automated detection of cerulean warbler songs using xbat

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 pre-

screening 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.


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 Start End Total time

(hh:mm) Location CERW

present?

ARU09-A 06/21 06/25 20:30 State Gamelands 86, Warren County YES

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 7:20 Rt 116 Shelterwood, Allegheny NF YES

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 ?


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.


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.


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.


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


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 CERW

present Detections/h Total

detections

True detections

(PPV) False

detections BTBW

detections

ARU09-A YES 3 67 49 18 0

(73%)

ARU09-B ? 14 507 0 507 496

(0%)

ARU09-C NO 1 37 0 37 0

(0%)

ARU18-A YES 53 1243 485 758 732

(39%)

ARU18-B YES 22 163 125 38 27

(77%)

ARU18-C ? 12 321 0 321 316

(0%)

ARU18-D NO 2 76 0 76 4

(0%)

ARU20-A YES 97 1003 975 28 4

(97%)

ARU20-B ? 1 29 0 29 21

(0%)

ARU20-C ? 2 26 0 26 0

(0%)

ARU20-D ? 4 124 0 124 0

(0%)


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 = Chestnut-sided 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).


Figure 5. Examples of Cerulean Warbler and Black-throated Blue Warbler songs detected by the data template detector. Upper: Cerulean Warbler. Lower: Black-throated 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.


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 --

TOTAL 50 144 31 22%

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).


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, Black-

throated Blues accounted for 82% of all false detections across all deployments. Cerulean and Black-

throated 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:


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 Black-

throated 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 Black-

throated 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., Black-

throated 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 Black-

throated 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


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 time-

of-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


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.


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automated detection of cerulean warbler songs using xbat

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