AnaBat software
AnaBat II software
AnaBat SD1 and CF ZCAIM software
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AnaBat II software
AnaBat II overview
AnaBat is a system designed to help users identify echolocating bats by drawing graphs of their calls on a PC screen. There are three main components to the system, a Bat Detector, a ZCAIM and software.
The Bat Detector is used to produce audible output from the ultrasonic (and therefore generally inaudible) sounds which bats generate in order to echolocate. The Bat Detector usually used for this purpose is the ANABAT II detector, which is optimised for this role, though any detector with a frequency divided or countdown output could be used.
The ZCAIM (Zero-Crossings Analysis Interface Module) is a piece of hardware which interfaces the audio-frequency signal from the Bat Detector to a PC.
The software consists of several quite small programs (see below).
AnaBat II Software and firmware
Software is the suite of applications which the user runs to carry out the functions of operating and maintaining the AnaBat system. Firmware is the internal software which controls the electronics inside the CF Storage ZCAIM and the SD1 detector.
Both software and firmware are occasionally upgraded. More recent versions always contain improvements and bug fixes compared to older versions. The newer versions will be more reliable and more capable than the versions they replace. In addition, it is important to make sure you have the latest versions if ever you encounter any problems with your AnaBat system, otherwise you may be encountering a bug which has already been fixed.
It is important to make sure you keep your software and firmware up to date. The latest versions will always be found at the links below. Be sure to check for updates on a regular basis. Note, however, that you may need to refresh the pages in your browser, because browsers often cache pages locally to make browsing more efficient, but that means the version you see in your browser may not be the latest version.
All the current AnaBat software runs under the Windows operating system, and all (except AnaPocket) will work with any version of Windows from Win 98 onwards. However, for most purposes, the more recent versions of Windows (such as XP) will be preferable, since they are associated with faster machines with higher screen resolution.
AnaBat6 is used to record new data from a ZCAIM, generating AnaBat sequence files which can be stored on a hard disk drive.
The latest version of ANABAT6.COM, used mainly to record new data and save it to AnaBat sequence files. Runs in DOS, but can only be used in raw DOS (without Windows loaded in the background!) if new data are to be recorded using the original, type 2 ZCAIM. The most recent, type 6 ZCAIMs, both standard and mini, can be used to record new data while Windows is running. However, it won't work under Windows NT, 2000 or XP, since these versions don't give DOS programs access to the hardware.
Download abat63g.zip (31k)
Analook is used to view and manipulate AnaBat sequence files for species identification, call parameter measurement and data management.
This is the workhorse program which contains most of the functionality of the AnaBat system. AnalookW allows you to view recordings you have made, either passively or actively, but it does much more than this. It offers an extraordinary level of flexibility in how you view your data, and provides many useful tools and features which help you manage and analyze your data.
ANALOOK.EXE is the program which should be used to view and manipulate AnaBat sequence files. It runs in DOS, but will work under Windows.
Download ALOOK49j.ZIP (101k)
CFCread is the program you use to download data recorded onto a CF card by a CF ZCAIM, whether a stand-alone unit or in an SD1 detector. It is also used to set the clock on the ZCAIM and to query the firmware version and ID of the ZCAIM, and to initialize a CF card so it can be used in a ZCAIM.
Download CFCREAD.ZIP (184k)
AnaPocket is the software used to link a ZCAIM to a Pocket PC. It will only run on a Pocket PC (also called a palmtop or PDA) under Pocket PC 2003 or a later Windows operating system (e.g. Windows Mobile 5).
AnaPocket lets you look at realtime sonograms of bat calls on your PDA, and it also allows you to store recordings, review earlier recordings, manage GPS input and draw simple maps of GPS positions to help you locate yourself along a transect. AnaPocket has brought bat call analysis into the world of the PDA, providing unprecedented portability.
Download ANAPOCKET.ZIP (53k)
PicLoad is the program you need to upload a new version of the firmware (see below) to a CF Storage ZCAIM or SD1 detector. PicLoad carries out the actual uploading procedure, and automatically verifies the upload to ensure it worked properly. There are different models of the CF Storage ZCAIM and the SD1 has quite different firmware, so PicLoad also takes care of checking the device against the firmware being uploaded, to make sure the device cannot be loaded with the wrong firmware.
Download PICLOAD.ZIP (120k)
Firmware is the internal software which operates the later models of ZCAIM, whether in a stand-alone CF Storage ZCAIM or in an SD1 detector. The firmware gives the ZCAIM its intelligence and flexibility, and the ability to update the firmware allows the ZCAIM to adjust to new technologies and to implement improvements and new features.
It is important to ensure your firmware is up-to-date, especially if you are having any difficulties with the hardware. Your first reaction to any problems should always be to update your firmware (using PicLoad) just in case the problem has already been fixed in the latest version. Using the latest firmware also ensures you are on the same page as other users or as anyone trying to help you sort out a problem.
Dataget, Anamusic and Anatimer are utility programs used to assist the user in various tasks.
Dataget is a DOS program which can be used to extract AnaBat sequence file header data into an ASCII form which can easily be imported into a spreadsheet program. It runs perfectly well under Windows.
Download DGET35.ZIP (30k)
Anamusic can be used to generate .WAV files, simulating bat detector output, from AnaBat sequence files. Anamusic runs in DOS, but this can be under Windows. Anamusic will make .WAV files from all the sequence files in the current directory.
Download AMUSIC34.ZIP (33k)
Anatimer is a utility program which can be used with AnaBat6 to switch AnaBat on and off at various times of day or night. It is designed to make it easy to set up a permanent bat monitoring station, and to free the user as much as possible from having to watch over the process. For example, it can be used to ensure that the system will restart correctly in the event of a power failure crashing the system. The ZIP file includes some documentation in two text files.
Download ATIMR12.ZIP (79k)
I've recently read a number of papers, or drafts of papers, which have quite seriously misrepresented aspects of AnaBat and what it can do. To some extent, I can't blame the authors for this, since there isn't much published on what AnaBat really can do. On the other hand, it gets tiresome to see the same old nonsense repeated ad nauseum as if it were fact, by people who clearly don't have a good understanding of how AnaBat works or of how it can be used.
One subject which comes up again and again, is the claim that AnaBat cannot deal with harmonics. To quote a recent example, in the paper "Constant-frequency and frequency-modulated components in the echolocation calls of three species of small bats (Emballonuridae, Thyropteridae, and Vespertilionidae)" by Fenton, Rydell, Vonhof, Eklof and Lancaster, Can J. Zool. 77: 1891-1900 (1999), the authors state (page 1895) "Variation in harmonic composition is a further difficulty, and the calls of R. naso make it obvious that a bat-detection system like AnaBat ........., which does not display information about harmonics, may not be reliable for identifying some species ..... such as R. naso." This statement is extremely misleading, so I'd like to put the record straight here, and explain what this means and why it's wrong.
Spectral analysis and ZCA
Firstly, let me explain what spectral analysis and ZCA are, and why there is a general perception that ZCA can't deal with harmonics.
In spectral analysis, as it is generally employed, a bat call is sampled at a very high rate and the samples are passed through a software algorithm called Fast Fourier Transform (FFT) which can be used to separate out the various frequency components which make up the signal being investigated. Output can take several forms, such as a power spectrum, which is typically presented as a summation of the various components in the whole bat call, or as a sonogram, which is effectively derived from many short term power spectra, and which shows graphically how the power spectra vary in time. Using spectral methods, many harmonics of the same signal can be displayed at once.
In ZCA, dots are drawn on the screen to show the average frequency of the loudest part of the signal, since the last dot. The time between dots is inversely proportional to the frequency being measured, but also depends on the frequency division ratio being used. The result, for a bat call, is a single curve which shows the way in which the frequency of the bat call varied in time. At any one time, ZCA can only respond to the loudest (dominant) component of the signal being investigated, so if more than one harmonic are present in the original signal, only one of these can be displayed and it will always be the loudest (but see below what that means).
So let me be really clear about this. If you want to investigate the harmonic structure of bat calls, then spectral analysis is the better way to do that, because it will show, for each bat call studied, the full range of component harmonics present in the signal (within certain limitations - if a component is too faint, even spectral analysis won't show it). If you wanted to quantify the relative intensities of the different harmonics, then spectral analysis is certainly the way to go. But for many purposes, full harmonic detail isn't required. AnaBat, using ZCA, is designed specifically to present data useful for species identification and it is optimized for real-time display of frequency-time characteristics of bat calls, and for convenient storage of recordings of those calls. While it can't match the harmonic detail possible with spectral analysis, it can still be used to reveal a great deal about the harmonic structure of bat calls, and in particular, it can reveal the details of most interest for species identification.
Harmonics
Any real sound consists of many frequency components. To have only a single component, a sound would have to consist of a perfect sinewave of infinite duration. As soon as we modulate a sound (turn it on and off, for example), we increase the spectral complexity of the sound (we increase the number of frequency components). Many real sounds have multiple sources. If we strike a bell, then that bell has several independent modes of vibration, which cause it to simultaneously emit several different signals, each with a different frequency composition, decay time, etc. Each of these modes appears as a separate source of sound.
Real sounds also aren't perfect sinewaves, and any distortion of a sinewave results in the production of a harmonic series. A harmonic series is just a collection of harmonics, where the frequency of each harmonic is an exact, whole number multiple of the fundamental frequency. Harmonics are, by definition, EXACT whole number multiples of the fundamental. We can refer to the fundamental as H1, the second harmonic as H2, the third as H3 etc. Note that the component which has a frequency exactly twice the frequency of the fundamental is called the SECOND harmonic, not the first as some people seem to think.
In some systems, different modes of vibration can have frequencies which are very close to, though not exactly, whole number multiples of the primary mode. If we pluck a violin string, it will vibrate with several modes, one of which will have a frequency very close to twice the frequency of the primary mode. But this second mode is not a harmonic, as the frequency is not exactly twice that of the primary mode, and it is produced by a different mechanism. Yet it is often called a harmonic. A better word would be overtone.
For practical purposes, we can think of a bat call as produced by a single mode of vibration and consisting of a series of harmonics. The fundamental (H1) is the lowest harmonic present in the series. The harmonic structure (the proportions of energy in the different harmonics) of the sound leaving the face of the bat may not closely resemble that of the signal emanating from the vocal chords. The different harmonics will be amplified or attenuated by various resonances inside the bat, or even outside it, in such a way that certain harmonics may be greatly emphasised in the signal leaving the bat, while others may be profoundly suppressed. It isn't necessary that the fundamental will be dominant at the face of the bat, and it also isn't necessary that the same harmonic will be dominant throughout a whole bat call.
Which harmonic is dominant?
From the point of view of an AnaBat detector, which harmonic is dominant will depend on many factors, some extrinsic to the detector and others internal to it.
Firstly, the bat itself will determine the mix emanating from it. Secondly, the mix will be different from different directions relative to the bat. Typically, high frequencies will be more focused by the bat than lower frequencies, so the proportions of energy in the different harmonics will depend to some extent on which way the bat is facing. We should expect that higher frequencies will be favoured in a beam out the front of the bat, while lower frequencies would be relatively emphasised to the side or rear. However, this relationship is going to be very complex, and will vary greatly between species.
Thirdly, the atmosphere attenuates high frequencies more than lower frequencies, so that will also have an effect. What this will mean, is that at a distance, lower harmonics will be favoured over higher harmonics. Even if H3 was dominant close to the bat, H1 might be still be dominant from a few metres away.
Then there are the frequency-dependent factors intrinsic to the bat detector. The microphone of the AnaBat is much more directional to higher frequencies than lower frequencies. The microphone also does not have a flat frequency response, and the preamplifiers inside are designed to roll off in sensitivity to lower frequencies, so that peak sensitivity should occur around about 50 to 60 kHz, with much less sensitivity down at 20 kHz or below.
When you consider all these factors, it should be apparent that which harmonic is dominant won't be easy to predict. However, we can illustrate how it works with a few examples.
Consider firstly a bat producing constant frequency calls of H1 = 20 kHz and H2 = 40 kHz with equal energy into both harmonics. The AnaBat will be much more sensitive to H2, while the atmosphere will transmit H1 better. So at close range, such a bat would always be detected on H2, while from a long distance, it would always be detected on H1. If such a bat emitted low intensity calls, it might always be detected on H2, since it couldn't be detected from far enough away for the frequency response of the atmosphere to become an issue. An interesting consequence of the CF call would be that at some distance, neither H1 nor H2 will really dominate the other, and in that situation (which will rarely happen in real life) the AnaBat would detect the bat as at some intermediate frequency which isn't present in either of the harmonics. Such an intermediate frequency signal would probably not look at all like a bat call, as it would fluctuate wildly even within a single call. Since the distance between bat and detector is continually changing in nearly all circumstances, the probability is extremely low that H1 and H2 could remain equally dominant for any length of time. In practice, I have never seen this happen.
Now consider another example. Suppose a bat produces an FM sweep which on H1 sweeps from 30 down to 10 kHz and on H2 sweeps from 60 down to 20 kHz, again placing the same energy into both harmonics throughout the call. From a long distance, such a bat would again be detected only on H1, while at close range, H2 would always dominate. At some intermediate range, however, H1 would be dominant in the early part of the call, and H2 would be dominant in the later part of the call. This would happen because of the interplay between the frequency responses of the AnaBat and the atmosphere. At such a distance, early in the call, H1 will dominate because it is less attenuated by the atmosphere. But as the frequency sweeps downwards, it will reach a point where the frequency response of the detector overwhelms the response of the atmosphere, and H2 will then become dominant. The user would see a pattern in which the harmonic switched midway through the call from H1 to H2. A typical manifestation might be a call which appears to sweep down from 30 to 20 kHz, then jumps up to 40 and continues to sweep down to 20. Just where in the call the switch from H1 to H2 happens, will depend on the distance to the bat, so as distance is increased, the switch between harmonics will occur progressively later until at some distance it doesn't occur at all.
Thirdly, consider another bat which produces a sweep down from 40 to 20 kHz on H1 and a sweep from 80 to 40 kHz on H2. This bat differs from the preceding bats in that H1 is produced with much more intensity than H2 for most of the call, but just as the H1 is reaching 25 kHz, the bat suddenly switches its output so that H2 is now much louder than H1. The pattern of harmonic switching shown by such a bat would depend on the extent to which the harmonic produced with most intensity overwhelms the other. It may be that H1 would still always be detected at distance and H2 at close range. But whether or not this is the case, there will be a range of intermediate distances at which the call will always appear to switch from H1 to H2 at the same point in the call. So at these distances, the call will be seen by AnaBat as sweeping down from 40 to 25 kHz, then jumping up to 50 kHz and sweeping down to 40 kHz.
Harmonic Switching, where AnaBat detects different harmonics at different times, is often encountered in AnaBat, and it can tell us a lot about the harmonic structure of the call emitted by the bat.
Harmonic switching
In the hypothetical cases above, we can see three different ways in which the dominant harmonic can be switched. In the first, the harmonic switches within a sequence but between calls. As the bat comes closer, H2 takes over from H1, and as it gets further away again, H1 will take over again. In the second case, the harmonics would again switch between calls, but they could also switch within a call, with the point at which the switch occurs being determined by the atmosphere and bat-detector characteristics and the distance between bat and detector. In the third case, the harmonics will again switch within-call, but the part of a call where the switch occurs will be controlled by the bat.
Bat-controlled Harmonic Switching seems to be very common. It often appears in species such as Tadarida brasiliensis and Eptesicus fuscus, when they are flying in high clutter or tight maneuvers, even though they never show it when flying in low clutter. It can be recognized when the switch between harmonics keeps occurring at the same place in each call, and this also often takes place at some boundary in the call (such as the "knee", where there is a sudden change of slope), producing an effect where the two (or more) harmonics seem to belong to completely different call shapes. Such an effect can look very much like the presence of two completely different bats, until a close examination of the calls shows that the higher portion always appears immediately after the lower portion.
It is my impression that in every case where Harmonic Switching is seen by AnaBat, the point of switching is at least partly controlled by the bat. Even in a case like that of Townsend's Big-eared Bat Corynorhinus townsendii, where the HS can occur almost anywhere in the call, it often tends to take place at the same point for several calls in a row.
Some real cases
In Australia, the Yellow-bellied Sheathtail Bat Saccolaimus flaviventris, is a widespread species, mainly in the inland. It is one of the few clearly audible species in Australia, yet AnaBat always picks it up at 20 kHz. I can't hear anything near 20 kHz, so I had always assumed that the 20 kHz being detected must be an H2, and that the audible signal was H1 at 10 kHz. But a bat researcher using spectral analysis assured me this wasn't the case, and that there was no sign of a signal at 10 kHz. Having puzzled over this for a while, I went carefully over all my recordings of the species, and eventually I found a case where there was obvious Harmonic Switching taking place within calls, between signals at 20 and 30 kHz. Clearly, these harmonics have a relationship of 2 to 3, so establishing that the 20 kHz is the second harmonic (it could be a higher number, divisible by 2, of course, but it is at least the second). In this case the H1 was presumably too weak to be detected by the spectral analysis being used (and it would probably never be detected by AnaBat), yet it is clearly audible to humans.
Townsend's Big-eared Bat Corynorhinus townsendii is a species whose calls could easily be confused with other species, except that it shows a very distinctive harmonic structure. In this case, Harmonic Switching occurs both within and between calls. Although the calls it gives are often faint and therefore hard to detect, even very brief sequences of their calls typically show the distinctive patterns of Harmonic Swicthing when AnaBat is used, and there is nothing confusing or ambiguous about the way this is presented.
Several species, such as Mexican Freetail Tadarida brasiliensis and Big Brown Bat Eptesicus fuscus frequently change their harmonic mix to emphasise the H2 at the end of their calls when they are in high clutter, a fact easily observed using AnaBat. Although this behavior is not in itself of diagnostic value, it is a point worth noting, since the presence of H2 in the AnaBat display tells the observer that the bat is in high clutter, and can help separate such calls from those of species like Pallid Bat Antrozous pallidus, which in low clutter produce similar calls but lacking the H2.
Rhynchonycteris naso, used by Fenton et al (above) as an example of a species not reliably identified by AnaBat, is in fact a good example of a species whose harmonic structure is readily apparent to a competent AnaBat user (see O'Farrell and Miller, Biotropica 31(3): 507-516 (1999)).
Summary
AnaBat would certainly not be a good choice of system for the purposes of studying harmonic structure in detail. However, in cases where harmonic structure seems to be of significance to species identification, it is quite easy to see the main features of the harmonic structure because harmonics of roughly similar amplitude become dominant under different circumstances. Bats detected under real field conditions vary in distance from the observer throughout a sequence, so the harmonic structure becomes apparent when several calls are seen, and in many species, Harmonic Switching within a call allows something of the harmonic structure to be seen even in a single call. Contrary to what is often stated, the presence of more than one harmonic is in no way "confusing" to an AnaBat, but is often helpful for species resolution. As with many aspects of AnaBat, the emphasis is on variation and the recognition of patterns of variation over many calls, rather than on resolving finer detail in a smaller number of calls, which is the strength (and also a weakness) of spectral analysis. In real field situations, AnaBat does a surprisingly good job of resolving harmonic structure where this is important.
- Active monitoring
- Approach phase
- Call
- Characteristic frequency
- Characteristic slope
- Clutter
- Commuting calls
- Drinking buzz
- Duration (pulse duration)
- Duty cycle
- Feeding buzz
- Frequency sweep
- Pass
- Passive monitoring
- Pulse
- Search phase
- Sequence
- Spectral analysis
- Terminal phase
- Time Between Calls (TBC)
- Zero Crossings Analysis (ZCA)
Active monitoring involves humans. There are many ways this can be done, but the common element is that a human is present and influencing the process of monitoring bats. At one extreme, Active Monitoring could be just like bird watching, where an observer goes out watching bats and using a combination of acoustic and visual cues to help identify them. But Active Monitoring doesn't necessarily involve collection of visual cues. Just holding the bat detector in your hand will have a significant impact on how you record bats, because you will tend to orient the detector towards the bats, thereby improving the quality and quantity of calls you record. I would say that Active Monitoring must involve some direct human control over the recording process. But like everything in biology, the boundaries can be fuzzy! I would argue that someone sitting on the roof of a moving vehicle watching bats is undeniably Actively Monitoring, but someone driving a car with a detector mounted on the roof is another proposition!
Active Monitoring, compared to Passive Monitoring, results in more and better quality calls being recorded, it often leads to much higher identification rates because of the better quality recordings and the presence of helpful visual cues, and it allows the observer to actively explore for bats instead of just waiting for the bats to appear.
Passive Monitoring takes place when the bat detector records bats in the absence of direct, human control over the recording process. In effect, this is data logging of bat calls. Passive Monitoring has the advantage that bats can be monitored for very long periods. It is not uncommon for Passive Monitoring stations to record all night, every night, for weeks, months or years. Thus the sampling effort achievable using Passive Monitoring is vastly greater than for Active Monitoring. This means there is a greatly enhanced chance of detecting rare or hard-to-detect bats, because the effort being put into looking for them is so great. Passive Monitoring is ideally suited to looking at temporal patterns of activity, but because several detectors can be deployed by a single human with little need for servicing, it is also an excellent tool for looking at spatial heterogeneities. Another aspect of Passive Monitoring is that it collects data without any human influence on the recording process, so it has the potential to produce highly objective measures uninfluenced by observer biases. This assumes, of course, that identifications can be made without human subjectivity, but that is the role of automated identification systems, which can identify and collate all the recordings by machine. Such systems are getting better and will continue to do so, but for many purposes, they are already useful and for handling large data sets from multiple Passive Monitoring stations, they are virtually essential.
This is a sequence of bat PULSES given as a bat approaches a potential prey item. It is typically characterised by a progressive reduction in PULSE DURATION and increase in FREQUENCY SWEEP as the bat transitions from SEARCH PHASE towards a FEEDING BUZZ. However, PULSE DURATION may actually increase initially in the APPROACH PHASE, and not all species increase the FREQUENCY SWEEP during approach. APPROACH PHASE can usually be recognised as a period of transition, where the PULSES progressively change their nature towards a FEEDING BUZZ. But on their own, APPROACH PHASE calls typically look very like SEARCH PHASE calls given in CLUTTER.
A single, complete burst of sound emitted by a bat, separated by silence from other calls. Most bat calls are given in a single breath, and are supposedly synchronised to wingbeats, but this obviously isn't always the case, when you consider rapidly repeated vocalisations such as those in a feeding buzz. If you compare the use of the term CALL with that applied to birds or frogs, it isn't analogous. In the bat world, a CALL could be just a single element of a FEEDING BUZZ, whereas in a bird or a frog, such an element would usually be called a PULSE, and the FEEDING BUZZ as a whole would be called a CALL. Yet in a bat, there isn't always a clear distinction between a FEEDING BUZZ and the CALLS leading up to it, so perhaps the term PULSE might be better for bat vocalisations, as this can always be equivalent to a PULSE in a frog or bird call.
This is a call parameter that is easily visualised if you look at an AnaBat display. It can be defined as the frequency at the right hand end of the flattest portion of a call. It is by far the most important single parameter for distinguishing species, though it will rarely be diagnostic in itself.
Fc is often close to the minimum frequency of a call, but is less variable because the minimum frequency will often occur in a downsweep at the end of a call, where the amplitude is decreasing rapidly. In other words, minimum frequency will typically occur when the call is dying out at the end, and just how much of the end of the call will be detected will depend on distance to the bat amongst other things. In a call such as produced by a Horseshoe Bat, Fc will be equal to the maximum frequency.
Some calls will contain more than one flat portion. In that case, it might not be evident which flat portion should be used to define Fc if you rigidly follow the above definition. But usually such calls will be given in a social context, and you can see that the second flat portion is a variation on a normal call. Bear in mind what the normal call would have looked like if the variation hadn't occurred, but better still, exclude such variations from your analysis.
Fc will most often not be diagnostic of a particular species in itself, but in any given locality, it will always limit the range of possibilities to a very small number. For identification purposes, you don't have to worry about measuring Fc - just look at the display and see what ballpark it lies in. Remember that it is subject to variation due to DOPPLER SHIFTS, like any parameter, and that it will usually vary between individuals, and according to what the bat is doing. Many species vary Fc depending on how many individuals are flying in the area. Again, it isn't the exact value which matters, but the general area where it lies.
Sc is another very important parameter for species identification, closely tied to the call SHAPE. It can be defined as the slope of the flattest part of the call. Sometimes, though, the flattest part of the call may occur in a short added on section at the end, often called a TOE. In such a case, it might be better to take the Sc as the slope of the section preceding the TOE, because that section is always present, whereas the TOE can be just an occasional add-on. For example, many Myotis calls have an essentially linear shape, in which case the Sc will just be the slope of the call as a whole. But in some cases, a short flat section might be added at the end of the call. In that case, it would be more meaningful to take Sc as the slope of the bulk of the call.
If ever you feel the need to make measurements of call parameters, you should always tailor your measurements to what makes most sense for the species you are working with. Don't feel constrained by traditional views of what should be measured, or by trying to force calls into a stereotyped model.
CLUTTER can be thought of as just the distance to the nearest object from which a bat can pick up an echo. So a bat flying in the open, well above the ground and well away from any objects, may be in zero CLUTTER, while a bat flying amongst treetrunks or along water close to a river bank would be in high CLUTTER. A bat emitting high frequency, low intensity CALLS may still be in low CLUTTER even when flying just a few metres from objects, while a bat emitting loud, low frequency calls might be in high CLUTTER even 30 m above the ground. So the degree of CLUTTER depends on the bat, as well as on the distance bewteen the bat and something else.
CLUTTER is the major factor determining the type of calls a bat produces when it is in SEARCH PHASE. Typically, a bat in high CLUTTER will produce calls of shorter DURATION, longer FREQUENCY SWEEPS, more rapid repetition rate and lower intensity, than the same bat would in low CLUTTER. So SEARCH PHASE calls tend to fall along a continuum, depending on the CLUTTER. At one extreme, a bat in zero CLUTTER will produce the flattest, longest DURATION, most widely spaced and loudest calls you will ever see from that bat. Such calls are often referred to as COMMUTING CALLS, as they are the sort of calls the bat will produce when flying directly from one place to another through open space. At the other extreme, calls produced in very high clutter may be very quiet, so hard to detect, and often just consist of very brief, steep DOWNSWEEPS. High CLUTTER calls are usually much more difficult to identify than low CLUTTER calls, because they tend to be similar even between very different species. But in some cases, higher clutter calls may be more distinctive. Every case should be treated on its own merits.
A useful term to describe the sort of SEARCH PHASE calls a bat produces in zero CLUTTER. These calls typically are the loudest and have the longest DURATION, shortest FREQUENCY SWEEP and are produced at the lowest rate of any that particular bat will emit.
Many species produce a distinctive series of CALLS when they come down to water to drink. These DRINKING BUZZES usually resemble a very long, drawn out FEEDING BUZZ.
The total time that a single CALL, or PULSE, lasts. It is important when measuring DURATION not to include any echoes. Usually echoes are distinctly separated from the main part of a call, but in some cases the distinction can be hard to make.
The Duty Cycle of a call sequence is just the proportion of time in which a call is actually being produced. It could be expressed as: DC = Dur / TBC * 100 where DC is Duty Cycle, Dur is call Duration and TBC is the Time Between Calls. As long as Dur and TBC have the same units, the DC is a simple percentage. Typical bat call sequences have a Duty Cycle of about 10%, but some species, such as the Horseshoe bats, have much larger Duty Cycles.
A FEEDING BUZZ is a rapid series of PULSES given as a bat approaches potential prey. In much bat literature, three echolocation phases are recognised, SEARCH PHASE, APPROACH PHASE and TERMINAL PHASE (which is the same as a FEEDING BUZZ). A FEEDING BUZZ is usually, but not always, a very distinct entity, clearly recognisable from other vocalisations by its rapid repetition rate.
Most bat CALLS consist at least partially of a FREQUENCY SWEEP, where the frequency of the CALL changes in time. The only real alternative is a CALL which stays on the same frequency for its entire DURATION, which hardly ever happens. The total FREQUENCY SWEEP of a CALL can be thought of as the total range of frequencies between the maximum and minimum frequencies. This is a better term than BANDWIDTH, often used in traditional bat literature, but quite inappropriate as the term has other meanings in engineering, and in any case, should apply across all the harmonics the bat emits.
A series of CALLS given by one bat, typically the CALLS detected by a bat detector during a single pass of one bat past the detector. Since a bat almost has no alternative than to approach the bat detector and then move away, it can do little else! Obviously, the concept will get a bit hairy if a bat flies continuous circles around the detector. Often, bats will make repeated PASSES past a single point, and the definition of when PASSES start and end can be completely arbitrary. For this reason, people often define a PASS as having to meet certain criteria. For example, a PASS might only end if the bat has gone away for more than one second.
A single vocalization produced by a bat, separated from other vocalizations by silence. Synonomous with CALL in typical usage applied to bats. Note that a PULSE given by a bat can always be thought of as analogous to a PULSE in a frog call. In a frog call, there are usually many PULSES, and the PULSES may blend together to a variable degree. With bats, the term CALL applies to just one PULSE. The problem arises when you think of a FEEDING BUZZ, which contains a number of PULSES. Is a feeding buzz a single, multi-PULSEd call, or is it a series of single-PULSEd calls? For this reason, PULSE is probably a better term, as it can always be thought of as analogous to a PULSE in a frog or bird call.
In the traditional jargon applied to bat echolocation, three phases of echolocation calls are recognised, SEARCH PHASE, APPROACH PHASE and TERMINAL PHASE, more descriptively called a FEEDING BUZZ. In the SEARCH PHASE, the idea is that the bat is just motoring along looking for something to eat. SEARCH PHASE calls, then tend to be characterised by their relative uniformity. As long as the bat stays in the same degree of CLUTTER, flies straight and doesn't detect anything it can eat or it should be frightened of, it will produce similar-looking calls. Of course, on an AnaBat screen, the calls won't all look the same, because when the bat is further away than some critical distance, you won't be able to see the quieter parts of the calls (typically the start, and sometimes also the end of each call tend to go missing).
This traditional jargon isn't much help to those interested in acoustic identification of bats, because it completely fails to take into account the fact that SEARCH PHASE covers a very wide range of different call types. Most of the time you detect a bat it will be giving SEARCH PHASE calls, yet these will probably vary immensely (depending on species). It will be more helpful to think of the variation in call types to be related to the degree of CLUTTER in which the bat is flying. In fact this approach could even be said to include all the traditional calling phases, since in a sense, a FEEDING BUZZ is just a specialised call type given in extremely high CLUTTER.
A SEQUENCE just refers to the series of CALLS which you happen to be dealing with at the time. So a SEQUENCE could be the same as a PASS, or it might contain several PASSES. It might refer to the contents of a single AnaBat file, or it might be spread over several files. It could be that all the calls in a SEQUENCE were produced by a single bat, or by several bats. Think of a SEQUENCE as just a bunch of calls.
The traditional way of revealing the frequency component of a signal is SPECTRAL ANALYSIS. This is a very complex process in which the original signal is scanned by a large number of filters, each filter tuned to a narrow part of the frequency spectrum being investigated. The output of each filter represents the content of the signal within the bandwidth of that filter, so if you use lots of filters, you can build up a three dimensional image of a signal, showing the amplitude of each frequency component and how that varies in time ( a sonogram). There are other displays you could use also.
In the past, this was carried out by a sonograph, a large machine which recorded the signal on a magnetic medium and then repeatedly scanned it with a tuneable filter. The frequency to which the filter was tuned was controlled by a physical arm which was moved gradually upwards by a rotating thread. As the arm moved upwards, it not only tuned the filter, but it also carried a high voltage which burned a trace onto a sheet of paper wrapped around a rotating metal drum. The louder the signal at the frequency the filter was tuned to, the blacker the trace burned into the paper. It was a brilliant piece of equipment but exasperatingly slow. I don't want to think how many weeks of my life I spent burning traces of frog calls into paper on a sonograph.
Nowadays, spectral analysis of audio sounds is easy to carry out on your PC, thanks to the universal occurrence of soundcards in PCs and to the ready availability of software, some expensive, some free, which performs a process called FFT (Fast Fourier Transform). FFT simulates the bank of filters approach using a very cunning, very efficient software algorithm. On a reasonably fast PC, you can even analyse audio signals in real time, though at a reduced resolution. It probably won't be long till a laptop will have the power to do realtime FFT analysis of bat calls, though using special hardware, not a PC soundcard.
The final phase of vocalisations as a bat zeroes in on a potential prey item. FEEDING BUZZ is usually a more decriptive term, since the TERMINAL PHASE is usually very distinct and easily recognised from other aspects of a bat's vocal repertoire.
This is simply the time (typically measured in milliseconds) between the start of one call and the start of the next call. A curious fact is that so many species, when they are flying in the open, produce calls at a rate of about 10 per second, giving a TBC of 100 milliseconds. There is evidence that in at least some situations, bat calls are synchronised to their wingbeats, and the evidence is such that it seems likely this will usually be the case in SEARCH PHASE. If you look at a frequency distribution of the TBC values in a SEARCH PHASE call sequence, you will typically see a major peak near 100 milliseconds, and a second, smaller peak at about 200. The latter comes about because bats often reduce their calling rate by missing some calls, and it supports the view that calls are sychronised to wingbeats, since this would require that a missed call will double the TBC and that intermediate values won't occur. In practice, of course, calls can be decoupled from wingbeats and the extreme example of this would be the DRINKING BUZZ.
ZCA is the approach used by AnaBat to analyse the frequencies in bat calls. It works as follows:
Every sound consists of waves of pressure where pressure rapidly alternates between being higher and lower than the average value. In air, this means that at any one point, as the sound wave passes, the air pressure fluctuates above and below the average air pressure (atmospheric pressure) These fluctuations are tiny, but they can be converted to electrical signals with a suitable microphone. These electrical signals alternate between positive and negative with respect to the average voltage at that point in the circuit. The average voltage isn't of interest here, the fluctuations are what matter. The ZeroCrossings are just the points in time when the electrical signal crosses over the average value from negative to positive (a positive-going transition) or from positive to negative (a negative-going transition). Consecutive transitions are always in opposite directions, so every second transition will be going in the same direction and transitions going in the same direction are called LIKE TRANSITIONS.
If you measure the time between LIKE TRANSITIONS, you get a value which is equal to the PERIOD of the waveform being examined. The PERIOD is just the reciprocal of the FREQUENCY, so the FREQUENCY can be calculated easily from the times of these transitions. All you need is a list of the transition times, and you can recover both frequency and time data. These data can be plotted on a graph to give a visual representation of the frequency characteristics of a bat call. Each DOT on the display represents the average frequency over the period since the last dot.
AnaBat uses a more complex scheme. Firstly, bat echolocation calls can use frequencies over the range of 4,000 to 210,000 Herz. Most of this is outside the range of human hearing. In order to hear the bat calls, you need to generate a representation of them at audible frequencies. AnaBat does this by counting transitions, and outputting one transition for every 8 LIKE TRANSITIONS of the original signal. This is the so-called "frequency-division" or "countdown" scheme, and results in an output frequency one sixteenth that of the original signal. AnaBat can also use DIVISION RATIOS of 4, 8 or 32. The signal entering the ZCAIM is an audible signal at some fixed fraction of the frequency of the bat call.
The ZCAIM again detects the ZERO CROSSINGS of the audible signal output by the bat detector. The AnaBat software produces a DOT on the screen for every CONSECUTIVE TRANSITION at the input to the ZCAIM, but it calculates the frequency for each dot by taking the reciprocal of the time between LIKE TRANSITIONS. This scheme results in twice as many DOTS for the same input frequency as you would get if you used only LIKE TRANSITIONS.
The flatness display and the use of Fpz as an indicator of bat call frequency
Introduction
The Flatness Display was introduced in Analook version 4.9f as an alternative display of bat call characteristics, accessible by pressing Ctrl-F4. The notes in this page refer to the manifestation of the display in version 4.9g, where it has been enhanced slightly.
The Flatness Display produces a graph of the time spent by a bat call in each frequency band, or the number of cycles in the original signal in each frequency band. The frequency bands are defined by the rows of pixels along the vertical axis of the graph, and exactly match the frequencies being displayed in the normal ZCA frequency vs time graph on the left side of the screen. The time or number of cycles being spent in a given frequency band are an indication of the inverse of the slope of the call as it passes through that frequency band, hence the term Flatness. Higher values in the Flatness Display indicate lower values of slope in the call, although the Flatness Display makes no distinction between positive and negative slopes.
Indicator frequencies
If you wanted to describe a bat call to someone else, probably the first thing you would like to convey is the frequency of that bat call. Since most bat calls change in frequency throughout the duration of the call, which frequency would you refer to? It would seem a bit pointless to refer to the maximum frequency, since any perception of that parameter will be strongly influenced by the distance between observer and bat. The same comment applies to minimum frequency, although more often it does correspond to something more meaningful. I'm sure most people with experience of bat calls will agree that a more useful parameter to be used as an indicator of frequency would be something along the lines of the frequency at which the call spends most time, or where it is loudest.
Traditionally, the parameter used as an indicator of frequency has been Fpeak, or the frequency of the call at the point where the amplitude is greatest. There are various ways in which Fpeak has been calculated, but usually it involves producing a power spectrum across a whole bat call and taking the frequency at the point where the power spectrum reaches its greatest value. Fpeak has not always been used in a consistent way, and it hasn't always been calculated properly. It should be determined using a single FFT slice across the entire call without any windowing function (or a rectangular window covering the whole slice). If a windowing function is used, this will make Fpeak depend on where in the window the call is placed, and it will introduce a variable which has nothing to do with the bat call but is an artifact of the measurement process.
Fpeak seems simple to measure and it seems to have a reasonable biological significance. It seems a reasonable assumption that the frequency where a bat puts most energy will somehow mean something, and it will also generally correspond to the frequency where the bat will detect an echo from the greatest distance. However, Fpeak has some disadvantages as well. Firstly, and most importantly, it requires the use of spectral analysis, which means it can't be used by AnaBat users. But even for those who feel they must torture themselves with spectral analysis, Fpeak has some problems. In general, Fpeak occurs somewhere in the part of a call with the lowest slope, but there doesn't seem to be much consistency about just where. To make matters worse, many bat calls fall into a category where Fpeak doesn't really mean anything, since the call ramps up in amplitude during the initial downsweep and then remains at essentially the same amplitude for an extended period of time until the final ramp down. In such a case, the position of Fpeak is essentially arbitrary - we are forcing the call into having a peak even where there is no peak.
From an AnaBat user's point of view, Fpeak has no value, since we can't measure it. The usual parameter to use as an indicator of frequency is therefore the characteristic frequency, Fc. Fc is the frequency at the right hand end of the flattest part of a call, and as such, it avoids the initial downsweep and any droop off in frequency at the end of the call. Fc shows a lot more consistency than Fpeak, showing lower variability within a species or within the calls of an individual. Partly, this is because Fc is inextricably linked to the shape of a call (the shape of its frequency vs time graph). If the calls are the same shape, Fc will always be at the same place, a feature not shown consistently by Fpeak. Fc is readily available to those who use spectral analysis, although it has rarely been used by them, mainly, I suspect, because Fpeak is so easy to measure.
Spectral analysis of AnaBat output?
Usually, the output of a frequency division bat detector will be analysed using ZCA. Why waste time and effort running spectral analysis on the output of a detector whose output information is entirely contained in the ZCA display? I discovered that some people in Australia were actually committing this heresy, and I spoke to Norm McKenzie in Western Australia about his use of the technique. His approach has been detailed in Bullen RD and McKenzie NL, 2002 (Differentiating Western Australian Nyctophilus echolocation calls. Australian Mammalogy 23: 89-93). I don't wish to convey here endorsement of their technique or the conclusions in that paper, but the approach sounded interesting and it made me wonder if there might be some way to achieve a similar result by using the data already available in the Analook display, without the messiness of the Fast Fourier Transform.
At this point, I do wish to make one thing very clear. Some people might get the impression from reading the paper cited above that spectral analysis of AnaBat output actually reveals some hidden information which isn't revealed using ZCA. This is absolutely untrue. There is NO hidden information in the AnaBat output, and anything extra which is revealed using spectral analysis is pure artifact. To give an example, spectral analysis of AnaBat output will reveal a signal rich in odd-numbered harmonics. But these harmonics did not come from the bat in any sense at all. They were produced as a pure artifact of the fact that the AnaBat output is a square wave. This has no bearing on anything the bat has done. There is NO hidden amplitude information buried in the AnaBat output that has anything at all to do with anything the bat did. Any amplitude information in the AnaBat output is entirely artifact resulting from the way the AnaBat output signal has been processed and/or stored. The ONLY information of any value in the AnaBat output is the time between zero-crossings, and this information is extracted completely by the ZCA process.
Nevertheless, a power spectrum of AnaBat output does produce a different view of the data, and because I felt that view might have some merit, I developed the Flatness Display so I could play around with it and see if it helped in any way. So here I want to explain how it works and how to use it, and let others decide for themselves how useful it might be.
Making a flatness display
In this section, I explain how the Flatness Display works, and how it relates to the FFT based power spectrum which it mimics.
To make a power spectrum, you run a single FFT transform across the sampled waveform of the AnaBat output corresponding to a single bat call. This transform results in dividing the frequencies present in the signal into a number of frequency "bins", and then outputting a graph showing how the amplitude of the signal is divided amongst those frequency bins. In essence, what you have done is taken the signal and played it simultaneously through a large number of filters, each tuned to a different frequency range. The frequencies of these filters are called orthogonal, because they are all mutually exclusive - no signal component appearing in any particular filter is present in any other filter - they are all independent. The FFT itself is an extremely clever and unbelievably efficient way of doing this mathematically in a single pass. It is a masterpiece of human intelligence.
In a power spectrum, there is just a single value output for each frequency, representing, in effect, how much energy is contained in the signal at that frequency. The FFT effectively integrates the amplitudes across the whole slice (the time interval which is being examined by the FFT). When you perform an FFT on AnaBat output, there is no amplitude information, since the AnaBat output is a square wave. So the net effect of the FFT is to produce an output for each frequency which is proportional to the time which the signal spends at that frequency. This is a bit of an oversimplification, since there are a number of possible ways of representing the FFT output, but the principal is there.
The Analook display is just a graph of frequency vs time. Furthermore, it is already quantised, that is, broken into a series of frequency bins, by the process of mapping the frequency values onto the rows of pixels which make up the display. Therefore, it is a simple matter to calculate how much time the bat call spends passing through each of these frequency bins (or bands), by simple linear interpolation between any two dots on the screen. In practice, some assumption must be made as to how the frequencies change in time between any two dots. One assumption could be that the frequency changes linearly with respect to time, but I actually used the assumption that the period (the reciprocal of frequency) changes linearly with time. The difference is of no consequence within a bat call, but I found the linear period assumption produces a nicer display when showing the results of having two consecutive dots at very different frequencies, as can happen when certain types of noise are being displayed. It was a purely cosmetic decision.
The net result of these surprisingly simple calculations is that you end up with an array of values which represent the time spent in each frequency band, accumulated over the whole bat call. These values are then scanned for the largest value (ie the frequency for which the time is greatest) and then the values are graphed by mapping them so the largest value extends right across the width of the Flatness Display. In practice, two options are available - display of times or cycles. The cycles display is derived very simply by multiplying the accumulated time values by the midpoint frequency of each frequency band. The output value then becomes the number of cycles (oscillations) which the original bat call produced in each frequency band. Using the alternative time display, the output values are in milliseconds. Both versions produce a Flatness Display, in which the higher the value, the lower the slope of that part of the original call (ie the lower the rate of change of frequency with time).
Using the Analook flatness display
You can select the Flatness Display by pressing the "Ctrl-F4" key combination while a bat call or sequence is being displayed on the screen. The resulting Flatness Display can be toggled on and off by pressing the "v" key, in the same way that this toggles the Slope Display.
The Flatness Display occupies the right hand side of the screen, and it makes its calculations based on all of the data points visible on the left side of the screen. To isolate one single call for analysis using this method, use a Real Time display (toggled against Compressed mode using the spacebar) and increase the magnification of the time axis using the function keys until that call is the only signal visible on the left side of the display.
A graph of the selected Flatness function will appear, mapped so it uses the entire width of the display on the horizontal axis. The vertical axis will be frequency, mapped onto the screen exactly the same as the frequency axis on the left hand side of the screen. Along the bottom of the Flatness Display there will be five values. On the right is the frequency represented by the widest peak in the graph, which will correspond to the frequency at which the call/sequence spends most time, or produces most cycles. This value is called Fpz, because of its analogy to Fpeak as derived from a spectral display. Think of it as the peak frequency derived from ZCA.
To the left of the Fpz value is another figure which represents the number of milliseconds (mS) or cycles (c) found within the frequency band represented by the Fpz value. This value will probably be of little use by itself, but it does have some significance in correlating (negatively) with the slope of the call as it passes through the frequency Fpz. It should correlate closely with the Characteristic Slope (Sc) calculated elsewhere in Analook.
Further left are three more values in kHz which represent the widths (in frequency) of the largest peak of the graph at points where the Flatness parameter (time or cycles) is at one half, one quarter and one eighth of its maximum value. Essentially, these values are computed by scanning both ways from the Fpz value until the accumulated parameter (time or cycles) falls to a half, quarter or eighth of its peak value. These values may have some value as indicators of the overall slope and shape of the call. Steep calls will show higher values than shallow or flat calls.
Options
Two options are available by pressing the "Alt-p" key combination. One of these chooses whether times or cycles are displayed as the Flatness parameter. Press "t" to display times and "c" to display cycles.
The other option determines how much smoothing or averaging is applied to the Flatness Display. Three choices are available, selected by pressing the "0", "1" or "2" key. The display will be updated as soon as any of these keys is pressed. To accept the change, press ENTER and to revert to the state in operation before "Alt-p" was pressed, press ESCAPE.
No averaging is chosen when the "0" key is pressed. When the "1" key is pressed, the value stored for each frequency band is the average of three adjacent frequency bands. When the "2" key pressed, averaging is performed over 5 adjacent bands. The effect of averaging is to smooth out the Flatness Display, making it less sensitive to small irregularities in the dot positions of a call, and thus less sensitive to short term signal dropout (which leaves gaps in a call).
Practical implications times vs cycles
In many cases, there will be little difference between using times or cycles as the Flatness Parameter. The cycles display has an inherent bias towards higher frequencies, so peaks at higher frequencies will get more emphasis. In contrast, the times display puts relatively more emphasis on lower frequencies, and might therefore be more susceptible to being influenced by echoes or other noise.
Averaging
Smoothing of the graph by using averaging will generally be desirable, since it produces a less spiky, erratic display. However, it can sometimes be a little misleading. For example, if examining a Rhinolophus call, the peak will no longer correspond with the maximum frequency, which is where the peak should obviously be. The reason for this is that the frequency which should be Fpz will also be the maximum frequency, so when that frequency band is averaged with those either side of it, it will contain a smaller value than the band below it, which will also contain the value from Fpz. In such a case, it would be better to use no averaging.
In general, then, it might be best not to use averaging when dealing with calls with extended constant frequency components. In such cases, averaging will also be less beneficial anyway, since the peak corresponding with Fpz will be very unambiguous.
The significance of Fpz
The usefulness of Fpz needs to be assessed carefully to see how it compares with Fc as a general indicator of bat call frequency. The Flatness graph does seem to be very sensitive to call shape, and responds a great deal to very subtle changes in slope. On the other hand, it also seems less sensitive to signal dropout which can cause "holes" in the display of a bat call. These would seem to be desirable attributes. The concept of Fc, and the way it is estimated by Analook, make that variable more dependent on gross shape features, while Fpz seems to respond to more subtle shape features. Fpz is also easier to calculate in a consistent manner, although it does suffer from the fact that bat calls often show more than one peak in the Flatness parameter and occasionally a peak which is normally smaller will become the larger. Using a display of times rather than cycles may reduce this tendency, since lower frequencies will be relatively emphasised and the lower peak should therefore tend to be more consistently the larger.
Fpz will be much more closely tied to call shape than Fpeak, since it isn't affected by the amplitude of the signal, just the shape. While the amplitude of a bat call may have some significance in relating to where the bat is putting most energy into a call, it is also readily affected by artifacts and practical experience suggests that it will probably show substantially more variability for a given set of calls than Fpz. Interestingly, Fpz should be readily calculated from full-spectrum recordings, so it might have value as a character which can be used by both spectral analysis and ZCA.
Unfortunately, AnaBat/Analook won't run on a MAC or a LINUX machine. But PC's also offer a variety of operating systems which work or don't work to varying extents. The most important of these operating systems (OS's) are: DOS, Windows 3.1 (Win3.1), Windows 95 (Win95), Windows 98 Win98), Windows ME (WinME), Windows NT (WinNT), Windows 2000 (Win2k) and Windows XP (WinXP). It gets more complex, with various flavours of each OS, but the most important version difference is between the original Win95 (Win95a) and the later versions (Win95b) which were only released as OEM products supplied with new computers. For our purposes, it seems effective to think of the difference between Win95a and Win95b as being that Win95b introduced long filenames and the FAT32 file system, neither of which was available in Win95a.
What I offer here are comments on issues which have arisen under these different OS's and how they affect your use of the major components of the AnaBat system. But first, some general advice on obtaining a laptop for in-the-field use.
Choosing a laptop
AnaBat and Analook have quite different typical uses, and this is likely to influence the type of machine you might choose to run them on. Furthermore, I am working towards a Windows version of Analook, which will only operate on a machine using Win95b or later. For these and other reasons, processing AnaBat files previously recorded, is best achieved using Analook on a fast Windows machine. However, there are some potential issues which might influence how a modern machine performs in this role, and these are detailed below.
For active monitoring of bat calls in the field, where it is desirable to see the calls in real time on a screen, AnaBat has to be used. Field operation of a laptop is typically very hard on the laptop, because no matter how hard you try, eventually you will cause damage to the machine which will make it much less useful for other purposes. Dust and water will find their way into the smallest cracks, and it is amazing how much damage can be caused by small gritty objects like the mouthparts of deceased insects. For this reason, I strongly recommend NOT using a modern, fancy and expensive laptop in the field. It is MUCH better to get hold of an older machine for field use and consider it dedicated to that purpose, and accept the inevitable fact that it will get damaged.
Old laptops are quite easy to obtain at very reasonable prices. You could try EBay or a similar site on the web, and there are numerous places which refurbish old laptops and then sell them at very competitive prices. You can also call around a range of local computer stores until you find one which has such a machine for sale at a reasonable price, and you can ask around in the hope that someone may have one that they don't use anymore.
In my view, the best machines for active monitoring are early Pentium laptops running Win95b or Win98. 486 or even 386 laptops running DOS or Win3.1 will work fine, but have some limitations which make them less desirable. Downloading the recorded files off these earlier machines to another computer tends to be dreadfully slow, though this won't matter if all you are downloading is one night's data from active monitoring. The later machines also give you more options for connecting to other machines. The downside of later machines is that they tend to be more power hungry, and powering them in the field will most likely require some thought. Unfortunately, most old laptops will not come with useful batteries, and replacing the batteries can easily cost more than the laptop itself. Furthermore, even a good battery may not last long enough for your purposes, in which case you may need several batteries (and some way to charge them quickly enough) or else you will have to look at an external power source.
Powering laptops in the field
My favourite way to power a laptop in the field is to use a 12 Volt, 7AH Sealed Lead-Acid Battery, and to power the laptop through a converter designed to power a laptop from the 12V provided by a car cigarette-lighter adaptor. These power converters come in many flavours, but it is important to ensure that the one you choose offers the correct voltage to supply your laptop, and that it provides sufficient current. Broadly speaking, you can work out the current needs by looking at the AC-DC power supply which comes with your laptop. In my case, it shows a label reporting its output as 15V DC at 2.5 Amps. Now 2.5 times 15 is 37.5 Watts, so a converter rated at 30 Watts probably won't be sufficient. A 60 Watt converter would be a much better choice, and it would need to have an output of 15 Volts or very close to that (14 or 16 would probably work fine). Many such converters have a variable output voltage which you must take care to set correctly before use.
7 AH batteries are the best choice because these are standard in many applications such as burglar alarms and uninterruptable power supplies. They are therefore considerably cheaper than other, even smaller sizes.
Another issue which affects the utility of a laptop for field use is the type of connector which can be used to connect the converter into the laptop. These vary greatly and some are really terrible for field use (eg cables which are much too heavy and can break the connector when things move around) but another important issue is whether or not the connector is of a standard type which is likely to be offered by the manufacturers of voltage converters. Some laptops have very strange proprietary connectors for which you will have trouble finding a mate.
One factor which seems to greatly influence power consumption of a laptop is the state of the computer's internal battery. If the internal battery is flat, then power will be wasted trying to charge it while running the computer. For this reason, it is best to make sure the computer's internal battery is fully charged before you use it in the field even from an external source. However, I find that even a fully charged battery still draws a lot of current out of the external battery, nearly doubling the current used by the computer. So I pull the battery out of the laptop once the external power source is connected. Do so at your own risk, but it works for me, and I can get several hours of use from a single 7 AH battery. Bear in mind that if you pull the internal battery and the power fails for even a brief instant, any unsaved data will be lost and you will have to reboot your machine.
Different ZCAIMs
ZCAIMs also come in different flavours. The original Type 2 or Type 5 ZCAIMs did not use internal buffering and so were much more fussy about the machines they could run with than the later Type 6 or Mini ZCAIMs, which employ internal buffering and handshaking so they are much less affected by other things going on in the system. The earlier, non-buffered ZCAIMs cannot run under Windows, though they can generally be run on a Windows machine, provided it can be switched over to Raw DOS mode, where Windows is not running in the background. These early ZCAIMs are also fussy about any other process which steal time from the microprocessor running the laptop, so features such as power management will often interfere with the quality of the signal received, or even prevent the ZCAIM from operating at all. In such cases, it is usually possible to turn off the offending process, but this requires a knowledge of how to control the machine, often at a BIOS setup level.
Storage ZCAIMs can not presently be used for sending signals to a laptop directly, only through a Compact Flash card. However, in the near future, it will be possible to record into a laptop with a Storage ZCAIM through the laptop's serial port, but in that case, it will only run under Win95b or later.
Screen problems
Analook (and in some cases AnaBat) has encountered problems with certain types of machines which do not display the text on the screens correctly. This typically manifests as strange characters appearing on the screen where there should be numbers on the graph axes, or text along the lower portion of the screen.
I have wrestled with this problem at length and found no solution to it. If anyone can help, this would be greatly appreciated. But the problem seems to lie with some interaction between the Borland graphics engine used in Analook and certain types of video drivers, which fail to display text drawn in graphics mode.
Unfortunately, this problem seems to getting worse, and has shown up on a number of recent machines running Win2k or WinXP.
Another graphics related problem in Analook is that certain machines scramble the screen when the context is switched. For example, if you are running Analook under Windows and you switch back to Windows to check the time, when you go back to Analook again, the screen is completely scrambled into random-looking colors. The only option is to close down Analook and open it again, and then not to switch back to Windows while using Analook. But this is very undesirable, since it negates the great advantages of running a multi-tasking OS. Again, I have been unable to find any way around this problem.
DOS
Any DOS version after DOS 2.1 should work fine for running AnaBat or Analook. However, DOS will not run CFCread (used for downloading storage ZCAIMs) and will not run other Windows-based software in the future (eg a Windows version of Analook).
DOS machines generally have very slow disk access when dealing with folders containing large numbers of small files (like AnaBat files), so downloading can be tedious and in extreme cases may get to the point of being barely practical. Downloading will generally have to be done using the serial or parallel port and a suitable cable and software to effect communication between the DOS machine and another machine.
Analook can usually be run on a DOS machine without any problem, but the slowness of the system will tend to be a disadvantage. Analook does require a VGA screen, which does preclude some of the very earliest DOS machines. 640 by 480 pixels is mandatory, and it is hard to use on a monochrome screen.
Windows 3.1and Windows 95a
A machine running Win3.1 or Win95a should be fine for running AnaBat. With an unbuffered ZCAIM (Types 2 or 5) it will be necessary to shut down Windows and run in Raw DOS mode, but a buffered ZCAIM (Types 6 or Mini) should run OK in a DOS Window, so Windows can still be running in the background. This has the advantage that you can quickly switch between AnaBat and other programs without closing down AnaBat first.
Downloading should have more options than with DOS machines, and using suitable hardware it may be possible to use an Ethernet network connection through a PCMCIA slot.
Analook can usually be run on a Win3.1 or Win95a machine without any problem, though these machines tend to be slower than is really desirabe. Analook is hard to use on a monochrome screen.
Windows 95b and Windows 98
A machine running Win95b or Win98 should be fine for running AnaBat. With an unbuffered ZCAIM (Types 2 or 5) it will be necessary to shut down Windows and run in Raw DOS mode, but a buffered ZCAIM (Types 6 or Mini) should run OK in a DOS Window, so Windows can still be running in the background. This has the advantage that you can quickly switch between AnaBat and other programs without closing down AnaBat first.
Downloading should have more options than with earlier machines. An Ethernet connection should be feasible through the PCMCIA slot, and later machines might even have USB ports. Downloading will be blindingly fast compared to DOS machines. It should also be possible to download to a Compact Flash drive card (such as used in digital cameras) mounted in an inexpensive adaptor to fit into the PCMCIA slot. This is a very convenient way to shift data between different machines, and doesn't suffer from the typical delays in initialising a connection, which tend to plague Ethernet connections.
Analook usually runs fine on a Win95b or Win98 machine, though faster models are better, since Analook may be asked to perform tasks on large numbers of files.
Win95b and Win98 are probably the optimum OS's for the AnaBat application.
Windows ME
This OS is similar to Win98 in features offered, but differs profoundly in not having a native DOS mode. This means that an unbuffered ZCAIM cannot be run on a WinME machine unless another operating system is established so that a Raw DOS mode can be invoked. DOS or an earlier version of Windows could be installed in a dual boot system so that a Raw DOS mode could be made available, but otherwise, it might be necessary to boot from a floppy disk to DOS so that AnaBat can be used to record. The problem with booting from a floppy disk is to make sure that the booted OS can read the hard drive on the laptop. Earlier versions of DOS could not read a FAT32 volume and a WinME system would likely have its hard drive formatted as FAT32. Therefore, it would be necessary to use DOS from either a Win95b or Win98 machine. Fortunately, this is easy to do, since a boot diskette can be made from either OS and then used to boot the WinME machine.
Another possibility is that WinME may itself be able to create a bootable diskette which could be used. I haven't any experience with this possibility - maybe someone out there can tell me if this works or not! Another, more drastic option would be to reformat the hard drive as Fat16, and this could be done using Partition Magic without having to reinstall all your software.
Windows NT, Windows 2000 and Windows XP
These OS's all have a similar ancestry, and they all have the problem that it is impossible for a DOS program to access the hardware while running under Windows. This means there is no way that AnaBat can operate, since it needs to access the Parallel port in order to talk to a ZCAIM, either unbuffered or buffered.
The only way around this (apart from reformatting the hard drive and installing a different OS) is to dual boot the system with either DOS or Win95b/Win98. This should be feasible and should work OK, provided the hard drive has not been formatted with the NTFS file system, which is the natural file system for an NT derivative OS. If the hard drive has been formatted as NTFS, then there is no way for any of the other versions to access it, so the hard drive could not be used. The only solution to this problem is to reformat the hard drive as FAT32, preferrably by using Partition Magic so that your software doesn't have to be reinstalled. It isn't necessary to reformat the whole drive as FAT32, and a reasonable alternative is to make a partition on the drive which is FAT32 while drive C is still NTFS (so Windows can boot). Note that WinNT, Win2000 or WinXP will all run perfectly well with a FAT32 (or even Fat16) file system, but some options would not then be available (such as setting permissions for individual folders).
Analook usually works fine in any of the NT derivative OS's, but there have been a number of problems. Screen problems are discussed above, and seem to worst afflict Win2k machines. Another class of problems concerns access to folders. In some cases, Analook doesn't seem able to see some folders on some drives, or even whole drives, and sometimes drives appear in the Analook directory screens under the wrong drive letter.
Conclusions
An obvious conclusion of all the above is that if you are choosing a laptop for the field, or for that matter a desktop for the laboratory, it would really be very wise to make sure the computer you are considering will work properly with the software you intend to run on it. This is true of any software. You don't want to spend a lot of money on a nice new WinXP machine, only to find the Analook screen is scrambled.
Ask the seller to allow you to run AnaBat and Analook (as required) on any potential machine and make sure it works properly. Also, consider buying a machine someone else has used successfully, though this approach carries risks, because not all machines of nominally the same make/model have the same hardware. This is especially true of video drivers, which are of most concern here.
Weather protection
AnaBat is often used for passive monitoring, when a detector is placed in the field and left there for weeks, months or years without human intervention, except for occasional downloading or servicing. Such systems require protection from the weather, so the electronic components are protected from rain. There is a greater issue here, because rain is not the only hazard - biofouling, vandalism, ice, extreme heat, etc are all factors which can degrade detector performance, or destroy equipment. But this page will concentrate on rain, which is by far the commonest problem. Basically, except for overnight deployments, rain protection is always essential.
Different types of protection
There are several ways your equipment can be protected from the rain. Many users have employed simple cones or umbrella type protection, where the assumption is that the rain will come from a predictable direction and if you orient the microphone away from that direction, or keep it under some kind of shelter, then damage will be prevented. But in most climates, these are not very realistic assumptions, so I will concentrate here on methods which afford a good deal of rain protection without making any assumptions about how heavy the rain will be, or where it will come from. The main methods which have been used are pipes, reflectors and membranes.
Membranes
The concept is simple enough - shield the equipment with some sort of membrane which lets the sound through but keeps the rain out. The advantage of the membrane approach is its simplicity - but it also benefits from the fact that a membrane such as thin plastic (eg "Glad Wrap") does not make a very favorable habitat for the kinds of animals which tend to mess up bat detectors, because the membrane surface is shiny and smooth and doesn't really provide anything very useful from a wild animal's point of view.
The main problem with a membrane is that the sorts of membranes which let sound through well, aren't very good at keeping out rain. Or even if they are, they might stay wet for extended periods after a soaking, affecting sound transmission even more. Tests I have done show that a thin membrane of "GladWrap" imposes about 10 to 20 dB of attenuation. Such attenuation might have relatively little effect on detection of bats which are easy to detect from a distance, because atmospheric attenuation becomes the main factor affecting how far a bat can be detected. But for quieter bats, this could represent a severe performance penalty. Furthermore, if the membrane is close to the microphone, there are likely to be resonances set up because the sound can bounce back and forth between the membrane and the microphone. Such resonances might not have too much effect on bat detection, but they will probably make it more difficult to achieve consistency between different passive monitoring units, because the nature of a resonance is likely to be fairly sensitive to subtle variations in geometry.
Even so, membranes might have a place. In situations where absolute sensitivity is not too much of an issue (and these are quite common - think how often you will turn down the sensitivity of a detector for passive monitoring purposes), and where unit to unit variation is tolerable, the advantages of a simple system which is relatively immune from biofouling might make it attractive.
There are other possibilities. A flywire screen lets sound through well (though I haven't yet done rigorous tests of this), and if the detector is far enough back from the screen, it could afford effective protection from rain, because the rain will tend to run down the screen rather than passing straight through. There may be situations where this very simple approach should be considered, for example where a detector will be housed inside a building with fly-wire screens which are not covered by closeable windows. An example which comes to mind is that many buildings in inland Australia, such as shearers' quarters, often have verandas which are completely enclosed by screens to keep flies and mosquitoes out. A detector placed on a table on the veranda, well back from the screen, will do a tolerable job of recording the local bat fauna straight through the screen. Without any hard evidence, I suspect performance will be best if the detector is oriented at right angles to the screen, so in the above case, the detector should be horizontal and facing straight outwards.
Pipes
Curved pipes have often been used to deflect sound upwards into a microphone which is out of reach of the rain. This approach is simple and effective, and it has been used by many people in many different manifestations.
In the above picture, you can see a simple manifestation which I was able to put together very quickly and at very little cost. The detector and ZCAIM are mounted on a pegboard backplate with a combination of cable ties (for strength) and adhesive backed Velcro tape (to keep things in place better). The pipe has a diameter a little larger than the microphone, which fits snugly into it. The pipe bends gently through 90 degrees. I have found that pipes with abrupt angle changes tend to degrade recording quality. At its lowest point, the pipe contains a small hole drilled through to let any water out. This won't work by itself, because surface tension prevents the water from flowing through the hole. But with a thread of fine cotton running through the hole, it seems to drain effectively. I simply ran the cotton in a loop through the hole and back into the open end of the tube. The whole assembly is wrapped in a thick sheet of polythene which is tied at the bottom in such a way that it protects everything from rain but does not obscure the pipe opening. A point to remember is that the polythene needs to be wrapped tightly and care taken to make sure it can't flap around in a breeze, because that would create a lot of ultrasonic noise. Note that the detector is pointing down at 45 degrees, but its main axis of detection is effectively facing upwards at 45 degrees.
Pipes do affect frequency response by creating little resonances which tend to have more effect at higher frequencies. In one set of tests, I found a significant loss of sensitivity at frequencies above about 40 kHz. Whether or not this is a problem will depend on the range of bats being monitored, and it may vary greatly with pipe geometries. Overall, though, they work well for the frequency range where most species are found, provided the pipe is large enough in diameter and doesn't bend too abruptly.
Reflectors
The most widely used method of weather protection is a simple reflector to deflect the sound up into a microphone which is protected from the rain.
Here's a very simple version which can be put together very quickly and cheaply. All the electronics are placed inside a sealable plastic bucket, with a hole cut in the bottom to let the microphone out near the outer edge. The microphone will get wet in a heavy rain, but only on the outside where it doesn't matter. The reflector in this case is a plastic cutting board, but this one was smoother than most. A cake pan or any other smooth, flat plate could be used. It doesn't matter much what it is made of, as long as it is smooth, flat and sufficiently rigid.
In the Bat Bucket above, the angle of the reflector has been chosen so the main axis of detection (the beam) is facing upwards at about 45 degrees, while the microphone is facing straight downwards. Remember that angle of incidence equals angle of reflection and you can easily figure out the angles. But it is a bit more complex, because you don't want too much of the beam obscured by the bucket, which is why the microphone is placed near the outer edge. The beam varies in angular width with the frequency of the incoming sound, and it doesn't have sharp edges anyway, but in practice, it is useful to think of the beam as 45 degrees wide (22.5 degrees either side of the central axis). You should therefore try to ensure that all of that beam falls onto the reflector plate, and that it doesn't reflect back up into the bucket. This is easiest with a reflector which is at 45 degrees to the microphone axis, and that is the ideal setup if it is desired to end up with a horizontal beam, because then the detector faces straight downwards and the reflector is 45 degrees from the horizontal. But if you want a beam which points up at 45 degrees, it is a little less ideal, because then the reflector is horizontal and the detector points down at 45 degrees. This has two main consequences: 1) if the detector is close enough to the reflector, then rain will actually bounce up off the reflector into the microphone, and 2) a horizontal reflector isn't so good at shedding water and other fouling.
The above diagram shows the geometry for the 45 degree reflector case where the reflector is at 45 degrees to the main axis of the microphone. With the microphone pointing down, the beam ends up horizontal. Note that the upper ray (green) just misses the microphone housing if the geometry is worked out well. By rotating this scheme 45 degrees anticlockwise, you get a horizontal reflector and a beam going up at 45 degrees.
In this case, the reflector is at 22.5 degrees from the horizontal, so a microphone facing straight down ends up with a beam rising at 45 degrees to the horizontal. In this case, the green ray is reflected straight back at the microphone, indicating that some portion of the beam is lost because the microphone and its housing get in the way. Note that the detector needs to be further away from the reflector in this arrangement.
Reflectors work extremely well . There is very little loss of sensitivity along the main axis, though there will be some loss of overall detection volume because not all of the beam is actually useable. There are a few general principles to bear in mind.
1) Greater distances between microphone and reflector reduce the chance of rain reflecting up into the microphone (to a limited extent, rain drops bounce just like sound) and also reduce the shading of the beam by the microphone and its housing. However, greater distances also require larger reflectors, making them less portable and more subject to problems from wind.
2) It s very important that if you have the microphone connected to the detector by a cable, that water cannot run down the cable into the back of the microphone housing. This is very destructive to the microphone!
3) You could use curved reflectors to alter the beam shape. This might have advantages in some cases, but it will also make it more difficult to achieve consistency between units. Many people think they might get advantages by using a parabola, in much the same way that people recording bird sounds will use a parabola. However, in order to do that, you need to match the parabola to the beam shape of the detector (which varies with frequency). A parabola is best suited to a microphone with a broad beam, because a small beam requires a longer distance between microphone and parabola in order to use the whole surface of the parabola. But more importantly, the effect of using a parabola would be to make the beam narrower. This would theoretically allow you to detect bats from further away, but over a narrower angular range, which means the volume of detection might be seriously reduced. Furthermore, for bats which are already detectable over substantial distances, there is little to gain, because increasing the gain in a particular direction achieves little improvement in maximum detection distance, which is mostly affected by atmospheric attenuation. Overall, I don't think there's much point in using curved reflectors, but there may be specific tasks for which it might be useful. It is possible to increase the gain of the microphone by using reflectors or horns, but only at the cost of its beam width.
On the other hand, there might be situations in which it would be advantageous to use a curved reflector (convex to the microphone) to REDUCE directionality. The general principle would be to spread out the central lobe of the microphone's response so that instead of going for distance, it accepts sound from a wider angular range. A smooth cone pointed straight at the detector might perform this function. A sphere would likely be less effective, because much of the central lobe would be reflected straight back into the microphone. But imagine a detector pointing straight downwards at a cone whose tip was pointed straight back at the detector. My guess is that it would need to be a fairly flat cone, and it should occupy about 45 degrees as seen from the microphone, so it intercepts much of the microphone's beam. The effect of such a device will be to reduce the distance at which bats can be detected, but there might still be significant gain in the volume within which bats could be detected. I can imagine this being useful for low intensity bats flying close to the detector, but I haven't tried it.
A word about "cones of reception"
In the diagrams above I have represented the beam as a cone spreading outwards. But this is only true in a very cru
