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<html>
<head>
<LINK href="../../../pamHelpStylesheet.css" type="text/css"
rel="STYLESHEET">
<title>Click Detector</title>
</head>
<body>
<h1 id="click-train-detector">Click Train Detector</h1>
<h2 id="overview">Overview</h2>
<p>When a toothed whale, bat or other echolocator uses echolocation
for hunting or sensing their surroundings they usually produce regular
clicks/calls which vary slowly in inter-click/call-interval,
amplitude, bearing etc. Individual click detections can be difficult
to classify from other random transients because recieved waveforms
and spectra are distorted by number of factors, such as narrow beam
profiles, frequency dependent absorption, propogation effects and
animal behaviour. The broadband clicks of many dolphins psecies are
especially difficult to distinguish because they are very similar to
many other sources of transient noise, such as cavitations from ship
propellors. However, the echolocation clicks used by toothed whales
(and bats) are not produced in isolation - animals tend to rapidly
produce clicks with a slowly varying inter-click-interval (ICI); there
are very few non-biological sources which produce regular repetitive
sound and so this provides an additional contextual dimension for
click classification. An automated algorithm which is based on
identifying repeating patterns of sounds therefore has the potential
to be significantly more accurate than an algorithm based on
identifying individual calls.</p>
<p>The PAMGuard click train detector module is used to detect and
then classify repeating patterns of clicks. It is designed to work
with multiple types of acoustic data, from CPOD detections to single
channel and multi-channel hydrophone recordings.</p>
<h2 id="how-it-works">How it works</h2>
<p>PAMGuard's click train detector utilises both a detection and
classification stage to extract click trains from recordings.</p>
<p>The detection stage is currently based on a multi hypothesis
tracking (MHT) algorithm. This algorithm considers all possible
combinations of transient detections creating a large hypothesis
matrix which holds potential click trains. As more clicks are added to
the hypothesis matrix it grows exponentially and so, to prevent a
computer running out memory, it is regularly <em>pruned</em> to keep only
the most likely click trains over time. The assigned likelihood of a
click train is based on number of properties which can be defined in
by the user. For example, a user might select, ICI, Amplitude and
Correlation as variables to score click trains; this would mean that
combinations of clicks with slowly changing ICI, amplitude and
waveforms would be favoured by the algorithm and stay in the
hypothesis matrix. Other properties such as bearing, click length and
peak frequency can also be selected. A graphical explanation of the
click train detection algorithm is shown in Figure 1 and a more
detailed explanation of the be found in Macaulay (2019).</p>
<p align="center">
<img width="930" height="900" src="resources/mht_diagram.png">
</p>
<p>
<em>Diagram demonstrating how the click train algorithm works.
Black dots are a set of 14 detected clicks at times t1 to t14. The
click train algorithm begins at click 1 and creates two possible
clicks trains, one that includes the first click (filled circle) and
the other in which the click is not part of the click train
(non-filled circle). The algorithm then moves to the next click and
adds it to the hypothesis matrix. As the number of clicks increases,
the hypothesis matrix exponentially expands in size and must be
pruned. After a minimum of Npmin clicks (in this case 4) each track
hypothesis (possible click train) is assigned a &Chi;<sup>2</sup> score.
The track hypothesis with lowest score (defined by larger coloured
circles) has it's branch traced back Np (in this case 3) clicks.
Any track hypothesis which do not include the click Np steps back are
pruned (defined by the double lines). Clicks which share no click
associations with the first track hypothesis are then pruned and the
process repeats until all clicks are part of a track or a maximum
number of tracks have been considered (in this example there are two
tracks). The algorithm then moves to the next click, adds it to the
hypothesis matrix, assigns &Chi;<sup>2</sup> scores and traces the
lowest &Chi;<sup>2</sup> branch Np steps back, pruning the hypothesis
matrix again; the process repeats until the last click. Note that
there is always a track hypothesis with no associated clicks (i.e.
the bottom-most branch where no clicks belong to a click train). If a
track hypothesis is confirmed and thus removed from the hypothesis
matrix, then this track can be used to start another click train
</em>
</p>
<p>The advantage of this MHT approach is that the click train
detection module is quite general and can cope with a large variety of
complex situations and multiple overlapping click trains. The
disadvantage is that there are a large number of potential variables
which can be set that affect the performance of the detector which can
make it complex to initially set up.</p>
<p>The subsequent classification stage attempts to classify
detected click trains to species. Classification is currently based on
a series of relatively simple binary classification steps but there is
scope for machine learning approaches in future versions. The binary
classification is based on parameters such as number of detected
clicks, the mean and standard deviation in ICI and bearing and the
correlation of the average spectrum of the click train with a
predefined spectral template.</p>
<p>A click train which has been both detected and classified is
saved to PAMGuard's database and can be reclassified in PAMGuard's
viewer mode.</p>
<h2 id="configuring-the-click-train-detector">Configuring the
click train detector</h2>
<p>The primary settings to configure can be split into MHT Kernel
and &Chi;<sup>2</sup> settings, these are all set in the primary click train
detector dialog as shown in Figure 2.</p>
<p align="center">
<img width="850" height="700" src="resources/detection_pane.png">
</p>
<p>
<em>The settings pane of the click train detector.</em>
</p>
<h3 id="mht-kernel-settings">MHT Kernel Settings</h3>
<p>The MHT Kernel is the part of the detection algorithm which
creates and then prunes the large hypothesis matrix which keeps a copy
of all possible click trains. MHT Kernel settings are therefore
important because they influence speed (a larger number of possible
click trains in memory is more processor intensive) and the quality of
the detections (the larger the number of possibilities the more likely
that <em>true</em> click trains are contained in the hypothesis matrix).
The specific settings are;</p>
<p>
<strong><em>Prune-back</em></strong>: The hypothesis matrix needs
pruned so that it does not grow exponentially and cause memory issues.
The matrix is pruned at Np (see Figure 1) previous detections i.e. if
Np is 5 then then then the algorithm selects the most likely click
train, moves back five detections back and discards other hypothesis
that do not contain the combination of clicks in that branch. Thus,
increasing the prune-back means that more hypothesis are kept at any
one time but decreasing will lead to faster processing times as less
combination are kept in memory.
</p>
<p>
<strong><em>Prune-start</em></strong>: The initial number of
detections before the pruning process starts. This cannot be less than
Prune-back and should generally should be set no more than 15 for 8GB
of memory.
</p>
<p>
<strong><em>Max no. coasts</em></strong>: A click train is saved and
removed the hypothesis mix once it has passed a number of tests. It
must be over three clicks long, survive the pruning process and have
missed the max no. coasts. A coast is when a click has been missed
from a click train based on ICI. i.e. if the ICI is 2s and a click
train goes for 6s without a detected click then there have been 3
coasts. Increasing the maximum number of coasts means that click
trains are less fragmented but can come at the cost of keeping click
trains in the hypothesis matrix for longer which have ended.
</p>
<p>
<strong><em>Max no. trains</em></strong>: This is a maximum allowed
number of trains in the hypothesis mix. Note this refers to the number
of trains which can survive pruning - the actual number of potential
click trains in the hypothesis mix will be much larger. Generally,
just via pruning, the hypothesis matrix will keep itself below the max
no. trains, however, in certain situations it can grow too large and
requires a limit. The max no. trains therefore usually have little
effect on results but should generally be set to less than 50 to
ensure smooth processing
</p>
<h3 id="-sup-2-sup-settings">
&Chi;<sup>2</sup> Settings
</h3>
<p>
&Chi;<sup>2</sup> is a measure of the likelihood that a click train is
from a (usually) biological source. The higher the &Chi;<sup>2</sup>
value the lower the quality of a click train.
</p>
<p>
The &Chi;<sup>2</sup> model used in the click train detector considers
both the slowly varying properties of click trains, as well as bonus
and penalty factors to discourage fragmentation and aliasing
(selecting a multiple of the true ICI) of detected click trains.
</p>
<p>The initial basis of the model is:</p>
<p align="center">
<img width="550" height="100" src="resources/mht_equation.png">
</p>
<p>
where <em>m</em> is the number of selected descriptors, e.g. ICI,
amplitude, bearing etc., and <em>y(i,k)</em> is the measurement of
descriptor <em>i</em> for click <em>k</em> in a click train with n
associated clicks. <em>t(k+1)</em> is the measured time of a click <em>k</em>.
Each descriptor is divided by q<sub>i</sub> which is a user tuneable
parameter that alters the importance each descriptor has on the total
&Chi;<sup>2</sup>. Ideally it should correspond to a prediction of the
likely variance of the descriptor.
</p>
<p>
The descriptors can be enabled and the variance set in the &Chi;<sup>2</sup>
Settings pane. The toggle button next to each descriptor sets whether
a descriptor is used to score a click train and the variance is then
set using the slider or by inputting manually by clicking the settings
cog. Increasing the variance means that the descriptor has less of an
influence on the calculation of &Chi;<sup>2</sup> and decreasing means
that the descriptor has a larger influence on &Chi;<sup>2</sup>. In some
cases, clicks can be so close together that the variance is tiny and
thus &Chi;<sup>2</sup> becomes huge e.g. during buzzes. A minimum
variance value (<em>qt<sub>i</sub></em>) prevents the variance <em>(max¡(q<sub>i</sub>
(t<sub>(k+1)</sub>-t<sub>k</sub> ),qt<sub>i</sub> )<sup>2</sup>)
</em> from falling below very low values.
</p>
<p>Ideally the variance for each parameter would be calculated from
a test dataset of manually annotated click trains e.g. by calculating
the variance of ICI of all marked click trains.</p>
<p align="center">
<img width="900" height="120" src="resources/varience_pane.png">
</p>
<p>
<em>Each descriptor has a variance setting which can be changed
by moving the slider or manually inputting data by clicking the
settings button. Variance is multiplied by the ICI for each click
detection because clicks closer together in time the descriptor
values will change less. In some cases, clicks can be so close
together that the variance is tiny and thus &Chi;<sup>2</sup> in Eq. 1
becomes huge e.g. during buzzes. A Min. Error prevents the variance
from falling below very low values.
</em>
</p>
<p>The available descriptors parameters can be set in the click
detector settings pane (Figure 3) and works as follows;</p>
<p>
<strong><em>IDI:</em></strong> the inter-detection-interval in
milliseconds. The algorithm looks for slowly changes in the interval
between detections.
</p>
<p>
<strong><em>Amplitude:</em></strong> the amplitude in dB re 1&mu;Pa pp.
The algorithm looks for slowly changing amplitude values. Note that
the algorithm is comparing the change in change in amplitude so that
the click train algorithm is not biased against large but consistent
changes in amplitude (e.g. due to a narrow beam profile sweeping
across a hydrophone).
</p>
<p>
<strong><em>Bearing:</em></strong> the bearing of multi-channel clicks
in degrees. Slowly changing bearings will increase the likelihood that
click trains are detected. Note that in a similar way to Amplitude,
the change in change in bearing is considered so that the algorithm is
not biased against large but consistent changes in bearings. The
bearing parameter has some additional settings which apply a large
penalty to clicks trains if there is a large (user-defined) jump in
bearing.
</p>
<p>
<strong><em>Correlation:</em></strong> the algorithm calculates the
peak of the cross-correlation value of subsequent clicks and looks for
slowly changing values in the cross-correlation value. This tells the
click train algorithm to search for clicks with consistent/slowly
changing spectra. The correlation descriptor also has some additional
settings which allow the user to pre-filter waveforms before
cross-correlation. This is especially useful in removing noise from
higher frequency detections.
</p>
<p>
<strong><em>Time Delays:</em></strong> the time delay between
multi-channel clicks in milliseconds. The algorithm looks for slowly
changing values in the time delays between multichannel clicks. This
is useful for arrays with more than two hydrophones where an error in
a single time delay measurement may cause a substantial error in
bearing. Like amplitude and bearing, the time delay values are the
change in change in time delays between subsequent clicks to ensure
that click trains are not biased against faster changes in bearing.
</p>
<p>
<strong><em>Click Length:</em></strong> the length of the saved
waveform of a click in milliseconds. This is a crude measure of the
length of a click; however, it can be useful in helping the algorithm
distinguish between species with long multi-modal clicks such as sperm
whales, and much shorter broadband clicks such as dolphins.
</p>
<p>
<strong><em>Peak Frequency:</em></strong> the peak frequency in Hz.
The peak frequency between subsequent clicks is used score click
trains. This is useful for click trains with very stable peak
frequencies such as echosounders, narrow band high frequency species
and perhaps some beaked whale species.
</p>
<h3 id="advanced-sup-2-sup-settings">
Advanced &Chi;<sup>2</sup> Settings
</h3>
<p>The descriptors used in Eq. 1 on their own do not provide a good
score for click train detections. This is because Eq.1 can achieve the
same score by either skipping clicks e.g. every second click in a
click train, or by splitting click trains into smaller fragments.</p>
<p align="center">
<img width="500" height="350" src="resources/advanced_pane.png">
</p>
<p>
<em>The advanced settings for calculating &Chi;^2. These parameters
are primarily used to prevent click train aliasing and fragmentation.
The advanced settings (see Figure 4) are a series of additional
factors that prevent aliasing and fragmentation and work as flows.</em>
</p>
<p>
<strong><em>Low ICI Bonus:</em></strong> if the median ICI of the
possible click train is above a specified maximum value, a large
penalty is added which effectively makes it one of the least likely
click trains in the hypothesis matrix. If the median ICI is below the
maximum value then &Chi;<sup>2</sup> = (&Chi;<sup>2</sup> (I/(max<sub>k</sub><em>I<sub>k</sub>))<sup>LI</sup>
where I is the median ICI, max<sub>k</sub>
</em>I<sub>k</sub> is the maximum ICI in the possible click train and LI is
the low ICI Bonus constant term. This bonus term favours lower ICI
values, preventing aliased click trains.
</p>
<p>
<strong><em>Long track bonus:</em></strong> add a bonus factor for
longer click trains to prevent fragmentation. This is the total length
of the click train in seconds divided by the total hypothesis matrix
time in seconds L which is then multiplied so that &Chi;<sup>2</sup> =
(&Chi;<sup>2</sup>*L)<sup>LT</sup> where LT is the long track bonus.
</p>
<p>
<strong><em>Coast penalty:</em></strong> add a penalty for
'coasting' i.e. when an expected click, based on ICI, is not
present in the click train. This penalty is multiplied by the number
of coasts i.e. the likely number of missed clicks based on ICI
</p>
<p>
<strong><em>New Track Penalty:</em></strong> if a track hypothesis is
newly added in the hypothesis matrix, then add a minor penalty factor.
This is added until the number of click trains exceeds No. New Track
Clicks
</p>
<h2 id="classification">Classification</h2>
<p>The classification process attempts to assign a species
identification to each detected click trains. Currently there is only
one implemented classifier, a simple binary classifier which tests
user defined parameters (e.g. IDI, bearing, spectral correlation and
classifies each click). Classification parameters are accessed via the
classification tab in the settings dialog.</p>
<p>There is currently a basic spectral correlation/IDI/bearing
classifier; more complex classifiers can be implemented in the future.
</p>
<p align="center">
<img width="510" height="800" src="resources/classifier_pane.png">
</p>
<p>
<em>The classifier settings. Users can add multiple classifiers
using the + button next to the classifier tabs. Each classifier
allows the user to choose a number of different approaches to
classification based on the goodness of fit, inter-click interval,
average spectra and bearings of the click trains. Users can use just
one or all of these options and set specific parameters for each.</em>
</p>
<p>Users can add multiple classifiers by selecting the + button
next to the classification tabs. Each classifier allows the user to
choose a number of different methods for click train classification
based on the goodness of fit, inter-click interval, average spectra
and/or bearings of the click trains; for a click train to be
classified it must pass all enabled methods (use toggle switches to
enable and disable different types of classification). The different
classification methods.</p>
<h3 id="-sup-2-sup-threshold-classifier">
&Chi;<sup>2</sup> threshold classifier
</h3>
<p>
The click train is classified if it's overall &Chi;<sup>2</sup> value
is lower than the set &Chi;<sup>2</sup> Threshold and it has more than
Min. Clicks and the time between the first and last click is greater
than Min. Time
</p>
<h3 id="idi-classifier">IDI Classifier</h3>
<p>The click train is classified if the median/mean and standard
deviation in the inter detection interval (IDI) between subsequent
clicks are within user defined limits.</p>
<h3 id="spectrum-template-classifier">Spectrum Template Classifier</h3>
<p>The click train is classified if the average spectra of the
click train has a correlation value above Spectrum Correlation
Threshold with a user defined spectral template. The template can be
set using the button on the top right of the spectrum plot – a
default spectrum can be loaded or a spectrum can be loaded from a .mat
or .csv file. A csv file should have the first row as the spectrum and
first column of the second row the sample rate. A .mat file should be
a single saved structure with sR (sample rate) and spectrum (array of
spectrum values) fields.</p>
<h3 id="bearing-classifier">Bearing Classifier</h3>
<p>The click train is classified if minimum and maximum bearing
(Bearing Limits) the average change in bearing (&deg; Bearing Mean), the
median change in bearing (&deg; Bearing Median) and/or the average
standard deviation in bearing change (&deg; Bearing Std) are within user
defined limits.</p>
<h2 id="parametrising-the-classifier">Parametrising the classifier</h2>
<p>Each classifier has a set of metadata that are added to click
trains. This can be accessed through the tooltip or right click menus
in various displays. For example, in the Time Base Display FX hover
the mouse over a click train or bring the pop menu with a right click.
Parameters such as the spectral correlation value, IDI and bearing
information etc are displayed which allows users to get an idea of
which values to set for the classifier. Currently this requires (like
most PAMGuard classifiers) a trial and error approach. It is hoped
that future update will allow manually validated data to be used to
parametrise both the detection and classification stage of the click
train detector.</p>
<p align="center">
<img width="700" height="500" src="resources/rightclickmenu.png">
</p>
<p>
<em>The metadata associated with each classifier is stored with
every click train and be accessed through right clicking on or
hovering the mouse over a click train detection.</em>
</p>
<h2 id="localisation">Localisation</h2>
<p>The click train detector can be used to localise the position of
animals detected by the click train detector using target motion
analysis. This generally means that the localisation capabilities are
generally restricted to data which has been collected using towed
hydrophone arrays.</p>
<p align="center">
<img width="242" height="430" src="resources/localisation1.png">
</p>
<p>
<em>Screenshot of the click train localisation settings.
Currently, only target motion is supported.</em>
</p>
<p>Localisation is enabled by ticking Localise click trains. The
type of localisation algorithm which is used is selected in the
Localisation algorithms (See the localisation section in PAMGuard help
for more info on localisation algorithms). Localisation using 3D
simplex and MCMC can be processor intensive, especially when there are
a large number of clicks in a train and so the Algorithm Limits pane
can be used to set a maximum number of input clicks for a
localisation. If the maximum is exceeded then clicks are sub sampled
from the click train evenly in time.</p>
<p>Generally, target motion localisation only works well when there
are a large number of clicks over a long time period. The Filters tab
allows users to select which click trains are localised and also to
remove spurious results from unsuccessful localisations. The Pre
Localisation Filter allows users to select a minimum number of
detections before localisations are attempted and a minimum bearing
change in the click train (Min Angle range). Click trains with larger
angle ranges will generally result in higher quality localisations.</p>
<p align="center">
<img width="242" height="430" src="resources/localisation2.png">
</p>
<p>
<em>The filter tab allows users to pre-filter which click train
are localised.</em>
</p>
<p>
The Results Filter allows for spurious localisation results to be
deleted: any results from target motion localisation (which can have
more than one possible localisation) which are further away than
Maximum Range, shallower than Minimum Depth or deeper than Maximum
Depth are discarded.<br>Running The click train detector can be
run in real time or post processing. In real time add the module and
it will automatically detected click trains once PAMGuard started.
</p>
<p align="center">
<img width="200" height="300" src="resources/offlineprocessing.png">
</p>
<p>
<em>The filter tab allows users to pre-filter which click train
are localised.</em>
</p>
<p>In viewer mode, add the module and then go to Settings&gt;Click
Train Detector &gt; Reanalyse click trains.This will bring up
PAMGuard's generic data reprocessing dialog with two settings, Click
Train Detector or Click Train Classifier. The Click Train Detector
option will run the detection and classification algorithm again. The
Click Train Classifier will only run the classification algorithm on
existing detected click trains (much faster). Note that users can
select how much data to reprocess in the Data dropdown menu. All
Data means the entire dataset will be reprocessed, Loaded Data means
just the current data loaded in the display (all scrollable data),
Select Data allows the user to define two time limits between which
all data is reprocessed.</p>
<h2 id="visualising-results">Visualising Results</h2>
<p>The results from the click train detector can be visualised in a
variety of displays in PAMGuard.</p>
<h3 id="click-bearing-time-display">Click bearing time display</h3>
<p>By default, clicks trains will be shown in the Click Detector
Module's in built bearing time display. Different click trains are
represented as different colours. Note that you must right click on
the display and select Colour by Click Trains</p>
<p align="center">
<img width="940" height="500" src="resources/clicktrain_BT.png">
</p>
<p>
<em>The results of the click train detector displayed on the
bearing time display. Different colours correspond to different click
trains.</em>
</p>
<h3 id="time-display-fx">Time Display FX</h3>
<p>The Time Display FX is a more modern display which allows any
time-based data to be plotted together on a large variety of y-axis
(e.g., frequency, bearing, amplitude etc.). Click trains will be
plotted on the time-based display by adding Click detections to the
display and then using the right</p>
<p align="center">
<img width="940" height="500" src="resources/clicktrain_TDFX.png">
</p>
<p>
<em>Click train data displayed in the time display FX. Users can
right click on click trains to view average spectra and waterfall
spectrograms (shown here in top right).</em>
</p>
<p CLASS="previousLink">
<a href=""></a>
</p>
<p CLASS="nextLink">
<a href="ClickDetector_clickDetectorDisplays.html">Next: Click
Detector Displays </a>
</p>
<br>
<br>
</body>
</html>

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src/pamguard/Pamguard.java
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@ -135,7 +135,7 @@ public class Pamguard {
Thread folderSizeThread = new Thread(folderSizeMon);
folderSizeThread.start();
TimeZone.setDefault(PamCalendar.defaultTimeZone);
// TimeZone.setDefault(PamCalendar.defaultTimeZone);
System.out.println("**********************************************************");
try {