ohun is intended to facilitate the automated detection of sound events, providing functions to diagnose and optimize detection routines. Detections from other software can also be explored and optimized.

The main features of the package are:

• The use of reference annotations for detection optimization and diagnostic
• The use of signal detection theory indices to evaluate detection performance

The package offers functions for:

• Curate references and acoustic data sets
• Diagnose detection performance
• Optimize detection routines based on reference annotations
• Energy-based detection
• Template-based detection

All functions allow the parallelization of tasks, which distributes the tasks among several processors to improve computational efficiency. The package works on sound files in ‘.wav’, ‘.mp3’, ‘.flac’ and ‘.wac’ format.

To install the latest developmental version from github you will need the R package remotes:

# install pacakge
remotes::install_github("maRce10/ohun")

library(ohun)

## Automatic sound event detection

Finding the position of sound events in a sound file is a challenging task. ohun offers two methods for automated sound event detection: template-based and energy-based detection. These methods are better suited for highly stereotyped or good signal-to-noise ratio (SNR) sounds, respectively. If the target sound events don’t fit these requirements, more elaborated methods (i.e. machine learning approaches) are warranted:

Still, a detection run using other software can be optimized with the tools provided in ohun.

## Signal detection theory applied to bioacoustics

Broadly speaking, signal detection theory deals with the process of recovering signals (i.e. target signals) from background noise (not necessarily acoustic noise) and it’s widely used for optimizing this decision making process in the presence of uncertainty. During a detection routine, the detected ‘items’ can be classified into 4 classes:

• True positives (TPs): signals correctly identified as ‘signal’
• False positives (FPs): background noise incorrectly identified as ‘signal’
• False negatives (FNs): signals incorrectly identified as ‘background noise’
• True negatives (TNs): background noise correctly identified as ‘background noise’

Several additional indices derived from these indices are used to evaluate the performance of a detection routine. These are three useful indices in the context of sound event detection included in ohun:

• Recall: correct detections relative to total detections (a.k.a. true positive rate or sensitivity; TPs / (TPs + FNs))
• Precision: correct detections relative to total detections (TPs / (TPs + FPs)).
• F1 score: combines recall and precision as the harmonic mean of these two, so it provides a single value for evaluating performance (a.k.a. F-measure or Dice similarity coefficient).

(Metrics that make use of ‘true negatives’ cannot be easily applied in the context of sound event detection as noise cannot always be partitioned in discrete units)

A perfect detection will have no false positives or false negatives, which will result in both recall and precision equal to 1. However, perfect detection cannot always be reached and some compromise between detecting all target signals plus some noise (recall = 1 & precision < 1) and detecting only target signals but not all of them (recall < 1 & precision = 1) is warranted. The right balance between these two extremes will be given by the relative costs of missing signals and mistaking noise for signals. Hence, these indices provide an useful framework for diagnosing and optimizing the performance of a detection routine.

The package ohun provides a set of tools to evaluate the performance of an sound event detection based on the indices described above. To accomplish this, the result of a detection routine is compared against a reference table containing the time position of all target sound events in the sound files. The package comes with an example reference table containing annotations of long-billed hermit hummingbird songs from two sound files (also supplied as example data: ‘lbh1’ and ‘lbh2’), which can be used to illustrate detection performance evaluation. The example data can be explored as follows:

# load example data
data("lbh1", "lbh2", "lbh_reference")

lbh_reference
 [30mObject of class  [1m'selection_table'
[22m [39m [90m* The output of the following call:
[39m [90m [3mwarbleR::selection_table(X = lbh_reference)
[23m [39m [90m [1m
Contains: [22m
*  A selection table data frame with 19 rows and 6 columns:
[39m [90m|sound.files | selec|  start|    end| bottom.freq| top.freq|
[39m [90m|:-----------|-----:|------:|------:|-----------:|--------:|
[39m [90m|lbh2.wav    |     1| 0.1092| 0.2482|      2.2954|   8.9382|
[39m [90m|lbh2.wav    |     2| 0.6549| 0.7887|      2.2954|   9.0426|
[39m [90m|lbh2.wav    |     3| 1.2658| 1.3856|      2.2606|   9.0774|
[39m [90m|lbh2.wav    |     4| 1.8697| 2.0053|      2.1911|   8.9035|
[39m [90m|lbh2.wav    |     5| 2.4418| 2.5809|      2.1563|   8.6600|
[39m [90m|lbh2.wav    |     6| 3.0368| 3.1689|      2.2259|   8.9382|
[39m [90m... and 13 more row(s)
[39m [90m
* A data frame (check.results) generated by check_sels() (as attribute)
[39m [90mcreated by warbleR 1.1.27 [39m

This is a ‘selection table’, an object class provided by the package warbleR (see selection_table() for details). Selection tables are basically data frames in which the contained information has been double-checked (using warbleR’s check_sels()). But they behave pretty much as data frames and can be easily converted to data frames:

# convert to data frame
as.data.frame(lbh_reference)
   sound.files selec    start       end bottom.freq top.freq
1     lbh2.wav     1 0.109161 0.2482449      2.2954   8.9382
2     lbh2.wav     2 0.654921 0.7887232      2.2954   9.0426
3     lbh2.wav     3 1.265850 1.3855678      2.2606   9.0774
4     lbh2.wav     4 1.869705 2.0052678      2.1911   8.9035
5     lbh2.wav     5 2.441769 2.5808529      2.1563   8.6600
6     lbh2.wav     6 3.036825 3.1688667      2.2259   8.9382
7     lbh2.wav     7 3.628617 3.7465742      2.3302   8.6252
8     lbh2.wav     8 4.153288 4.2818085      2.2954   8.4861
9     lbh2.wav     9 4.723673 4.8609963      2.3650   8.6948
10    lbh1.wav    10 0.088118 0.2360047      1.9824   8.4861
11    lbh1.wav    11 0.572290 0.7201767      2.0520   9.5295
12    lbh1.wav    12 1.056417 1.1972614      2.0868   8.4861
13    lbh1.wav    13 1.711338 1.8680274      1.9824   8.5905
14    lbh1.wav    14 2.190249 2.3416568      2.0520   8.5209
15    lbh1.wav    15 2.697143 2.8538324      1.9824   9.2513
16    lbh1.wav    16 3.181315 3.3344833      1.9129   8.4861
17    lbh1.wav    17 3.663719 3.8133662      1.8781   8.6948
18    lbh1.wav    18 4.140816 4.3045477      1.8433   9.2165
19    lbh1.wav    19 4.626712 4.7851620      1.8085   8.9035

All ohun functions that work with this kind of data can take both selection tables and data frames. Spectrograms with highlighted sound events from a selection table can be plotted with the function label_spectro() (this function only plots one wave object at the time):

# save sound file
writeWave(lbh1, file.path(tempdir(), "lbh1.wav"))

# save sound file
writeWave(lbh2, file.path(tempdir(), "lbh2.wav"))

# print spectrogram
label_spectro(wave = lbh1, reference = lbh_reference[lbh_reference$sound.files == "lbh1.wav", ], hop.size = 10, ovlp = 50, flim = c(1, 10)) # print spectrogram label_spectro(wave = lbh2, reference = lbh_reference[lbh_reference$sound.files == "lbh2.wav", ], hop.size = 10, ovlp = 50, flim = c(1, 10))

The function diagnose_detection() evaluates the performance of a detection routine by comparing it to a reference table. For instance, a perfect detection is given by comparing lbh_reference to itself:

lbh1_reference <-
lbh_reference[lbh_reference$sound.files == "lbh1.wav",] # diagnose diagnose_detection(reference = lbh1_reference, detection = lbh1_reference)[, c(1:3, 7:9)]  total.detections true.positives false.positives overlap.to.true.positives recall 1 10 10 0 1 1 precision 1 1 We will work mostly with a single sound file for convenience but the functions can work on several sound files at the time. The files should be found in a single working directory. Although the above example is a bit silly, it shows the basic diagnostic indices, which include basic detection theory indices (‘true.positives’, ‘false.positives’, ‘false.negatives’, ‘recall’ and ‘precision’) mentioned above. We can play around with the reference table to see how these indices can be used to spot imperfect detection routines (and hopefully improve them!). For instance, we can remove some sound events to see how this is reflected in the diagnostics. Getting rid of some rows in ‘detection’, simulating a detection with some false negatives, will affect the recall but not the precision: # create new table lbh1_detection <- lbh1_reference[3:9,] # print spectrogram label_spectro( wave = lbh1, reference = lbh1_reference, detection = lbh1_detection, hop.size = 10, ovlp = 50, flim = c(1, 10) ) # diagnose diagnose_detection(reference = lbh1_reference, detection = lbh1_detection)[, c(1:3, 7:9)]  total.detections true.positives false.positives overlap.to.true.positives recall 1 7 7 0 1 0.7 precision 1 1 Having some additional sound events not in reference will do the opposite, reducing precision but not recall. We can do this simply by switching the tables: # print spectrogram label_spectro( wave = lbh1, detection = lbh1_reference, reference = lbh1_detection, hop.size = 10, ovlp = 50, flim = c(1, 10) ) # diagnose diagnose_detection(reference = lbh1_detection, detection = lbh1_reference)[, c(1:3, 7:9)]  total.detections true.positives false.positives overlap.to.true.positives recall 1 7 7 3 1 1 precision 1 0.7 The function offers three additional diagnose metrics: • Split positives: target sound events overlapped by more than 1 detecion • Merged positives: number of cases in which 2 or more target sound events in ‘reference’ were overlapped by the same detection • Proportional overlap of true positives: ratio of the time overlap of true positives with its corresponding sound event in the reference table In a perfect detection routine split and merged positives should be 0 while proportional overlap should be 1. We can shift the start of sound events a bit to reflect a detection in which there is some mismatch to the reference table regarding to the time location of sound events: # create new table lbh1_detection <- lbh1_reference # add 'noise' to start set.seed(18) lbh1_detection$start <-
lbh1_detection$start + rnorm(nrow(lbh1_detection), mean = 0, sd = 0.1) ## print spectrogram label_spectro( wave = lbh1, reference = lbh1_reference, detection = lbh1_detection, hop.size = 10, ovlp = 50, flim = c(1, 10) ) # diagnose diagnose_detection(reference = lbh1_reference, detection = lbh1_detection)  total.detections true.positives false.positives false.negatives split.positives 1 10 10 0 0 0 merged.positives overlap.to.true.positives recall precision f1.score 1 0 0.528 1 1 1 In addition, the following diagnostics related to the duration of the sound events can also be returned by setting time.diagnostics = TRUE. Here we tweak the reference and detection data just to have some false positives and false negatives: # diagnose with time diagnostics diagnose_detection(reference = lbh1_reference[-1, ], detection = lbh1_detection[-10, ], time.diagnostics = TRUE)  total.detections true.positives false.positives false.negatives split.positives 1 8 8 1 1 0 merged.positives overlap.to.true.positives mean.duration.true.positives 1 0 0.61125 139 mean.duration.false.positives mean.duration.false.negatives 1 55 158 proportional.duration.true.positives recall precision f1.score 1 1 0.8888889 0.8888889 0.8888889 These additional metrics can be used to further filter out undesired sound events based on their duration (for instance in a energy-based detection as in energy_detector(), explained below). Diagnostics can also be detailed by sound file: # diagnose by sound file diagnostic <- diagnose_detection(reference = lbh1_reference, detection = lbh1_detection, by.sound.file = TRUE) diagnostic  sound.files total.detections true.positives false.positives false.negatives 1 lbh1.wav 10 10 0 0 split.positives merged.positives overlap.to.true.positives recall precision f1.score 1 0 0 0.528 1 1 1 These diagnostics can be summarized (as in the default diagnose_detection() output) with the function summarize_diagnostic(): # summarize summarize_diagnostic(diagnostic)  total.detections true.positives false.positives false.negatives split.positives 1 10 10 0 0 0 merged.positives overlap.to.true.positives recall precision f1.score 1 0 0.528 1 1 1 ## Detecting sound events with ohun ### Energy-based detection This detector uses amplitude envelopes to infer the position of sound events. Amplitude envelopes are representations of the variation in energy through time. The following code plots an amplitude envelope along with the spectrogram for the example data lbh1: # plot spectrogram and envelope label_spectro( wave = cutw( lbh1, from = 0, to = 1.5, output = "Wave" ), ovlp = 90, hop.size = 10, flim = c(0, 10), envelope = TRUE ) This type of detector doesn’t require highly stereotyped sound events, although they work better on high quality recordings in which the amplitude of target sound events is higher than the background noise (i.e. high signal-to-noise ratio). The function ernergy_detector() performs this type of detection. #### How it works We can understand how to use ernergy_detector() using simulated sound events. We will do that using the function simulate_songs() from warbleR. In this example we simulate a recording with 10 sounds with two different frequency ranges and durations: # install this package first if not installed # install.packages("Sim.DiffProc") #Creating vector for duration durs <- rep(c(0.3, 1), 5) #Creating simulated song set.seed(12) simulated_1 <- warbleR::simulate_songs( n = 10, durs = durs, freqs = 5, sig2 = 0.01, gaps = 0.5, harms = 1, bgn = 0.1, path = tempdir(), file.name = "simulated_1", selec.table = TRUE, shape = "cos", fin = 0.3, fout = 0.35, samp.rate = 18 )$wave

The function call saves a ‘.wav’ sound file in a temporary directory (tempdir()) and also returns a wave object in the R environment. This outputs will be used to run energy-based detection and creating plots, respectively. This is how the spectrogram and amplitude envelope of the simulated recording look like:

# plot spectrogram and envelope
label_spectro(wave = simulated_1,
env = TRUE,
fastdisp = TRUE)

Note that the amplitude envelope shows a high signal-to-noise ratio of the sound events, which is ideal for energy-based detection. This can be conducted using ernergy_detector() as follows:

# run detection
detection <-
energy_detector(
files = "simulated_1.wav",
bp = c(2, 8),
threshold = 50,
smooth = 150,
path = tempdir()
)

# plot spectrogram and envelope
label_spectro(
wave = simulated_1,
envelope = TRUE,
detection = detection,
threshold = 50
)

The output is a selection table:

detection
 [30mObject of class  [1m'selection_table'
[22m [39m [90m* The output of the following call:
[39m [90m [3menergy_detector(files = "simulated_1.wav", path = tempdir(),
[23m [39m  [90m [3mbp = c(2, 8), smooth = 150, threshold = 50)
[23m [39m [90m [1m
Contains: [22m
*  A selection table data frame with 10 rows and 5 columns:
[39m [90m|sound.files     | duration| selec|  start|    end|
[39m [90m|:---------------|--------:|-----:|------:|------:|
[39m [90m|simulated_1.wav |   0.2328|     1| 0.5309| 0.7638|
[39m [90m|simulated_1.wav |   0.7947|     2| 1.3955| 2.1901|
[39m [90m|simulated_1.wav |   0.2334|     3| 2.8308| 3.0642|
[39m [90m|simulated_1.wav |   0.7944|     4| 3.6955| 4.4899|
[39m [90m|simulated_1.wav |   0.2333|     5| 5.1307| 5.3641|
[39m [90m|simulated_1.wav |   0.7945|     6| 5.9956| 6.7901|
[39m [90m... and 4 more row(s)
[39m [90m
* A data frame (check.results) generated by check_sels() (as attribute)
[39m [90mcreated by warbleR 1.1.28 [39m

Now we will make use of some additional arguments to filter out specific sound events based on their structural features. For instance we can use the argument minimum.duration to provide a time treshold (in ms) to exclude short sound events and keep only the longest sound events:

# run detection
detection <-
energy_detector(
files = "simulated_1.wav",
bp = c(1, 8),
threshold = 50,
min.duration = 500,
smooth = 150,
path = tempdir()
)

# plot spectrogram
label_spectro(wave = simulated_1, detection = detection)

We can use the argument max.duration (also in ms) to exclude long sound events and keep the short ones:

# run detection
detection <- energy_detector(files = "simulated_1.wav", bp = c(1, 8),  threshold = 50, smooth = 150, max.duration = 500, path = tempdir())

# plot spectrogram
label_spectro(wave = simulated_1,  detection = detection)

We can also focus the detection on specific frequency ranges using the argument bp (bandpass). By setting bp = c(5, 8) only those sound events found within that frequency range (5-8 kHz) will be detected, which excludes sound events below 5 kHz:

# Detecting
detection <- energy_detector(files = "simulated_1.wav", bp = c(5, 8), threshold = 50, smooth = 150, path = tempdir())

# plot spectrogram
label_spectro(wave = simulated_1,  detection = detection)

The same logic can be applied to detect those sound events found below 5 kHz. We just need to set the upper bound of the band pass filter below the range of the higher frequency sound events (for instance bp = (0, 6)):

# Detect
detection <-
energy_detector(
files = "simulated_1.wav",
bp = c(0, 6),
threshold = 50,
min.duration = 1,
smooth = 150,
path = tempdir()
)

# plot spectrogram
label_spectro(wave = simulated_1,  detection = detection)

Amplitude modulation (variation in amplitude across a sound event) can be problematic for detection based on amplitude envelopes. We can also simulate some amplitude modulation using warbleR::simulate_songs():

#Creating simulated song
set.seed(12)

#Creating vector for duration
durs <- rep(c(0.3, 1), 5)

sim_2 <-
sim_songs(
n = 10,
durs = durs,
freqs = 5,
sig2 = 0.01,
gaps = 0.5,
harms = 1,
bgn = 0.1,
path = tempdir(),
file.name = "simulated_2",
selec.table = TRUE,
shape = "cos",
fin = 0.3,
fout = 0.35,
samp.rate = 18,
am.amps = c(1, 2, 3, 2, 0.1, 2, 3, 3, 2, 1)
)

# extract wave object and selection table
simulated_2 <- sim_2$wave sim2_sel_table <- sim_2$selec.table

# plot spectrogram
label_spectro(wave = simulated_2, envelope = TRUE)

When sound events have strong amplitude modulation they can be split during detection:

# detect sounds
detection <- energy_detector(files = "simulated_2.wav", threshold = 50, path = tempdir())

# plot spectrogram
label_spectro(wave = simulated_2, envelope = TRUE, threshold = 50, detection = detection)

There are two arguments that can deal with this: holdtime and smooth. hold.time allows to merge split sound events that are found within a given time range (in ms). This time range should be high enough to merge things belonging to the same sound event but not too high so it merges different sound events. For this example a hold.time of 200 ms can do the trick (we know gaps between sound events are ~0.5 s long):

# detect sounds
detection <-
energy_detector(
files = "simulated_2.wav",
threshold = 50,
min.duration = 1,
path = tempdir(),
hold.time = 200
)

# plot spectrogram
label_spectro(
wave = simulated_2,
envelope = TRUE,
threshold = 50,
detection = detection
)

smooth works by merging the amplitude envelope ‘hills’ of the split sound events themselves. It smooths envelopes by applying a sliding window averaging of amplitude values. It’s given in ms of the window size. A smooth of 350 ms can merged back split sound events from our example:

# detect sounds
detection <-
energy_detector(
files = "simulated_2.wav",
threshold = 50,
min.duration = 1,
path = tempdir(),
smooth = 350
)

# plot spectrogram
label_spectro(
wave = simulated_2,
envelope = TRUE,
threshold = 50,
detection = detection,
smooth = 350
)

The function has some additional arguments for further filtering detections (peak.amplitude) and speeding up analysis (thinning and parallel).

#### Optimizing energy-based detection

This last example using smooth can be used to showcase how the tunning parameters can be optimized. As explained above, to do this we need a reference table that contains the time position of the target sound events. The function optimize_energy_detector() can be used finding the optimal parameter values. We must provide the range of parameter values that will be evaluated:

optim_detection <-
optimize_energy_detector(
reference = sim2_sel_table,
files = "simulated_2.wav",
threshold = 50,
min.duration = 1,
path = tempdir(),
smooth = c(100, 250, 350)
)
3 combinations will be evaluated:
optim_detection[, c(1, 2:5, 7:12, 17:18)]
  threshold peak.amplitude smooth hold.time min.duration thinning total.detections
1        50              0    100         0            1        1               20
2        50              0    250         0            1        1               15
3        50              0    350         0            1        1               10
true.positives false.positives false.negatives split.positives
1             20               0               0              10
2             15               0               0               5
3             10               0               0               0
mean.duration.false.negatives proportional.duration.true.positives
1                            NA                             1.000000
2                            NA                             1.179487
3                            NA                             1.000000

The output contains the combination of parameters used at each iteration as well as the corresponding diagnose indices. In this case all combinations generate a good detection (recall & precision = 1). However, only the routine with the highest smooth (last row) has no split sound events (‘split.positive’ column). It also shows a better overlap to the reference sound events (‘overlap.to.true.positives’ closer to 1).

In addition, there are two complementary functions for optimizing energy-based detection routines: feature_reference() and merge_overlaps(). feature_reference() allow user to get a sense of the time and frequency characteristics of a reference table. This information can be used to determine the range of tuning parameter values during optimization. This is the output of the function applied to lbh_reference:

feature_reference(reference = lbh_reference, path = tempdir())
                  min   mean    max
sel.duration   117.96 142.60 163.73
gap.duration   624.97 680.92 811.61
annotations      9.00   9.50  10.00
duty.cycle       0.24   0.27   0.31
peak.amplitude  73.76  81.58  88.03
bottom.freq      1.81   2.11   2.37
top.freq         8.49   8.82   9.53

Features related to selection duration can be used to set the ‘max.duration’ and ‘min.duration’ values, frequency related features can inform banpass values, gap related features inform hold time values and duty cycle can be used to evaluate performance. Peak amplitude can be used to keep only those sound events with the highest intensity, mostly useful for routines in which only a subset of the target sound events present in the recordings is needed.

merge_overlaps() finds time-overlapping selections in reference tables and collapses them into a single selection. Overlapping selections would more likely appear as a single amplitude ‘hill’ and thus would be detected as a single sound event. So merge_overlaps() can be useful to prepare references in a format representing a more realistic expectation of how a pefect energy detection routine would look like.

### Template-based detection

This detection method is better suited for highly stereotyped sound events. As it doesn’t depend on the signal-to-noise ratio it’s more robust to higher levels of background noise. The procedure is divided in three steps:

• Choosing the right template (get_templates())
• Estimating the cross-correlation scores of templates along sound files (template_correlator())
• Detecting sound events by applying a correlation threshold (template_detector())

The function get_templates() can help you find a template closer to the average acoustic structure of the sound events in a reference table. This is done by finding the sound events closer to the centroid of the acoustic space. When the acoustic space is not supplied (‘acoustic.space’ argument) the function estimates it by measuring several acoustic parameters using the function spectro_analysis() from warbleR) and summarizing it with Principal Component Analysis (after z-transforming parameters). If only 1 template is required the function returns the sound event closest to the acoustic space centroid. The rationale here is that a sound event closest to the average sound event structure is more likely to share structural features with most sounds across the acoustic space than a sound event in the periphery of the space. These ‘mean structure’ templates can be obtained as follows:

# get mean structure template
template <-
get_templates(reference = lbh1_reference, path = tempdir())

The graph above shows the overall acoustic spaces, in which the sound closest to the space centroid is highlighted. The highlighted sound is selected as the template and can be used to detect similar sound events in the example ‘lbh1’ data (but note that get_templates() can return more than 1 template):

# get correlations
correlations <-
template_correlator(templates = template,
files = "lbh1.wav",
path = tempdir())

The output is an object of class ‘template_correlations’, with its own printing method:

# print
correlations

This object can then be used to detect sound events using template_detector():

# run detection
detection <-
template_detector(template.correlations = correlations, threshold = 0.4)

detection
 [30mObject of class  [1m'selection_table'
[22m [39m [90m* The output of the following call:
[39m [90m [3mtemplate_detector(template.correlations = correlations, threshold = 0.4)
[23m [39m [90m [1m
Contains: [22m
*  A selection table data frame with 27 rows and 6 columns:
[39m [90m|sound.files | selec|  start|    end|template    | scores|
[39m [90m|:-----------|-----:|------:|------:|:-----------|------:|
[39m [90m|lbh1.wav    |     1| 0.0931| 0.2498|lbh1.wav-15 | 0.6030|
[39m [90m|lbh1.wav    |     2| 0.1281| 0.2848|lbh1.wav-15 | 0.4416|
[39m [90m|lbh1.wav    |     3| 0.1514| 0.3080|lbh1.wav-15 | 0.4028|
[39m [90m|lbh1.wav    |     4| 0.1746| 0.3313|lbh1.wav-15 | 0.4072|
[39m [90m|lbh1.wav    |     5| 0.5705| 0.7272|lbh1.wav-15 | 0.7269|
[39m [90m|lbh1.wav    |     6| 0.6054| 0.7621|lbh1.wav-15 | 0.4089|
[39m [90m... and 21 more row(s)
[39m [90m
* A data frame (check.results) generated by check_sels() (as attribute)
[39m [90mcreated by warbleR 1.1.28 [39m

The output can be explored by plotting the spectrogram along with the detection and correlation scores:

# plot spectrogram
label_spectro(
wave = lbh1,
detection = detection,
template.correlation = correlations$lbh1.wav-10/lbh1.wav, flim = c(0, 10), threshold = 0.4, hop.size = 10, ovlp = 50) The performance can be evaluated using diagnose_detection(): #diagnose diagnose_detection(reference = lbh1_reference, detection = detection)  total.detections true.positives false.positives false.negatives split.positives 1 27 27 0 0 10 merged.positives overlap.to.true.positives recall precision f1.score 1 0 0.7225926 1 1 1 #### Optimizing template-based detection The function optimize_template_detector() allows to evaluate the performance under different correlation thresholds: # run optimization optimization <- optimize_template_detector( template.correlations = correlations, reference = lbh1_reference, threshold = seq(0.1, 0.5, 0.1) ) 5 thresholds will be evaluated: # print output optimization  threshold templates total.detections true.positives false.positives false.negatives 1 0.1 lbh1.wav-15 47 47 42 0 2 0.2 lbh1.wav-15 43 43 22 0 3 0.3 lbh1.wav-15 39 39 4 0 4 0.4 lbh1.wav-15 27 27 0 0 5 0.5 lbh1.wav-15 10 10 0 0 split.positives merged.positives overlap.to.true.positives recall precision f1.score 1 10 0 0.5168085 1 0.5280899 0.6911765 2 10 0 0.5581395 1 0.6615385 0.7962963 3 10 0 0.6123077 1 0.9069767 0.9512195 4 10 0 0.7225926 1 1.0000000 1.0000000 5 0 0 0.9470000 1 1.0000000 1.0000000 Additional threshold values can be evaluated without having to run it all over again. We just need to supplied the output from the previous run with the argument previous.output (the same trick can be done when optimizing an energy-based detection): # run optimization optimize_template_detector( template.correlations = correlations, reference = lbh1_reference, threshold = c(0.6, 0.7), previous.output = optimization ) 2 thresholds will be evaluated:  threshold templates total.detections true.positives false.positives false.negatives 1 0.1 lbh1.wav-15 47 47 42 0 2 0.2 lbh1.wav-15 43 43 22 0 3 0.3 lbh1.wav-15 39 39 4 0 4 0.4 lbh1.wav-15 27 27 0 0 5 0.5 lbh1.wav-15 10 10 0 0 6 0.6 lbh1.wav-15 9 9 0 1 7 0.7 lbh1.wav-15 2 2 0 8 split.positives merged.positives overlap.to.true.positives recall precision f1.score 1 10 0 0.5168085 1.0 0.5280899 0.6911765 2 10 0 0.5581395 1.0 0.6615385 0.7962963 3 10 0 0.6123077 1.0 0.9069767 0.9512195 4 10 0 0.7225926 1.0 1.0000000 1.0000000 5 0 0 0.9470000 1.0 1.0000000 1.0000000 6 0 0 0.9544444 0.9 1.0000000 0.9473684 7 0 0 0.9850000 0.2 1.0000000 0.3333333 In this case several threshold values can achieved an optimal detection. #### Detecting several templates Several templates can be used within the same call. Here we correlate two templates on the two example sound files, taking one template from each sound file: # get correlations correlations <- template_correlator( templates = lbh_reference[c(1, 10),], files = c("lbh1.wav", "lbh2.wav"), path = tempdir() ) # run detection detection <- template_detector(template.correlations = correlations, threshold = 0.5) correlations <- template_correlator( templates = lbh_reference[c(1, 10),], files = c("lbh1.wav", "lbh2.wav"), path = tempdir() ) Note that in these cases we can get the same sound event detected several times (duplicates), one by each template. We can check if that is the case just by diagnosing the detection: #diagnose diagnose_detection(reference = lbh_reference, detection = detection)  total.detections true.positives false.positives false.negatives split.positives 1 23 23 0 0 4 merged.positives overlap.to.true.positives recall precision f1.score 1 0 0.9573913 1 1 1 Duplicates are shown as split positives. Fortunately, we can leave a single detected sound event by leaving only those with the highest correlation. To do this we first need to label each row in the detection using label_detection() and then remove duplicates using filter_detection(): # labeling detection labeled <- label_detection(reference = lbh_reference, detection = detection) This function adds a column (‘detection.class’) with the class label for each row: table(labeled$detection.class)

true.positive true.positive (split)
15                     8 

Now we can filter out duplicates and diagnose the detection again, telling the function to select a single row per duplicate using the correlation score as a criterium (by = "scores", this column is part of the template_detector() output):

# filter
filtered <- filter_detection(detection = labeled, by = "scores")

# diagnose
diagnose_detection(reference = lbh_reference, detection = filtered)
  total.detections true.positives false.positives false.negatives split.positives
1               19             19               0               0               0
merged.positives overlap.to.true.positives recall precision f1.score
1                0                      0.95      1         1        1

We successfully get rid of duplicates and detected every single target sound event.

### Improving detection speed

Detection routines can take a long time when working with large amounts of acoustic data (e.g. large sound files and/or many sound files). These are some useful points to keep in mine when trying to make a routine more time-efficient:

• Always test procedures on small data subsets
• template_detector() is faster than energy_detector()
• Parallelization (see parallel argument in most functions) can significantly speed-up routines, but works better on Unix-based operating systems (linux and mac OS)
• Sampling rate matters: detecting sound events on low sampling rate files goes faster, so we should avoid having nyquist frequencies (sampling rate / 2) way higher than the highest frequency of the target sound events (sound files can be downsampled using warbleR’s fix_sound_files())
• Large sound files can make the routine crash, use split_acoustic_data() to split both reference tables and files into shorter clips.
• Think about using a computer with lots of RAM memory or a computer cluster for working on large amounts of data
• thinning argument (which reduces the size of the amplitude envelope) can also speed-up energy_detector()

• Use your knowledge about the sound event structure to determine the initial range for the tuning parameters in a detection optimization routine
• If people have a hard time figuring out where a target sound event occurs in a recording, detection algorithms will also have a hard time
• Several templates representing the range of variation in sound event structure can be used to detect semi-stereotyped sound events
• Make sure reference tables contain all target sound events and only the target sound events. The performance of the detection cannot be better than the reference itself.
• Avoid having overlapping sound events or several sound events as a single one (like a multi-syllable vocalization) in the reference table when running an energy-based detector
• Low-precision can be improved by training a classification model (e.g. random forest) to tell sound events from noise

Araya-Salas, M. (2021), ohun: diagnosing and optimizing automated sound event detection. R package version 0.1.0.

### References

1. Araya-Salas, M. (2021), ohun: diagnosing and optimizing automated sound event detection. R package version 0.1.0.
2. Araya-Salas M, Smith-Vidaurre G (2017) warbleR: An R package to streamline analysis of animal sound events. Methods Ecol Evol 8:184-191.
3. Khanna H., Gaunt S.L.L. & McCallum D.A. (1997). Digital spectrographic cross-correlation: tests of sensitivity. Bioacoustics 7(3): 209-234.
4. Knight, E.C., Hannah, K.C., Foley, G.J., Scott, C.D., Brigham, R.M. & Bayne, E. (2017). Recommendations for acoustic recognizer performance assessment with application to five common automated signal recognition programs. Avian Conservation and Ecology,
5. Macmillan, N. A., & Creelman, C.D. (2004). Detection theory: A user’s guide. Psychology press.

Session information

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default

locale:
[1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] ohun_0.1.1         warbleR_1.1.28     NatureSounds_1.0.4 knitr_1.41
[5] seewave_2.2.0      tuneR_1.4.2

loaded via a namespace (and not attached):
[1] Rcpp_1.0.10        class_7.3-20       fftw_1.0-7         rprojroot_2.0.3
[5] digest_0.6.31      utf8_1.2.2         R6_2.5.1           Sim.DiffProc_4.8
[9] signal_0.7-7       evaluate_0.20      e1071_1.7-12       ggplot2_3.4.0
[13] highr_0.10         pillar_1.8.1       rlang_1.0.6        jquerylib_0.1.4
[17] rmarkdown_2.20     pkgdown_2.0.7      textshaping_0.3.6  desc_1.4.2
[21] stringr_1.5.0      igraph_1.3.5       RCurl_1.98-1.9     munsell_0.5.0
[25] proxy_0.4-27       Deriv_4.1.3        compiler_4.2.2     xfun_0.36
[29] pkgconfig_2.0.3    systemfonts_1.0.4  htmltools_0.5.4    tibble_3.1.8
[33] gridExtra_2.3      dtw_1.23-1         fansi_1.0.4        viridisLite_0.4.1
[37] crayon_1.5.2       sf_1.0-9           MASS_7.3-58.1      bitops_1.0-7
[41] grid_4.2.2         jsonlite_1.8.4     gtable_0.3.1       lifecycle_1.0.3
[45] DBI_1.1.3          magrittr_2.0.3     units_0.8-1        scales_1.2.1
[49] KernSmooth_2.23-20 cli_3.6.0          stringi_1.7.12     cachem_1.0.6
[53] pbapply_1.7-0      viridis_0.6.2      fs_1.6.0           bslib_0.4.2
[57] ragg_1.2.5         vctrs_0.5.2        rjson_0.2.21       tools_4.2.2
[61] glue_1.6.2         purrr_1.0.1        parallel_4.2.2     fastmap_1.1.0
[65] yaml_2.3.7         colorspace_2.1-0   classInt_0.4-8     memoise_2.0.1
[69] sass_0.4.5