Bat Echolocation Researc h - Bat Conservation International

ZERO-CROSSINGS ANALYSIS FOR BAT IDENTIFICATION:
AN OVERVIEW
CHRIS CORBEN
In this paper, I present an overview of the effectiveness of Zero-crossings Analysis (ZCA) for the identification of
free-flying bats, based on 17 years of experience using the Anabat system. My opinions are based on intimate
involvement with ZCA, as the original designer of the hardware and software used by Anabat, and also as one
who routinely uses Anabat as a major component of field surveys for bats. I explain how the system works and
how it compares with other approaches to the general problem of making echolocation calls available to humans
for bat identification. I argue that ZCA has numerous advantages over other approaches, especially for passive
monitoring. I also offer my views on a number of general issues pertaining to the use of acoustic monitoring for
bats.
Key words: Anabat, bats, echolocation, Fast Fourier Transform, frequency division, heterodyne, spectral analysis, zero-crossings
Correspondent: corben@hoarybat.com
INTRODUCTION
I became involved with bat acoustics in the mid-1980s
when I saw the chance to combine my passions for electronics
and wildlife in the design of a practical bat-detection
system. I was intrigued by papers about the use of
bat echolocation calls for species identification, but I
found that equipment then available for making use of
ultrasonic signals was too expensive for my budget. I realized
that recent advances in computer hardware would
make it possible to find other solutions. Consequently, I
resolved to develop a less-expensive system, better tailored
to the specific needs of bat acoustic identification.
I designed both hardware and software for a system based
on Zero-Crossings Analysis (ZCA), subsequently developed
as a commercial product called Anabat (Titley Electronics,
Australia, www.titley.com.au). In this paper, I use
the terms Anabat and ZCA interchangeably because
Anabat is the implementation of ZCA with which I am
most familiar, and which is most widely used for monitoring
bats, at least in Australia and North America.
In recent years, my main employment has been to survey
bats for various purposes, and to this end I have made
extensive use of ZCA, as implemented by Anabat. I find
this system to be effective for both field identification of
bats and passive monitoring of bat activity. Using Anabat
has allowed me to treat bats as field-observable animals,
in much the same way as birds or frogs.
For the symposium, I was originally asked to write on
the subject of frequency division, but this is a relatively
minor part of the whole system, so I mostly address ZCA
and its strengths. To do so requires comparing ZCA with
spectral analysis, which is the principal alternative
means of visually displaying bat calls. I argue that ZCA
is a more practical and effective approach to echolocation-call
analysis for species-identification purposes. I
have extensive experience with spectral analysis, mainly
for analyzing frog calls, though I have also used it to
Section 3: Ultrasound Species Identification
analyze vocalizations of birds and bats, and other
sounds. I greatly admire the concept of spectral analysis,
and I think of the Fast Fourier Transform (FFT) as one of
the most elegant mathematical inventions. The sounds
of most animals are best analyzed using spectral analysis,
but for identifying free-flying bats, this approach is technological
overkill because bat echolocation calls are simple
signals well suited to analysis by ZCA. It is therefore
appropriate to make use of the many advantages ZCA
offers in cost and efficiency. In my view, spectral analysis
does not provide clear advantages that offset its practical
liabilities for this purpose.
HOW DO FREQUENCY DIVISION AND ZCA WORK?
One way to determine the frequency of a signal is to
count how many cycles, or vibrations, occur in a given
time interval. This works well when dealing with continuous
signals of constant frequency. However, bat
echolocation call frequencies typically change rapidly,
so another approach is required. Instead of counting
cycles, the duration of each individual cycle can be measured
and the frequency computed from its reciprocal to
produce a graph of frequency against time. This is the
basis of ZCA.
In practice, it is often advantageous to first derive a
second signal, which produces one cycle for each group
of several cycles from the original signal. This is the process
of frequency division. Using it has the advantage
that the frequency-divided signal is downshifted in frequency
by a constant factor, the division ratio, making it
audible to humans. Another advantage is that a frequency-divided
signal averages out small fluctuations, giving
a smoother representation, better suited to the level of
detail needed to see the underlying structure of a bat
call. A lower division ratio displays more points in the
frequency display but at the cost of increased storage
requirements and processing power, and if the extra
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