Like in module-loopback a Kalman filter is used to reduce noise. Since it
strongly depends on the validity of the underlying model, the average
square of the time factor derivative is used as a measure of convergence
of the time factor.
---
src/modules/alsa/alsa-sink.c | 36 +++++++++++++++++++++++++++++++++---
src/modules/alsa/alsa-source.c | 30 +++++++++++++++++++++++++++++-
2 files changed, 62 insertions(+), 4 deletions(-)
diff --git a/src/modules/alsa/alsa-sink.c b/src/modules/alsa/alsa-sink.c
index de51710..37544e0 100644
--- a/src/modules/alsa/alsa-sink.c
+++ b/src/modules/alsa/alsa-sink.c
@@ -148,6 +148,10 @@ struct userdata {
double drift_filter;
double drift_filter_1;
double time_factor;
+ double time_factor_variance;
+
+ double kalman_variance;
+ double time_variance;
bool first_delayed;
bool usb_hack;
@@ -866,6 +870,9 @@ static void init_time_estimate(struct userdata *u) {
u->first_delayed = false;
u->usb_hack = false;
u->time_offset = 0;
+ u->time_factor_variance = 10000.0;
+ u->kalman_variance = 10000000.0;
+ u->time_variance = 100000.0;
/* Check if this is an USB device, see alsa-util.c
* USB devices unfortunately need some special handling */
@@ -891,7 +898,7 @@ static void update_time_estimate(struct userdata *u) {
double byte_count, iteration_time;
int err;
double time_delta_system, time_delta_card, drift, filter_constant, filter_constant_1;
- double temp;
+ double temp, filtered_time_delta_card, expected_time_delta_card;
pa_usec_t time_stamp;
snd_pcm_status_t *status;
@@ -957,6 +964,19 @@ static void update_time_estimate(struct userdata *u) {
* pa_bytes_to_usec() here because u->start_pos need not
* be on a sample boundary */
time_delta_card = byte_count / u->frame_size / u->sink->sample_spec.rate * PA_USEC_PER_SEC;
+ filtered_time_delta_card = time_delta_card;
+
+ /* Prediction of measurement */
+ expected_time_delta_card = time_delta_system * u->time_factor;
+
+ /* Filtered variance of card time measurements */
+ u->time_variance = 0.9 * u->time_variance + 0.1 * (time_delta_card - expected_time_delta_card) * (time_delta_card - expected_time_delta_card);
+
+ /* Kalman filter, will only be used when the time factor has converged good enough */
+ if (u->time_factor_variance < 100) {
+ filtered_time_delta_card = (time_delta_card * u->kalman_variance + expected_time_delta_card * u->time_variance) / (u->kalman_variance + u->time_variance);
+ u->kalman_variance = u->kalman_variance * u->time_variance / (u->kalman_variance + u->time_variance) + u->time_variance / 4 + 500;
+ }
/* This is a horrible hack which is necessary because USB devices seem to fix up
* the reported delay by some millisecondsconds shortly after startup. This is
@@ -967,13 +987,16 @@ static void update_time_estimate(struct userdata *u) {
* avoid false triggers. When run as batch device, the threshold for the hack must
* be lower than for timer based scheduling. */
if (u->usb_hack && time_delta_system < 5 * PA_USEC_PER_SEC) {
- if (abs(time_delta_system - time_delta_card / u->time_factor) > u->hack_threshold) {
+ if (abs(time_delta_system - filtered_time_delta_card / u->time_factor) > u->hack_threshold) {
/* Recalculate initial conditions */
temp = time_stamp - time_delta_card - u->start_time;
u->start_time += temp;
u->first_start_time += temp;
u->time_offset = -temp;
+ /* Reset time factor variance */
+ u->time_factor_variance = 10000;
+
pa_log_debug("USB Hack, start time corrected by %0.2f usec", temp);
u->usb_hack = false;
return;
@@ -984,8 +1007,11 @@ static void update_time_estimate(struct userdata *u) {
filter_constant = iteration_time / (iteration_time + 318310.0);
filter_constant_1 = iteration_time / (iteration_time + 2387324.0);
+ /* Temporarily save the current time factor */
+ temp = u->time_factor;
+
/* Calculate geometric series */
- drift = (u->drift_filter_1 + 1.0) * (1.5 - time_delta_card / time_delta_system);
+ drift = (u->drift_filter_1 + 1.0) * (1.5 - filtered_time_delta_card / time_delta_system);
/* 2nd order lowpass */
u->drift_filter = (1 - filter_constant) * u->drift_filter + filter_constant * drift;
@@ -994,6 +1020,10 @@ static void update_time_estimate(struct userdata *u) {
/* Calculate time conversion factor, filter again */
u->time_factor = (1 - filter_constant_1) * u->time_factor + filter_constant_1 * (u->drift_filter_1 + 3) / (u->drift_filter_1 + 1) / 2;
+ /* Filtered variance of time factor derivative, used as measure for the convergence of the time factor */
+ temp = (u->time_factor - temp) / iteration_time * 10000000000000;
+ u->time_factor_variance = (1 - filter_constant_1) * u->time_factor_variance + filter_constant_1 * temp * temp;
+
/* Save current system time */
u->last_time = time_stamp;
}
diff --git a/src/modules/alsa/alsa-source.c b/src/modules/alsa/alsa-source.c
index c3dd75d..ce2e79a 100644
--- a/src/modules/alsa/alsa-source.c
+++ b/src/modules/alsa/alsa-source.c
@@ -133,6 +133,10 @@ struct userdata {
double drift_filter;
double drift_filter_1;
double time_factor;
+ double time_factor_variance;
+
+ double kalman_variance;
+ double time_variance;
bool first_delayed;
@@ -776,6 +780,9 @@ static void init_time_estimate(struct userdata *u) {
u->time_factor = 1.0;
u->start_pos = 0;
u->first_delayed = true;
+ u->time_factor_variance = 10000.0;
+ u->kalman_variance = 10000000.0;
+ u->time_variance = 100000.0;
/* Start times */
u->start_time = pa_rtclock_now();
@@ -787,6 +794,7 @@ static void update_time_estimate(struct userdata *u) {
snd_pcm_sframes_t delay = 0;
double byte_count, iteration_time;
double time_delta_system, time_delta_card, drift, filter_constant, filter_constant_1;
+ double temp, filtered_time_delta_card, expected_time_delta_card;
pa_usec_t time_stamp;
int err;
snd_pcm_status_t *status;
@@ -842,13 +850,29 @@ static void update_time_estimate(struct userdata *u) {
* pa_bytes_to_usec() here because u->start_pos need not
* be on a sample boundary */
time_delta_card = byte_count / u->frame_size / u->source->sample_spec.rate * PA_USEC_PER_SEC;
+ filtered_time_delta_card = time_delta_card;
+
+ /* Prediction of measurement */
+ expected_time_delta_card = time_delta_system * u->time_factor;
+
+ /* Filtered variance of card time measurements */
+ u->time_variance = 0.9 * u->time_variance + 0.1 * (time_delta_card - expected_time_delta_card) * (time_delta_card - expected_time_delta_card);
+
+ /* Kalman filter, it will only be used when the time factor has converged good enough */
+ if (u->time_factor_variance < 100) {
+ filtered_time_delta_card = (time_delta_card * u->kalman_variance + expected_time_delta_card * u->time_variance) / (u->kalman_variance + u->time_variance);
+ u->kalman_variance = u->kalman_variance * u->time_variance / (u->kalman_variance + u->time_variance) + u->time_variance / 4 + 500;
+ }
/* Parameter for lowpass filter with 2s and 15s time constant */
filter_constant = iteration_time / (iteration_time + 318310.0);
filter_constant_1 = iteration_time / (iteration_time + 2387324.0);
+ /* Temporarily save current time conversion factor */
+ temp = u->time_factor;
+
/* Calculate geometric series */
- drift = (u->drift_filter_1 + 1.0) * (1.5 - time_delta_card / time_delta_system);
+ drift = (u->drift_filter_1 + 1.0) * (1.5 - filtered_time_delta_card / time_delta_system);
/* 2nd order lowpass */
u->drift_filter = (1 - filter_constant) * u->drift_filter + filter_constant * drift;
@@ -857,6 +881,10 @@ static void update_time_estimate(struct userdata *u) {
/* Calculate time conversion factor, filter again */
u->time_factor = (1 - filter_constant_1) * u->time_factor + filter_constant_1 * (u->drift_filter_1 + 3) / (u->drift_filter_1 + 1) / 2;
+ /* Filtered variance of time factor derivative, used as a measure for the convergence of the time factor */
+ temp = (u->time_factor - temp) / iteration_time * 10000000000000;
+ u->time_factor_variance = (1 - filter_constant_1) * u->time_factor_variance + filter_constant_1 * temp * temp;
+
/* Save current system time */
u->last_time = time_stamp;
}
--
2.8.1
