Technical Report 08-02
Automated detection of
Cerulean Warbler songs using
XBAT data template detector software
Preliminary Report
9 April 2008
Prepared for:
US Forest Service
San Dimas Technology & Development Center
Inventory & Monitoring Program
By
Russell A. Charif
Michael Pitzrick
Bioacoustics Research Program
Cornell Laboratory of Ornithology
Cornell University
159 Sapsucker Woods Rd.
Ithaca, NY 14850
Contents
SUMMARY................................................................................................................................ 1
BACKGROUND ........................................................................................................................... 2
METHODS ................................................................................................................................ 3
Recording ............................................................................................................................................. 3
Analysis ................................................................................................................................................ 4
The data template detector .............................................................................................................. 4
Selection of candidate templates ..................................................................................................... 4
Detector configuration...................................................................................................................... 5
Verification of detector results ......................................................................................................... 5
Estimating detector sensitivity ......................................................................................................... 6
RESULTS .................................................................................................................................. 6
Processing speed ................................................................................................................................ 6
Detector performance: positive predictive value and sources of false detections .................... 7
Detector performance: sensitivity ................................................................................................. 10
DISCUSSION ............................................................................................................................ 12
CONCLUSION ........................................................................................................................... 14
AUTOMATED ACOUSTIC METHODS FOR TARGETED SPECIES SURVEYS:
FUTURE PROSPECTS.............................. 14
ACKNOWLEDGEMENTS ............................................................................................................... 15
LITERATURE CITED .................................................................................................................... 16
Summary
The standard approach to monitoring populations of breeding songbirds relies on point counts in
which a skilled observer records species of birds detected at a sampling point during a short (typically
3 – 10 minute) count interval. The number of points that can be sampled is limited in a season by (a)
the availability of personnel with the necessary field identification skills, (b) the short daily period of
activity when birds are easily detectable, and (c) the number of days in the year on which surveys can
be performed. In some habitats and for some species, the majority of birds detected are heard but not
seen. In such cases, it may be possible to expand the scope of survey efforts by using programmable
audio recorders deployed for one to several days at a site, with presence or absence of species
determined by subsequent expert review of recordings. In some cases, this review may be
substantially accelerated by the use of automatic signal detection software.
In order to investigate the feasibility of such methods with surveys targeted at a single species, a field
test was undertaken during the summer of 2007 in and around the Allegheny National Forest, where
a population of Cerulean Warblers (Dendroica caerulescens) has been monitored regularly since 2003.
Programmable digital audio recorders were deployed at eleven sites during June and July. Four sites
were on or adjacent to known Cerulean Warbler breeding territories; two were later determined to
be unoccupied; the remaining five were in habitat suitable for Ceruleans, but where the actual
presence or absence of the birds was unknown. A total of 251.9 hours of sound was recorded.
All recordings were scanned by data template detector software that compared the recording against
a set of six template recordings of known Cerulean Warbler songs from an archive collection. The
software scanned the data at a rate 12.8 times real-time. Putative Cerulean Warbler songs were
detected at all eleven sites. Expert review of all detected events confirmed successful detections of
Cerulean Warbler songs at the four sites where the birds were known to be present. Review of
detections and daily samples from the other seven sites failed to find any Cerulean Warbler songs,
suggesting that these sites were unoccupied by Cerulean Warbler. The detection software reported
false detections at all sites, in addition to the true detections at the four occupied sites. The majority
(82%) of all false detections were songs of Black-throated Blue Warblers, which are very similar to
Cerulean Warbler songs. Hourly detection rates for three of the four occupied sites exceeded 20
events/hour; all seven apparently unoccupied sites had detection rates below 15 events/hour.
An expert analyst exhaustively examined one five-minute sample each day from all sites, noting all
Cerulean Warbler songs detected. At the four occupied sites, the detector found a total of 22% of all
Cerulean songs found by the analyst. The percent of sounds found (the detector’s sensitivity) at each
site varied between 0% (one site only) and 51%.
Several approaches are discussed that could reduce the data template detector’s rate of false detection
without compromising sensitivity. A number of more sophisticated approaches to detection and
classification of songs also hold promise but have not been implemented.
The results suggest that the use of programmable digital recorders and automated signal detection
software may provide an effective approach to expanding the scope of targeted surveys for some
species of concern, particularly when surveys are limited by the availability of skilled field personnel
during a brief daily window within a short breeding season. Automated detection software is not a
substitute for review of recordings by trained experts, but can greatly accelerate the review process.
Automated detection of Cerulean Warbler songs
2
Background
The USDA Forest Service (USFS, San Dimas Technology & Development Center, Inventory &
Monitoring Program) is interested in exploring the utility of automated acoustic monitoring of bird
populations using programmable digital audio recording devices and automated software analysis of
the resulting recordings. These methods have the potential to significantly reduce the costs of some
bird population monitoring efforts, and/or expand the scope of such efforts with a given budget.
During the summer of 2007, the Bioacoustics Research Program (BRP) at the Cornell Lab of
Ornithology (CLO) and USFS collaborated on a pilot project to investigate the effectiveness of such
methods for monitoring populations of Cerulean Warbler (Dendroica cerulea), a forest songbird
species of conservation concern.
Traditional methods of monitoring songbird populations during the breeding season rely on point
counts, in which an observer notes the species, and in some cases the numbers, of all birds heard or
seen at a site during a short sampling period (typically 3 – 10 minutes). In forested habitats, the
majority of individual bird detections are acoustic; many of the birds noted du
ring a typical point count are never seen. Depending on the particular application, point counts may
be intended to survey all species present at a site, or may be focused on one or a few target species of
special concern.
Point counts aimed at detecting songbirds are typically performed during a 4 – 6 hour period
beginning around dawn. After this time many species become less active and more difficult to detect.
At most sites in North America, the season during which point counts can be undertaken is typically
4 to 8 weeks long, depending on latitude and species of interest. Point counts require the ability of
the field worker to reliably identify all species likely to occur at a given site, both by sight and sound.
In some cases, the availability of field personnel with the required identification skills may limit the
number of sites that can be surveyed during the short seasonal and daily period when reliable counts
can be conducted. One approach to alleviating these constraints is to employ non-specialist personnel
to deploy programmable acoustic recording devices at numerous sites to collect recordings that can be
reviewed by experts at a later time. In cases where the objective is to determine the presence of one
or a few target species at a site, existing software can greatly accelerate this review process by prescreening the recordings to detect sounds resembling those of the target species. Skilled experts then
need to review only those sounds detected by the program.
In June 2007, Cornell BRP provided three autonomous recording units to USFS for deployment at
sites in the Allegheny National Forest where Cerulean Warblers are known or suspected to breed.
The Cerulean Warbler is a Neotropical migrant songbird that breeds in mature deciduous forests of
the eastern United States (Hamel 2000). Cerulean Warbler populations have been declining over
much of the species range in recent years, prompting several states to list the species as threatened or
endangered (Rosenberg et al. 2000; Rich 2004). USFS has been monitoring Cerulean Warblers in and
around Allegheny National Forest since 2003.
This preliminary report summarizes the results of initial efforts to use existing software tools to detect
songs of Cerulean Warblers in these long-term recordings based on archived training data. A revised,
final report is planned that will describe improvements in detector performance from using local
training data extracted from the ARU recordings themselves.
Automated detection of Cerulean Warbler songs
3
Methods
Recording
Three autonomous recording units (ARUs) were provided by BRP to USFS personnel for deployment
in and around Allegheny National Forest in Pennsylvania in June and July 2007. ARUs are
programmable battery-operated digital audio recorders developed by BRP. Each ARU contains a
microprocessor, 12-bit analog-to-digital converter, an omnidirectional microphone, preamplifier and
signal conditioning circuitry, and a hard disk for storing audio data. These components are packaged
into a water resistant cylindrical polyvinyl chloride housing, that can easily be attached to a tree
trunk or post driven into the ground, and powered by an external battery pack.
Each ARU was programmed to record for five hours each morning beginning at 06:00 Eastern
Daylight Time. Each unit was deployed at three or four different sites in succession, with each
deployment lasting two to seven days. In all, eleven sites were sampled. Four deployments were at
sites where Cerulean Warblers were known to be present. The remaining sites were in habitat
suitable for Ceruleans, but where the presence or absence of Ceruleans was not known from previous
work at the start of deployments. Two of these sites were later surveyed with the standard field
protocol, and no Ceruleans were found there (S. Stoleson, pers. comm via email 9 April 2008). Table 1
summarizes the dates, locations, time recorded, and whether Ceruleans were known to be present for
each recording site.
At the conclusion of the field season, the ARUs were returned to Cornell, where the recorded audio
data were extracted and archived for analysis.
Table 1. Recording dates, durations, locations, and known presence of Cerulean Warblers for
11 recording sites. All dates are in 2007.
Deployment
Total time
(hh:mm) Location
CERW
present?
Start
End
ARU09-A
06/21
06/25
20:30 State Gamelands 86, Warren County
ARU09-B
06/29
07/06
35:45 Forest Rd 488 in Allegheny NF
ARU09-C
07/11
07/17
28:50 Forest Rd 149 near Ludlow
NO
ARU18-A
06/14
06/19
23:35 Bug Hollow Adjacent Site, Allegheny NF
YES
ARU18-B
06/19
06/21
ARU18-C
06/21
06/26
27:35 Forest Rd 449E in Allegheny NF
ARU18-D
07/11
07/16
30:25 Forest Rd 160 in Warren
NO
ARU20-A
06/18
06/20
10:20 Wolf Run, Allegheny NF
YES
ARU20-B
06/21
06/26
26:25 Forest Rd 558, Allegheny NF
?
ARU20-C
07/06
07/08
13:15 Forest Rd 256 and 256a nr Cherry Grove
?
ARU20-D
07/11
07/17
27:55 Forest Rd 364 near Ludlow
?
7:20 Rt 116 Shelterwood, Allegheny NF
YES
?
YES
?
Automated detection of Cerulean Warbler songs
4
Analysis
The data template detector
To find songs of Cerulean Warblers in the ARU recordings, we used automatic signal detection and
interactive sound visualization tools provided by the XBAT software system (eXtensible BioAcoustic
Tool, http://xbat.org). We used XBAT’s data template detector to detect acoustic events that are
similar to known songs of Cerulean Warbler. The data template detector scans the recorded sound
stream and compares it to a set of templates, sounds known to be from the target species. The
detector quantifies acoustic similarity by spectrogram cross-correlation, and logs all events for which
the correlation value exceeds a specified threshold. The results of such a scan are stored in an XBAT
event log.
Selection of candidate templates
Template sounds for Cerulean Warbler were obtained from the Lab of Ornithology’s Macaulay
Library (ML), the world’s largest archive of recorded animal sounds. Macaulay Library has 62
recordings of Cerulean Warbler, recorded in seven states between 1951 and 2001. Of these, 49
recordings contained a total of 693 songs potentially usable as templates.
An automated template ranking program was used to compare all of the available templates within
each recording and rank them based on their similarity to each other as measured by spectrogram
cross-correlation. This ranking procedure identifies the template that has the highest average
correlation with all other templates. This template is identified by the program as the ―best‖ template,
most likely to detect all of the remaining candidate templates. The algorithm then identifies the
template that it deems most likely to detect templates that would be missed by the first template, and
so on. In this way, a manageable number of templates can be selected that will have the best chance
of detecting the target sounds. (Using very large numbers of templates requires prohibitive
computation time and yields excessive numbers of false detections.)
Once the best template song from each recording was identified, the ranking algorithm was run
again, on the 49 best templates (one from each recording). The six best templates from this aggregate
collection were then used to scan the recorded ARU data.
Figure 1 shows sound spectrograms of the six Cerulean Warbler songs used as templates.
Automated detection of Cerulean Warbler songs
5
Figure 1. Sound spectrograms of six Cerulean Warbler songs used as detector
templates. (a) New York, 1998; (b) New York, 1996; (c) West Virginia, 1996; (d)
Maryland, 1997; (e) Maryland, 1997; (f) New York, 1964.
Detector configuration
We configured the data template detector to use the following spectrogram parameters: FFT size =
256 points, data window overlap = 50%, window function = Hann. We used a correlation threshold of
0.27, selected after some exploratory testing on archived Cerulean Warbler recordings from the
Macaulay Library.
Verification of detector results
To determine what percentage of all detections were true detections (the detector’s positive
predictive value, PPV) we reviewed all putative Cerulean Warbler detections logged by the data
template detector. Each detection was classified by an analyst as either true or false. Whenever
possible, the actual source of each false detection was logged by the analyst, in order to permit later
analysis of false detection errors. To review detections, the analyst used XBAT to display a
spectrogram of the recording at a suitable time scale (typically 15 seconds). XBAT’s Event palette was
used to display a list of all events logged by the detector, and to rapidly navigate from one event to
the next in the spectrogram display (Figure 2). In many cases, the source of the event could be
determined just based on visual inspection of the spectrogram. In other cases, the analyst needed to
listen to the event as well. Once the source of the event was determined, the analyst entered an
annotation into the log indicating the known or suspected source of the sound.
Automated detection of Cerulean Warbler songs
6
Estimating detector sensitivity
In order to estimate what percentage of recorded Cerulean Warbler songs were found by the detector
(detector sensitivity), we examined the period between 08:00 and 08:05 EDT for each day of
recording. For each 5-minute sample, we logged the number of Cerulean Warbler songs that a human
analyst could detect, and the number of these that were flagged by the data template detector. Initial
examination of these samples was by visual inspection of 15-second spectrograms. If the source of a
sound was not clear by visual inspection, the sound was identified aurally.
Figure 2. Screenshot showing the XBAT spectrogram display window and the Event
palette. The sounds outlined in green on the spectrogram are Cerulean Warbler songs
that were detected by the data template detector. The spectrogram shows 20 seconds
of data, slightly more than is typically displayed when reviewing detection logs. The
Event palette provides search, sort, and navigation facilities for rapidly moving among
detected events.
Results
Processing speed
The data template detector required a total of 19.8 hours to process the 251.9 hours of recorded
sound, or an average processing speed of 12.8 times real-time, on a computer with two Intel Xeon
5160 (dual-core) processors running at 3.00 GHz, with 2 GB of RAM under Windows XP.
Automated detection of Cerulean Warbler songs
7
While reviewing detection results in this preliminary analysis, we attempted to identify the source of
all false detections (see below), which resulted in a much slower review process than would be
required to simply classify each detection as true (CERW) or false. In many cases, this required
stopping to listen to detections that could not be confidently assigned to species by visual
examination (e.g. faint signals from distant sources or signals badly smeared by reverberation). In
general, however, for simple binary (true/false) review of detection results, rates of 400 – 1200 events
per hour are attainable, with the exact rate depending primarily on what proportion of events are
reviewed aurally, and what percentage the user chooses to annotate.
Detector performance: positive predictive value and sources of false detections
Putative Cerulean Warbler songs were detected at all sites. Detection rates at three out of four known
Cerulean sites exceeded 20 events/hour (median = 37). At all sites where Ceruleans were not known
to be present (and where manual sampling of recordings ultimately failed to find any Cerulean songs;
see below), detection rates were below 15 events/hour (median = 2; Figure 3).
Figure 3. Putative Cerulean Warbler detections per hour at sites where territorial male
Ceruleans were known or not known to be present. Rates shown are for all detections,
irrespective of true source.
Confirmed Cerulean Warbler songs were detected at all of the sites known to have territorial
Ceruleans present, and at none of the sites where Ceruleans were apparently absent or where
Automated detection of Cerulean Warbler songs
8
territory status was unknown. False detections occurred at all sites. At known Cerulean sites, true
detections (PPV) constituted from 39% to 97% of all detections; the PPV exceeded 70% at three out
of four sites. Table 2 shows the total number of events detected and the number of true and false
detections for each deployment. Overall PPV for the four occupied sites was 66%.
Table 2. Putative Cerulean Warbler song detections using six template songs from Macaulay
Library. PPV = positive predictive value, the percentage of all detections that are true. Shaded
rows represent deployments where Cerulean Warblers were known to be present. BTBW
detections are false detections from Black-throated Blue Warbler.
Deployment
ARU09-A
CERW
present
YES
Detections/h
3
Total
detections
67
True
detections
(PPV)
49
False
detections
BTBW
detections
18
0
507
496
37
0
758
732
38
27
321
316
76
4
28
4
29
21
26
0
124
0
(73%)
ARU09-B
?
14
507
0
(0%)
ARU09-C
NO
1
37
0
(0%)
ARU18-A
YES
53
1243
485
(39%)
ARU18-B
YES
22
163
125
(77%)
ARU18-C
?
12
321
0
(0%)
ARU18-D
NO
2
76
0
(0%)
ARU20-A
YES
97
1003
975
(97%)
ARU20-B
?
1
29
0
(0%)
ARU20-C
?
2
26
0
(0%)
ARU20-D
?
4
124
0
(0%)
Automated detection of Cerulean Warbler songs
Most false detections were sounds from a variety of other bird species, with Black-throated Blue
Warbler (D. caerulescens) responsible for 82% of all false detections (Table 2, Figure 4). Figure 5
shows a true Cerulean Warbler song and a Black-throated Blue Warbler Song (both of which were
found by the detector), for comparison.
Figure 4. Numbers of false detections by species, across all deployments. BTBW =
Black-throated Blue Warbler (Dendroica caerulescens), COYE = Common Yellowthroat
(Geothlypis trichas), MAWA = Magnolia Warbler (D. magnolia), BTGW = Black-throated
Green Warbler (D. virens), DEJU = Dark-eyed Junco (Junco hyemalis), CSWA = Chestnutsided Warbler (D. pensylnanica), EATO = Eastern Towhee (Pipilo erythrophthalmus),
HOWA = Hooded Warbler (Wilsonia citrina), INBU = Indigo Bunting (Passerina cyanea),
SOSP = Song Sparrow (Melospiza melodia), NOPA = Northern Parula (Parula americana),
AMRE = American Redstart (Setophaga ruticilla).
9
Automated detection of Cerulean Warbler songs
10
Figure 5. Examples of Cerulean Warbler and Black-throated Blue Warbler songs
detected by the data template detector. Upper: Cerulean Warbler. Lower: Blackthroated Blue Warbler.
Detector performance: sensitivity
Table 3 shows the results of the detector sensitivity sampling. The analyst found Cerulean songs on
all four of the deployments where Ceruleans were known to be present, and none on any deployment
where Ceruleans were apparently absent or where their presence was unknown. Overall, the detector
found 22% of the songs detected by the analyst. Estimated sensitivity within individual deployments
varied from 0% to 51%. Although the detector found none of the 27 songs found by the analyst in the
four samples from deployment ARU09-A, the resulting estimate of 0% sensitivity is obviously an
underestimate, since overall the detector did find 49 true detections on this deployment (Table 2).
The analyst noted that the songs found in daily samples from this recording were all relatively faint,
apparently from one or more distant birds.
Automated detection of Cerulean Warbler songs
11
Table 3. Estimated sensitivity of data template detector. Each sample was five minutes long,
beginning at 08:00 EDT. ‘CERW songs’ is the number of songs detected by an expert analyst.
‘Detections’ is the number of true CERW songs logged by the detector. Shaded rows represent
deployments where Cerulean Warblers were known to be present.
Deployment
CERW
present
Samples
CERW
songs
Detections
Estimated
Sensitivity
ARU09-A
YES
4
27
0
0%
ARU09-B
?
7
0
0
--
ARU09-C
NO
6
0
0
--
ARU18-A
YES
5
67
11
16%
ARU18-B
YES
2
13
1
8%
ARU18-C
?
5
0
0
--
ARU18-D
NO
6
0
0
--
ARU20-A
YES
2
37
19
51%
ARU20-B
?
5
0
0
--
ARU20-C
?
2
0
0
--
ARU20-D
?
6
0
0
--
50
144
31
22%
TOTAL
In some cases, the detector was able to find Cerulean songs even when they were overlapped by one
or more other species, making them difficult for a human to recognize either visually or aurally
(Figure 6).
Automated detection of Cerulean Warbler songs
12
Figure 6. A Cerulean Warbler (CERW) song that was correctly detected despite
overlapping a song of a Chestnut-sided Warbler (CSWA). Green rectangle shows XBAT’s
detection event. Green shaded area outlines entire CERW song. Red shaded areas
outline CSWA songs. The first CSWA song is from a distant individual; the second one is
from a nearby individual.
Discussion
XBAT’s data template detector found putative Cerulean Warbler songs in recordings from all
deployments, irrespective of whether Ceruleans were known to be present, or were found by an
analyst in daily five-minute samples. Detection rates tended to be much higher in recordings where
Cerulean sounds were actually present; the small number of sites with known Ceruleans present
(four) precludes statistical significance testing of this trend.
In three out of four recordings where Ceruleans were present, more than 70% of detections were in
fact Cerulean Warbler songs. In the one deployment with a lower positive predictive value (39%), all
of the 732 false detections were songs of one species, Black-throated Blue Warbler. Overall, Blackthroated Blues accounted for 82% of all false detections across all deployments. Cerulean and Blackthroated Blue Warbler songs are similar to each other and can be challenging even for humans to
distinguish.
These data demonstrate that positive predictive value (PPV) of a detector or classifier—the
proportion of events that are classified correctly—is not an intrinsic property of the algorithm, but
rather varies for a given algorithm with the particular mix of positive and negative events in the set of
data being processed. Thus, in the data reviewed here, the PPV of the data template detector is low at
sites where a large number of Black-throated Blue Warbler songs occurred, and high where few
Black-throated Blue songs were found.
Several approaches are available that should enable us to reduce or mitigate the rate of false
detections:
Automated detection of Cerulean Warbler songs
13
Rejection templates. The data template detector has the ability to run rejection templates in
parallel with target templates. When one or more rejection templates are specified, XBAT compares
all potential event detections to both the target and rejection templates. If an event is more similar
to a rejection template than it is to any of the target templates, the event is rejected and not logged.
For this preliminary analysis, we have not explored the use of rejection templates for Blackthroated Blue Warblers. However, from past experience, we know that the use of such an approach
can reduce false detection rates by an order of magnitude. The effectiveness of rejection templates
with any given data set depends on the variability of the signal class to be rejected, and its
similarity to the target signals. The option to use rejection templates is already implemented in the
software, and would require no additional development work.
Improved target template selection. The six templates used in this analysis were chosen by a
template ranking approach that took into account only the similarities among a set of known
Cerulean Warbler songs. Armed with the knowledge we now have about the problem of high false
detection rates when Black-throated Blue Warblers are present, adjustments could be made to the
set of templates, so as to eliminate or replace any that are particularly prone to detecting Blackthroated Blues. More generally, it should not be difficult to develop a modified template ranking
algorithm that penalizes candidate templates based on their similarity to a known ―impostor‖ signal
(e.g., Black-throated Blue in this case).
Post-detection classification. A variety of techniques are available that should enable us to classify
events once they are detected as either true or false detections, based on extracted measurements.
XBAT already has a large number of measurements available that can easily be exported to
statistical programs that could be used to explore various classification approaches. Implementation
of a successful classifier that could distinguish between true Cerulean songs and others (e.g., Blackthroated Blue) would require some additional development work.
Because the data template detector relies on cross-correlation of spectrogram images of templates and
unknown data, the performance of the detector (both in terms of PPV and sensitivity—the
percentage of all songs present that are found) can be affected by changes in spectrogram parameters.
For this preliminary analysis, we chose spectrogram parameters subjectively based on past experience.
More in-depth exploration of other combinations of parameters might yield better performance than
reported here. Changes in the correlation threshold used might also improve performance.
Because songs of most oscine songbirds vary geographically and individually, the performance of the
data template detector can be improved in terms of both sensitivity and PPV by incorporating more
local song templates into the training data. If a survey is being done in a new area (as in the present
case), such data may not be available at the outset. If surveys are to be done repeatedly in a given
area, performance can be improved in subsequent iterations by incorporating templates extracted
from prior surveys. No such site-specific templates were used for this preliminary analysis. Given the
absence of such local templates, the performance of the data template detector shown here is
encouraging. Further analysis using local templates to detect Cerulean songs and reject Blackthroated Blue songs is planned, and will be described in a future final report on this project.
The signal detection approach used here (template matching by spectrogram cross-correlation)
represents a simple initial attempt at finding sounds of a target species in a recording. Other, more
Automated detection of Cerulean Warbler songs
14
sophisticated approaches to signal detection and classification presently being explored are likely to
yield improved performance.
Conclusion
Automated acoustic methods for targeted species surveys: future prospects
Several previous studies have explored the utility of acoustic recordings for monitoring bird
populations. Haselmyer and Quinn (2000) compared sound recordings made with a directional
microphone to traditional point counts for estimating bird species richness in a tropical forest
environment. Hobson et al. (2002) and Rempel et al. (2005) compared recordings made with an
omnidirectional microphone to point counts in boreal forest habitat. Acevedo and Villanueva-Rivera
(2006) used a system with an omnidirectional microphone that was programmed to record short
hourly samples for 24 hours to monitor birds and amphibians in Puerto Rican wetland forest. In all of
these studies the goal was to compile a list of all species present at a site, via a complete aural review
of the recordings by expert listeners. All three reported high degrees of concordance between species
lists compiled in the field and in review of recordings. Acevedo and Villanueva-Rivera (2006)
suggested that automated processing by pattern-recognition software could greatly reduce the time
required to review recordings. However, reliable detection and classification of large numbers of bird
species in omnidirectional field recordings remains an elusive goal because of high variability of
species-specific sounds, poor signal-to-noise ratios, reverberation, and overlapping signals. Even
without fully automated processing, such recordings have advantages over ―live‖ field counts in that
they constitute a permanent objective record of all acoustically active species present at a site that can
be reviewed and carefully analyzed multiple times if necessary. Such documentation may be
particularly useful in situations where management decisions may depend on the results.
The present study focused on the less challenging task of automated detection of sounds from a single
target species. The results suggest that such an approach may provide an effective way of expanding
the scope of targeted surveys for some species of acoustically active animals. In particular, studies that
seek to determine the simple presence or absence of one or a few species at a large number of sites
could benefit from this approach, especially when traditional methods (e.g., point counts for songbird
species) are limited by the availability of skilled field observers, a short season of activity, and/or a
short daily period of high detectability for the target species. Autonomous recording devices can be
deployed at any time of day by personnel with no specialized skills or biological knowledge. Thus,
limited availability of experts skilled in field identification during a short season would not constrain
the number of sites that could be monitored.
Once data are recorded, experts can review the recordings at any time, free of the seasonal and timeof-day constraints that limit field counts. By pre-screening recordings, automated processing software
can accelerate the rate at which data can be reviewed by hundreds or thousands of times, enabling
more intensive monitoring than is practical in the field. For example, standard field survey protocols
for monitoring Cerulean Warbler populations at Allegheny National Forest call for a 10-minute
survey sample at each site. These surveys sometimes fail to detect Ceruleans even at sites where the
birds are known to be present (S. Stoleson, pers. comm. via email 31 Mar 2008). Longer or more
samples would presumably have a higher probability of detecting birds present at each site, but would
Automated detection of Cerulean Warbler songs
15
reduce the number of sites that can be sampled in a season. By using autonomous recorders and
automated detection software, more intensive sampling could become practical. USFS biologists at
Allegheny National Forest conduct an average of 16 10-minute point counts per day during the
Cerulean Warbler survey season, for a total of 2.7 hours of data hours/day. In one hour of reviewing
detector results, an expert could ―visit‖ between 6 and 60 sites for one hour each (instead of 10
minutes)1, for a total of 360 to 3,600 data hours. If we assume that the expert spends four hours per
day reviewing detections, this translates into 1,440 to 14,440 hours data hours/day, an increase of 540
to 5,440 times the amount of data ―reviewed‖ in one day in the field.2 Although the numbers given
here do not include time spent handling the digital audio data, and configuring and running
detectors, those activities are much less time-consuming than reviewing detector results.
In most situations, automated sound processing software will not replace expert review of recordings.
However, by directing expert attention to events that are potential signals of interest, such software
can accelerate the review process sufficiently to make this approach practical.
Acknowledgements
This research was funded by USFS via cooperative agreement # 06-CA-11138150-034 with Cornell
University. Field work was performed by Scott Stoleson, Jeanne Hickey, Linda Ordiway, Emily
Thomas, and Don Watts. ARUs were developed by Tom Calupca and Rob MacCurdy and built by
Chris Tessaglia-Hymes and Tom Fowler. XBAT and the data template detector were developed by
Harold Figueroa. The template ranking algorithm was developed by Kurt Fristrup and Kathryn
Cortopassi.
1
These figures assume that the expert inspects every event, and that detection rates are between 5 and 50
events/hour.
2
For sites where the target species is detected, the review rates would be even higher, because not all detections
need to be reviewed for simple presence/absence monitoring—once the target is found, the remaining
detections need not be examined.
Automated detection of Cerulean Warbler songs
16
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