q14.
static void UpdateRobustValidationStatistics(BinaryDelayEstimator* self,
int candidate_delay,
int32_t valley_depth_q14,
int32_t valley_level_q14) {
const float valley_depth = valley_depth_q14 * kQ14Scaling;
float decrease_in_last_set = valley_depth;
const int max_hits_for_slow_change = (candidate_delay < self->last_delay) ?
kMaxHitsWhenPossiblyNonCausal : kMaxHitsWhenPossiblyCausal;
int i = 0;
assert(self->history_size == self->farend->history_size);
// Reset |candidate_hits| if we have a new candidate.
if (candidate_delay != self->last_candidate_delay) {
self->candidate_hits = 0;
self->last_candidate_delay = candidate_delay;
}
self->candidate_hits++;
// The |histogram| is updated differently across the bins.
// 1. The |candidate_delay| histogram bin is increased with the
// |valley_depth|, which is a simple measure of how reliable the
// |candidate_delay| is. The histogram is not increased above
// |kHistogramMax|.
self->histogram[candidate_delay] += valley_depth;
if (self->histogram[candidate_delay] > kHistogramMax) {
self->histogram[candidate_delay] = kHistogramMax;
}
// 2. The histogram bins in the neighborhood of |candidate_delay| are
// unaffected. The neighborhood is defined as x + {-2, -1, 0, 1}.
// 3. The histogram bins in the neighborhood of |last_delay| are decreased
// with |decrease_in_last_set|. This value equals the difference between
// the cost function values at the locations |candidate_delay| and
// |last_delay| until we reach |max_hits_for_slow_change| consecutive hits
// at the |candidate_delay|. If we exceed this amount of hits the
// |candidate_delay| is a "potential" candidate and we start decreasing
// these histogram bins more rapidly with |valley_depth|.
if (self->candidate_hits < max_hits_for_slow_change) {
decrease_in_last_set = (self->mean_bit_counts[self->compare_delay] -
valley_level_q14) * kQ14Scaling;
}
// 4. All other bins are decreased with |valley_depth|.
// TODO(bjornv): Investigate how to make this loop more efficient. Split up
// the loop? Remove parts that doesn't add too much.
for (i = 0; i < self->history_size; ++i) {
int is_in_last_set = (i >= self->last_delay - 2) &&
(i <= self->last_delay + 1) && (i != candidate_delay);
int is_in_candidate_set = (i >= candidate_delay - 2) &&
(i <= candidate_delay + 1);
self->histogram[i] -= decrease_in_last_set * is_in_last_set +
valley_depth * (!is_in_last_set && !is_in_candidate_set);
// 5. No histogram bin can go below 0.
if (self->histogram[i] < 0) {
self->histogram[i] = 0;
}
}
}
// Validates the |candidate_delay|, estimated in WebRtc_ProcessBinarySpectrum(),
// based on a mix of counting concurring hits with a modified histogram
// of recent delay estimates. In brief a candidate is valid (returns 1) if it
// is the most likely according to the histogram. There are a couple of
// exceptions that are worth mentioning:
// 1. If the |candidate_delay| < |last_delay| it can be that we are in a
// non-causal state, breaking a possible echo control algorithm. Hence, we
// open up for a quicker change by allowing the change even if the
// |candidate_delay| is not the most likely one according to the histogram.
// 2. There's a minimum number of hits (kMinRequiredHits) and the histogram
// value has to reached a minimum (kMinHistogramThreshold) to be valid.
// 3. The action is also depending on the filter length used for echo control.
// If the delay difference is larger than what the filter can capture, we
// also move quicker towards a change.
// For further description see commented code.
//
// Input:
// - candidate_delay : The delay to validate.
//
// Return value:
// - is_histogram_valid : 1 - The |candidate_delay| is valid.
// 0 - Otherwise.
static int HistogramBasedValidation(const BinaryDelayEstimator* self,
int candidate_delay) {
float fraction = 1.f;
float histogram_threshold = self->histogram[self->compare_delay];
const int delay_difference = candidate_delay - self->last_delay;
int is_histogram_valid = 0;
// The histogram based validation of |candidate_delay| is done by comparing
// the |histogram| at bin |candidate_delay| with a |histogram_threshold|.
// This |histogram_threshold| equals a |fraction| of the |histogram| at bin
// |last_delay|. The |fraction| is a piecewise linear function of the
// |delay_difference| between the |candidate_delay| and the |last_delay|
// allowing for a quicker move if
// i) a potential echo control filter can not handle these large differences.
// ii) keeping |last_delay| instead of updating to |candidate_delay| could
// force an echo control into a non-causal state.
// We further require the histogram to have reached a minimum value of
// |kMinHistogramThreshold|. In addition, we also require the number of
// |candidate_hits| to be more than |kMinRequiredHits| to remove spurious
// values.
// Calculate a comparison histogram value (|histogram_threshold|) that is
// depending on the distance between the |candidate_delay| and |last_delay|.
// TODO(bjornv): How much can we gain by turning the fraction calculation
// into tables?
if (delay_difference > self->allowed_offset) {
fraction = 1.f - kFractionSlope * (delay_difference - self->allowed_offset);
fraction = (fraction > kMinFractionWhenPossiblyCausal ? fraction :
kMinFractionWhenPossiblyCausal);
} else if (delay_difference < 0) {
fraction = kMinFractionWhenPossiblyNonCausal -
kFractionSlope * delay_difference;
fraction = (fraction > 1.f ? 1.f : fraction);
}
histogram_threshold *= fraction;
histogram_threshold = (histogram_threshold > kMinHistogramThreshold ?
histogram_threshold : kMinHistogramThreshold);
is_histogram_valid =
(self->histogram[candidate_delay] >= histogram_threshold) &&
(self->candidate_hits > kMinRequiredHits);
return is_histogram_valid;
}
// Performs a robust validation of the |candidate_delay| estimated in
// WebRtc_ProcessBinarySpectrum(). The algorithm takes the
// |is_instantaneous_valid| and the |is_histogram_valid| and combines them
// into a robust validation. The HistogramBasedValidation() has to be called
// prior to this call.
// For further description on how the combination is done, see commented code.
//
// Inputs:
// - candidate_delay : The delay to validate.
// - is_instantaneous_valid : The instantaneous validation performed in
// WebRtc_ProcessBinarySpectrum().
// - is_histogram_valid : The histogram based validation.
//
// Return value:
// - is_robust : 1 - The candidate_delay is valid according to a
// combination of the two inputs.
// : 0 - Otherwise.
static int RobustValidation(const BinaryDelayEstimator* self,
int candidate_delay,
int is_instantaneous_valid,
int is_histogram_valid) {
int is_robust = 0;
// The final robust validation is based on the two algorithms; 1) the
// |is_instantaneous_valid| and 2) the histogram based with result stored in
// |is_histogram_valid|.
// i) Before we actually have a valid estimate (|last_delay| == -2), we say
// a candidate is valid if either algorithm states so
// (|is_instantaneous_valid| OR |is_histogram_valid|).
is_robust = (self->last_delay < 0) &&
(is_instantaneous_valid || is_histogram_valid);
// ii) Otherwise, we need both algorithms to be certain
// (|is_instantaneous_valid| AND |is_histogram_valid|)
is_robust |= is_instantaneous_valid && is_histogram_valid;
// iii) With one exception, i.e., the histogram based algorithm can overrule
// the instantaneous one if |is_histogram_valid| = 1 and the histogram
// is significantly strong.
is_robust |= is_histogram_valid &&
(self->histogram[candidate_delay] > self->last_delay_histogram);
return is_robust;
}
void WebRtc_FreeBinaryDelayEstimatorFarend(BinaryDelayEstimatorFarend* self) {
if (self == NULL) {
return;
}
free(self->binary_far_history);
self->binary_far_history = NULL;
free(self->far_bit_counts);
self->far_bit_counts = NULL;
free(self);
}
BinaryDelayEstimatorFarend* WebRtc_CreateBinaryDelayEstimatorFarend(
int history_size) {
BinaryDelayEstimatorFarend* self = NULL;
if (history_size > 1) {
// Sanity conditions fulfilled.
self = malloc(sizeof(BinaryDelayEstimatorFarend));
}
if (self == NULL) {
return NULL;
}
self->history_size = 0;
self->binary_far_history = NULL;
self->far_bit_counts = NULL;
if (WebRtc_AllocateFarendBufferMemory(self, history_size) == 0) {
WebRtc_FreeBinaryDelayEstimatorFarend(self);
self = NULL;
}
return self;
}
int WebRtc_AllocateFarendBufferMemory(BinaryDelayEstimatorFarend* self,
int history_size) {
assert(self != NULL);
// (Re-)Allocate memory for history buffers.
self->binary_far_history =
realloc(self->binary_far_history,
history_size * sizeof(*self->binary_far_history));
self->far_bit_counts = realloc(self->far_bit_counts,
history_size * sizeof(*self->far_bit_counts));
if ((self->binary_far_history == NULL) || (self->far_bit_counts == 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->binary_far_history[self->history_size],
0,
sizeof(*self->binary_far_history) * size_diff);
memset(&self->far_bit_counts[self->history_size],
0,
sizeof(*self->far_bit_counts) * size_diff);
}
self->history_size = history_size;
return self->history_size;
}
void WebRtc_InitBinaryDelayEstimatorFarend(BinaryDelayEstimatorFarend* self) {
assert(self != NULL);
memset(self->binary_far_history, 0, sizeof(uint32_t) * self->history_size);
memset(self->far_bit_counts, 0, sizeof(int) * self->history_size);
}
void WebRtc_SoftResetBinaryDelayEstimatorFarend(
BinaryDelayEstimatorFarend* self, int delay_shift) {
int abs_shift = abs(delay_shift);
int shift_size = 0;
int dest_index = 0;
int src_index = 0;
int padding_index = 0;
assert(self != NULL);
shift_size = self->history_size - abs_shift;
assert(shift_size > 0);
if (delay_shift == 0) {
return;
} else if (delay_shift > 0) {
dest_index = abs_shift;
} else if (delay_shift < 0) {
src_index = abs_shift;
padding_index = shift_size;
}
// Shift and zero pad buffers.
memmove(&self->binary_far_history[dest_index],
&self->binary_far_history[src_index],
sizeof(*self->binary_far_history) * shift_size);
memset(&self->binary_far_history[padding_index], 0,
sizeof(*self->binary_far_history) * abs_shift);
memmove(&self->far_bit_counts[dest_index],
&self->far_bit_counts[src_index],
sizeof(*self->far_bit_counts) * shift_size);
memset(&self->far_bit_counts[padding_index], 0,
sizeof(*self->far_bit_counts) * abs_shift);
}
void WebRtc_AddBinaryFarSpectrum(BinaryDelayEstimatorFarend* handle,
uint32_t binary_far_spectrum) {
assert(handle != NULL);
// Shift binary spectrum history and insert current |binary_far_spectrum|.
memmove(&(handle->binary_far_history[1]), &(handle->binary_far_history[0]),
(handle->history_size - 1) * sizeof(uint32_t));
handle->binary_far_history[0] = binary_far_spectrum;
// Shift history of far-end binary spectrum bit counts and insert bit count
// of current |binary_far_spectrum|.
memmove(&(handle->far_bit_counts[1]), &(handle->far_bit_counts[0]),
(handle->history_size - 1) * sizeof(int));
handle->far_bit_counts[0] = BitCount(binary_far_spectrum);
}
void WebRtc_FreeBinaryDelayEstimator(BinaryDelayEstimator* self) {
if (self == NULL) {
return;
}
free(self->mean_bit_counts);
self->mean_bit_counts = NULL;
free(self->bit_counts);
self->bit_counts = NULL;
free(self->binary_near_history);
self->binary_near_history = NULL;
free(self->histogram);
self->histogram = NULL;
// BinaryDelayEstimator does not have ownership of |farend|, hence we do not
// free the memory here. That should be handled separately by the user.
self->farend = NULL;
free(self);
}
BinaryDelayEstimator* WebRtc_CreateBinaryDelayEstimator(
BinaryDelayEstimatorFarend* farend, int max_lookahead) {
BinaryDelayEstimator* self = NULL;
if ((farend != NULL) && (max_lookahead >= 0)) {
// Sanity conditions fulfilled.
self = malloc(sizeof(BinaryDelayEstimator));
}
if (self == NULL) {
return NULL;
}
self->farend = farend;
self->near_history_size = max_lookahead + 1;
self->history_size = 0;
self->robust_validation_enabled = 0; // Disabled by default.
self->allowed_offset = 0;
self->lookahead = max_lookahead;
// Allocate memory for spectrum and history buffers.
self->mean_bit_counts = NULL;
self->bit_counts = NULL;
self->histogram = NULL;
self->binary_near_history =
malloc((max_lookahead + 1) * sizeof(*self->binary_near_history));
if (self->binary_near_history == NULL ||
WebRtc_AllocateHistoryBufferMemory(self, farend->history_size) == 0) {
WebRtc_FreeBinaryDelayEstimator(self);
self = NULL;
}
return self;
}
int WebRtc_AllocateHistoryBufferMemory(BinaryDelayEstimator* self,
int history_size) {
BinaryDelayEstimatorFarend* far = self->farend;
// (Re-)Allocate memory for spectrum and history buffers.
if (history_size != far->history_size) {
// Only update far-end buffers if we need.
history_size = WebRtc_AllocateFarendBufferMemory(far, history_size);
}
// 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;
}