1 Echolocation in bats and dolphins Chapter 64, pp 484-492

Echolocation in bats and dolphins
Chapter 64, pp 484-492
WHO’S CALLING? - ACOUSTIC BAT SPECIES IDENTIFICATION
REVISED WITH SYNERGETICS
Obrist, Martin K.; Boesch, Ruedi; 1 Flückiger, Peter, F. and 2 Ulrich Dieckmann
Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
1
present address: Fledermausschutz Kt SO. c/o Naturmuseum Olten, CH-4600 Olten, Switzerland
2
Fraunhofer Institute for Integrated Circuits, Am Weichselgarten 3, D-91058 Erlangen, Germany
Introduction
Nocturnal activity, small size, and secretive roosting habits make species of the
Microchiroptera among the most elusive of mammals. As bats constitute about 20%
of mammalian species, the assessment of their occurrence and distribution should be
part of any efforts to evaluate, conserve and monitor biodiversity, but bats are often
overlooked. Some species of microchiropteran bats are however, conspicuous
because they echolocate to detect, track, and assess airborne prey, usually insects.
Since the early years of echolocation research (Griffin 1958) considerable
knowledge and understanding has been collected about ecology, echolocation and
physiology of bats (Busnel and Fish 1980; Fenton et al. 1987; Nachtigall and Moore
1986; Neuweiler 1999). Advancing technology allowed characterization of
echolocation calls of many species. In addition a moderate financial investment in
electro-technical tools allows interested laypeople to identify some species of bats by
their distinctive echolocation calls.
One complication is the fact that bats vary the structure of their calls (Kalko and
Schnitzler 1993; Obrist 1995; Rydell 1993) and can emit signals similar to those of
other species when facing comparable orientational tasks, such as flying close to
clutter. The variety of echolocation calls that any one species of bat produces can
lead to partial or complete overlap of the time and/or frequency structure of calls of
1
other species, making species identification impossible despite the use of very
expensive tape recorders and analytical tools. In an extensive study on the variability
and flexibility of bat echolocation, Obrist (1995) repeatedly discriminated signals not
discernible by standard parameter measurements (highest, lowest, loudest frequency,
duration, interval, etc.) only by the "pattern" or "shape" of the frequency contour in a
spectrogram.
Recent approaches to signal recognition tryed to holistically identify bioacoustic
signals by pattern recognition algorithms, artificial neural networks, and methods like
decision trees in different organisms like birds (Kogan and Margoliash 1998; Mills
1995), frogs (Taylor et al. 1996), whales (Mellinger and Clark 1997), other marine
mammals (Fristrup and Watkins 1995), and in humans (Gish and Schmidt 1994).
Few studies have tackled bats (Gannon et al. in press; Herr et al. 1997; O'Farrell et al.
1999).
Automatic and robust acoustic species identification would be extremely helpful
in a variety of research areas. A method capable of recognizing "patterns" of speciesspecific signals has the potential to identify environmental, behavioural, or individual
differences in sonar or communicative sounds from whales to birds to crickets.
Therefore, it would be a tremendous tool in scientific research and ecosystem
monitoring.
With this project, we wanted to identify all Swiss bat species by their sonar calls
using signal features like variations in sweep rate or shape of sonograms. Even in
such acoustically similar species as the members of the Myotis group, we predicted
that a new pattern recognition approach should allow species recognition.
The study had three goals: 1) find a suitable and economical method to record bat
echolocation calls in the field, 2) evaluate a pattern recognition algorithm with the
potential for fast and automated identification of bat species and optimize it for
correct identification of Swiss bats, and 3) make the whole "recognizer" operable
under field conditions, preferably in an unsupervised, automated mode. This text will
focus primarily on the second topic.
Material and Methods
Recording
Capture of animals
All recordings were made in northern Switzerland between 28 June and 27
September 1995. From 12 species a total of 172 recorded sequences (equals
2
individuals, assuming extremely unlikely rerecording of individuals), comprising 816
calls, were included in the analysis (Table 64.1). Calls were recorded from either
individual bats when emerging from "single-species" roosts or from bats captured at
cave roosts. The latter recordings were made shortly before dawn when releasing the
bats. We tried to minimize environmental noise (wind, leaves, echo clutter) by
choosing an appropriate release location. Only the more species-specific search calls
or commuting calls (Griffin 1958), starting a few seconds after release were recorded,
avoiding the first few very short and rapid calls, typical of bats reorienting after
release. Figure 2 shows waveforms and spectrograms of calls typical of the 12
species recorded.
Signal acquisition
All ultrasonic recordings were accomplished with a D140 ultrasonic bat detector
(Lars Pettersson Electronics AB, Uppsala, Sweden). The detector's built-in electret
microphone picks up the frequencies from 10 kHz to 120 kHz, but sensitivity drops
progressively above 80 kHz. Using the available digital time expansion mode,
signals of 0.87 s where sampled at 350 kHz with 8 bit resolution. Such samples
contained between 3 (Nyctalus noctula ) and 15 (Myotis mystacinus) calls and are
further referred to as a sequence. Replays performed at one-tenth of the original
sampling rate were recorded using a Sony WM-D6C Walkman Professional for
subsequent analysis in the laboratory.
Direct recording
To allow future field use of our setup, we also implemented software on a
Macintosh PowerBook computer to make direct recording of ultrasound signals
using a high speed analog-to-digital converter PCMCIA-card. Recording and pattern
recognition algorithm were integrated in one software package, BATIT (BioAcoustic
Taxa Identification Tool), and the provided interface to the Macintosh scripting
language (AppleScript) allows one to implement an automatic acoustic "specieslogger".
Conventional signal analysis
Cassette recordings were digitized using an Apple Macintosh PowerPC 8500
with 22.05 kHz sampling rate at default 16-bit sampling depth. After extraction with
the automated algorithm (see Preprocessing, below) signals submitted to the
synergetic computer (SC, see below) also were analyzed with Canary software
(Charif et al. 1995) to measure parameters from the sonograms (Table 64.1). Time
3
and frequency measurements (duration, DUR; lowest frequency, LFR; highest
frequency, HFR; frequency of maximum energy, MFR) were taken from
spectrographic representations of the calls. Spectrograms contained temporally
overlapping windows, which possibly led to systematic, but minimal error in
measuremnts of call durations (time-smearing) when compared to published signals
of the species in question. However, analysing a spectrographic display was
preferred over analysing an amplitude display and a spectrum display, because lowintensity parts of calls invisible in the latter, led to decreased readings for high
frequency, low frequency and duration.
Signal analysis with a synergetic computer
Pattern recognition
For the identification of bat calls we used the classical pattern recognition system.
It consists of several modules (FIG. 64.1.). In general, the system is able to learn a
representative set of calls from each species. Using these sets, the system assigns
newly recorded calls of an unknown species to one of the learned species. The
output consists of a position table on which the most likely species is on top followed
by other possible species in decreasing order of probability.
Preprocessing
Call sequences stored in binary format with Canary or BATIT can directly be
input to preprocessing and the classification algorithm of the SC. Recorded
sequences show three main different parts: the call itself, the echo (not always
present) and noise. Although the intercall interval or repetition rate can be speciesspecific in some bat populations (Fenton and Bell 1981), we neglected this parameter
and concentrated on analyses of single vocalizations for the following reason:
preliminary tests with short time Fourier transformations (STFT) of whole sequences
showed bad recognition results because a) too much noise was included, b) the time
interval between two consecutive calls often varied too widely, c) signal characteristics
of echolocation calls did not resolve enough in the available spectrogram resolution
(memory restrictions), d) long silent intervals dominated spectrograms of complete
sequences, leading to overall less discernible segments. To analyze and classify, it
was therefore necessary to select each call out from a sequence.
Call extraction
Peaks were identified in a signal previously smoothed with a 256-point moving
4
average filter. To ensure a good signal-to-noise-ratio and the detection of all suitable
calls, only values 16.9 dB in amplitude (10*log(50)) above the average signal level
were considered as peaks. This level was commonly encountered in quiet recording
situations, or achieved in noisy recordings by high-pass filtering above 10 kHz.
After detection of a peak, 3072 samples before and 5120 samples after the peak's
position were extracted, thereby creating short signals of 8192 samples.
Separating the actual call from its echo was attempted in some species (e.g.
Nyctalus noctula in FIG. 64.1). All modes of manipulation such as filling in with
noise or cutting before echo introduced artifacts. This led us to leave extracted
signals untouched. Therefore, the extracted calls often contained a considerable
amount of interfering echos from ground- or water reflections, and ambient noise,
wind, Doppler shifts and other interferences (Pye 1993).
Fourier Transformation
After detection, the STFT for each call was calculated (amplitude spectra =
sqrt(real*real + imag*imag), 256 samples, Hamming window, 81% overlap). These
calculated windows were composed to one feature vector of 20352 floating point
numbers normalized to 1, which is equivalent to a spectrographic display of 159
spectra each containing 128 data points (see FIG. 64.2). Phase was therefore
neglected from analysis and only magnitude considered.
Synergetic Computer
Synergetics is an interdisciplinary field which deals with self-organizational
phenomena in nature (Haken 1978; Kohonen 1984). These phenomena have in
common that many microscopic parts in an unsorted order (chaos) transform
themselves in a sorted order. The importance of each part is minor; only the
properties of the whole system are relevant and can be described through synergetic
differential equations.
The synergetic computer (SC) is not an actual computer per se, but a new set of
algorithms emanating from this interdisciplinary field, and has only recently been
used for classification tasks (Haken 1988; Haken 1996; Wagner et al. 1993; Wagner
et al. 1995). For the classification of bat calls we used an algorithm termed SCMELT (Dieckmann 1997). One significant advantage of this algorithm is its ability
to combine several training patterns per class into one feature vector without losing
any information about the training patterns. The training patterns are melted into one
prototype. The prototype has the same dimension as the training vectors and is
normalized to length 1. Because of this ability the SC can handle big dimensions in
5
contrast to artificial neural networks (ANN). ANN also can handle big dimension,
but the computational power needed to train an ANN with input vector of 16384
features is prohibitive. Description of the synergetic computer using adjoint
prototypes (SCAP) are given in Hogg (1996) and Wagner (1993). The adjoined
prototypes of the SC-MELT are achieved through simple addition of the
corresponding, adjoined prototypes. One of the most interesting properties of the SC
is that it emphasizes pattern contents that are unique among all others and it
diminishes of pattern contents common to all others. The learning time of the SC is
easily determined and very fast. The generation of the prototypes of the bat calls
takes only a couple of minutes on a 200 MHz 603e RISC processor (such as an
Apple Macintosh PowerBook 3400). The classification is even faster because it is
simply a scalar or dot product.
Classification
Assume we let the SC training 3 calls of each of 10 species (classes), resulting in
10 prototype feature vectors. We now test 50 echolocation calls of an unknown
species contained in the training base. The SC computes the scalar product of each
test call with each class prototype, resulting in 10 values per call, varying between 1
(identical call as in training base) and 0 (no resemblance to any of the training calls).
The training class with the highest scalar product identifies the species where the calls
most likely came from. Some signals invariably get assigned to wrong species,
resulting in a frequency distribution over the 10 classes. The peak in the frequency
distribution finally identified the best fitting species (FIG. 64.4 A).
Dependencies of recognition success
To evaluate the effect of training call selection on recognition success, we repeated
the training and classification process 115 times with our data set. Calls included in
the training base were drawn from 1 to 9 sequences. The number of calls per
sequence was varied from 1 to 5. Each combination of sequence-call selection was
repeated 5 times with different sequences and calls each time. These selection criteria
were restricted by available computer memory, not allowing for more than 12 calls
per species (144 training calls). For two species (Myotis blythii and Myotis
bechsteini) we had to use identical training bases repeatedly due to a lack of
recordings. For each of the resulting 115 repetitions, we counted for every species
the number of correctly assigned calls, the number of misclassified calls and whether
the correct species was the most common classification or not. These classification
results were analyzed by multivariate analysis of variance (MANOVA) for the effect
6
of number of sequences, number of calls, and number of calls per sequence on
recognition success.
As described above, the higher the maximum scalar product, the clearer the
recognition of a tested call. Similarly, a great difference between the highest and
second-highest scalar product indicated a clear differentiation between classes. Both
parameters, maximum scalar product (MSP) and difference to second-best (DSB)
were used to delimit the number of valid calls. We systematically tested all
combinations of the MSP (0, 0.4, 0.5, 0.6) and the DSB (0, 0.1, 0.2, 0.3), to reject
calls from the classification. Similar to a rejection criterion like signal-to-noise-ratio,
this eliminated a number of calls from the original base of calls, thereby changing the
number of cases in subsequent analyses.
Statistical analysis
Using DataDesk software (Data Description Inc., Ithaca, New York, USA) we
performed a linear regression analysis to test whether the increase in delimiting
parameters (MSP, DSB) improved classification. The influence of species, number
of learned calls, and number of sequences (individuals) on the classification was
tested using ANOVA. To compare the identification abilities of the SC, we
performed multivariate analyses on the four parameters measured with Canary. We
used DataDesk for a MANOVA with subsequent post-hoc tests (Sheffé) to separate
means of species’ call characteristics. With the same software we performed a single
linkage cluster analysis. SAS (SAS Institute Inc., Cary, NC, USA) statistical
software was used for a discriminant function analysis with reclassification for a
cross-validation. A significance level of p ≤ 0.05 was applied in all statistical tests.
Results
Cluster analysis
The cluster analysis clearly separated Nyctalus noctula from the other species due
to the bandwidth and the duration of its calls (compare spectrograms in FIG. 64.1).
Pipistrellus pipistrellus also stands apart from the other species, but less clearly.
Vespertilio murinus, Eptesicus serotinus, and Plecotus auritus are separated from the
Myotis (FIG. 64.3).
Multivariate analysis of variance
MANOVA showed significant variance in all four call parameters. The Scheffé
post-hoc test separated most species by one or more of their mean call characteristics,
7
most often by MFR and LFR followed by HFR and DUR, reflecting increasing
variability (CV) in these parameters (Table 64.1). However, Myotis mystacinus, M.
bechsteini, M. blythii, and M. brandti were difficult to separate just by the
measurements taken with Canary. The reason remains unclear for the first species,
but it might be the relative small sample size available from the latter three species (5,
16 and 19 calls respectively; see Table 64.1).
Discriminant function analysis
Using only parameters in Table 64.1, discriminant component analysis properly
identified 46% of all Myotis-calls and 60% of all species’ calls, when splitting the
data pool in half and testing the second half. Considering only non-Myotis species, a
recognition success of 82% was achieved. Splitting the data pool in four parts and
testing the remaining three quarters of the signals recognition success deteriorated to
45% (Myotis group), 58% (all species) and 78% (non-Myotis species).
Synergetic computer
Using the synergetic computer to classify patterns of spectrograms of
echolocation calls was surprisingly effective. In some cases a single call was
sufficient to characterize the species, as seen in FIG. 64.4. However, most species
showed considerable variations in call contour (see CV in Table 64.1). Accordingly,
at least five, optimally chosen vocalizations had to be learned from every species to
cover a reasonable part of the flexibility and make classification reliable and results
consistent.
We performed a variety of exploratory statistical analyses to evaluate the
influences of different variables on classification. In the remaining statistics Myotis
bechsteini, Myotis blythii and Myotis brandti were not considered, because too few
recordings from these species were available for statistical analysis.
As expected, ANOVA indicated a highly significant influence of species on the
classification success. Averaged over 115 trials with 1 to 9 training calls per species,
Nyctalus noctula scored best with 75% correct classifications while calls of Myotis
daubentoni had a correct classification rate of only 32%. Example results of tests
with 5 and 9 training calls are given in Table 64.2.
Classification success (percentage of correctly classified calls) increased with the
number of calls in the training base, except in Nyctalus noctula, where classification
success reached a plateau at 100% with only 1 training call. The general impression,
that the increase in success rate started to level out towards 9 training calls per species
could not be further investigated due to lack of recordings and computer memory
8
restrictions (FIG. 64.5, FIG. 64.6).
In all species, the recognition success was dependent on the number of sequences
included in the training base, probably due to the inclusion of more variability
occurring among recordings of different individuals.
Increasing the number of training calls drawn per sequence leads to a marginal
increase in recognition success, underlining high inter-individual and lower intraindividual variability in echolocation calls (Obrist 1995). Recording and hardware
restrictions again prohibited further analysis; it was not known wether increasing the
number of calls per sequence would saturate or even decrease recognition success at
higher training call numbers.
We used the maximum scalar product (MSP) and the difference of the secondbest scalar product (DSB) to evaluate a combination of both which achieves highest
classification results with lowest rejection of calls. For all species, any increase in
MSP (0, 0.4, 0.5, 0.6) and any increase in DSB (0, 0.1, 0.2, 0.3) resulted in a
significantly higher recognition success (p ≥ 0.001; calls of Myotis blythii and Myotis
bechsteini not considered), at the cost of rejecting a number of calls (FIG. 64.5, FIG.
64.6). While 57% of all calls were correctly classified (0% call rejection) when MSP
and DSB were both set to 0, average recognition rate increased to 62% (with 23%
rejection) for MSP > 0.4 and DSB > 0.1. Even more (71%) were correctly classified
with MSP > 0.6 and DSB > 0.3, but with these settings 56% of all calls where
rejected. Judging from these results, we considered a combination of MSP > 0.5 and
DSB > 0.2 as adequately delimiting rejection criteria for a call, offering the best
compromise between recognition success and call rejection. Using 9 training calls per
species and applying the above rejection criteria, we reached an average recognition
rate of 80% at the cost of rejecting 33% of all detected signals. For reasons of call
similarity (FIG. 64.1), this result was slightly different between Myotis and nonMyotis species. Increasing scalar product criteria further lead to a loss of more than
half the data recorded.
Discussion
Using a synergetic computer for pattern recognition, we identified calls of 12 bat
species from spectrograms of their sonar calls with variable, mostly high, degrees of
success. Recognition success varied with species and with the size and variety of the
training base. Applying various restrictions helped to improve classification results.
Classical recognition tasks
Since Griffin first reviewed bat echolocation (1958), species have been known to
9
differ in the structure of their vocalizations. Many publications have since dealt with
the identification of echolocating bats by their calls (Ahlén 1981; Fenton and Bell
1981; Vaughan et al. 1997b; Zingg 1990). Despite the fact that Griffin (1958)
observed plasticity in call structures of individual bats, this topic only recently has
received extensive attention (Betts 1998; Obrist 1995; Rydell 1993; Zbinden 1989).
Variability is one problematic aspect of acoustic monitoring, the training of qualified
personnel with detectors yet another. The intensity of vocalizations can vary within
30-50 dB between bat species. Therefore, some species will invariably be more
conspicuous than others and are over-represented in any acoustic monitoring study.
Comparing mist-net catches and acoustically recorded bat passes can show
corresponding activity patterns in temperate regions (Kunz and Brock 1975) but this
seems not to be the case in southern Africa (Rautenbach et al. 1996). Nevertheless,
acoustic monitoring coupled with the ability to distinguish species, makes monitoring
studies feasible (Fenton 1970; Hickey and Neilson 1995), at least in temperate
regions and when counting all Myotis as a single species. Several thorough studies
proved the potential of acoustic monitoring of habitat use by bats, as well as its
limitations (e.g. Furlonger et al. 1987; Vaughan et al. 1997a). Limitations include the
necessity to have trained personnel spend long nights in the field, and the inability to
separate species within the genus Myotis or similarly calling groups (e.g.
Phyllostomatidae) in the field. Myotis contribute significantly to biodiversity: nine
out of 25 bat species (36%) occurring in Switzerland (Hausser 1995) belong to
Myotis. Only recently was an acoustic identification of Myotis-species by statistical
analysis achieved (Vaughan et al. 1997b).
Pattern recognition approaches
In the last decade there has been an increased interest in automatic recognition of
acoustic patterns. Human speaker identification is most prominent and voluminous
in literature (Gish and Schmidt 1994), and has mainly commercial aspects, in contrast
to the identification of species (Mills 1995; Taylor et al. 1996) or behaviours
(Mellinger and Clark 1997; Potter et al. 1994). Species recognition approaches
generally use decision trees (Quinlan 1993) or artificial neural networks. Synergetic
recognition algorithms have so far been applied to speaker recognition (acoustic and
visual, Dieckmann 1997) or industrial part identification tasks (Wagner et al. 1993)
with impressive results. The ability to learn test patterns unsupervised and to classify
full spectrograms at high rates makes synergetics a promising technique for animal
vocalization recognition.
10
Statistical analyses can reach high recognition rates for non Myotis
speciesVespertilionids. Zingg (1990) achieved 86% using 5 signal parameters,
Vaughan et al. reached 89% (1997b, 67% for Myotis) using 6 parameters, including
the call interval. Using only 4 call measures, we achieved comparable recognition
(82%) with the discriminant function analysis. For the genus Myotis, performance
droped below 50% in our tests. However, the a–priori probability of identifying the
proper species by chance is only 8%.
The success rate of the SC compares favorably to these numbers. When
carefully choosing the learning base and rejecting ambiguous calls, the SC reached
average call recognition rates of 77% for the genus Myotis and 84% for other
Vespertilionids (average 80%). It is possible to improve these results further. First,
digital recording of the ultrasound as 12-bit data will substantially improve signal
quality. Second, the preparation of the spectrogram (window size and shift) could be
further optimized for the SC, and, with more available RAM we will be able to train
the SC with more species, and better evaluate the effect of increasing call numbers per
species. Finally, the inclusion of the intercall interval in the species’ characterization
could further improve performance. Based on results from studies using similar
algorithms in industry (99% recognition, Wagner et al. 1993) and "human"
applications (93% recognition, Dieckmann 1997), and taking into consideration the
substantial variability occurring in echolocation (Obrist 1995), we hope to reach an
overall bat species recognition success of approximately 90%. Only then, do we
intend to make the SC system available to fellow scientists.
For the characterization of, and the quantitative comparison among species or
behaviorally specific call characteristics, standard statistical analysis of measured call
parameters remains the only choice. But, for monitoring purposes, the pattern
recognition approach rivals this method and certainly is faster and more economical.
We estimate a total throughput (cut calls, calculate spectrograms, classify, write
output) of 5-6 calls per second on a current Macintosh laptop. As this is roughly the
rate at which an average bat emits vocalizations the system approximates real time
performance, but lags behind real time by the duration of the recording plus the
processing time. If none or only some of the signals are kept for later reference, the
system could autonomously log species-specific bat activity for a number of nights,
powered by a portable 12 V battery. With falling prices of increasingly powerful
laptop computers and the availability of high-speed PCMCIA data acquisition cards,
direct field recording of ultrasound to computer hard disc will become economic and
widespread in the very near future.
11
Our proposed system constitutes a so-called "black box". The user inputs
signals and the machine identifies the emitter of the sounds. Many field workers will
object to this approach because no control can be attained over species classification.
We do not encourage inexperienced people to feed data into our system without prior
knowledge of bat echolocation calls and inherent variability. Special care has to be
taken when compiling a valid training base for the synergetic computer. Considering
geographic variations of sonar characteristics, compilation of a single training base
for a certain species will likely never be feasible. Rather, a variable approach will be
more realistic, where qualified observers compile their own database for their regional
coworkers. However, our system is attractive because of its ease of operation, cost
effectiveness, and the potential for automated monitoring of any acoustically
conspicuous species assemblage.
Acknowledgments
We are very grateful to Lars Pettersson for his generous help with recording
hardware.
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15
TABLE 64.1._ _ _ Recording statistics. Abbreviation of species names (ab), sample size n (seq = number of sequences; call = number of
calls included in analysis) and call characteristics of the 12 recorded species. Mean ± standard deviation, as well as coefficients of
variation (cv), are given for the following parameters: DUR = call duration, LFR = lowest frequency in call, MFR = frequency of main
(highest) energy in call, and HFR = highest frequency in call. Species indicated with an asterisk have low sample sizes.
Call parameter
n
DUR (ms)
SPECIES
ab
seq
call
mn±sd
Eptesicus serotinus
ES
21
80
7.2 ±1.7
Myotis bechsteini *
MB
1
5
Myotis blythii *
ML
3
Myotis brandti *
MR
Myotis daubentoni
cv
LFR (kHz)
MFR (kHz)
cv
HFR (kHz)
mn±sd
cv
mn±sd
mn±sd
cv
24%
25.5 ±1.3
5%
30.3 ±2.1
7%
57.0 ±4.4
8%
4.1 ±0.1
3%
23.2 ±0.9
4%
45.2 ±1.4
3%
96.5 ±4.5
5%
16
2.8 ±0.9
32%
27.7 ±6.0
22%
41.4 ±5.1
12%
72.0 ±14.5
20%
3
19
3.4 ±0.7
22%
27.3 ±1.5
6%
43.4 ±2.8
6%
83.1 ±11.3
14%
MD
32
146
3.8 ±0.8
20%
27.2 ±2.0
7%
42.2 ±2.8
7%
76.1 ±10.8
14%
Myotis myotis
MM
32
155
4.0 ±1.2
30%
23.7 ±2.6
11%
37.0 ±3.6
10%
77.1 ±13.3
17%
Myotis mystacinus
MY
10
60
3.1 ±0.9
30%
27.8 ±2.7
10%
45.0 ±4.0
9%
85.2 ±9.0
11%
Myotis nattereri
MN
27
149
3.0 ±0.8
28%
17.4 ±4.6
26%
33.7 ±7.0
21%
78.0 ±14.4
18%
Nyctalus noctula
NN
12
24
13.0 ±3.0
24%
21.5 ±1.4
6%
24.2 ±1.7
7%
32.5 ±8.6
26%
Pipistrellus pipistrellus
PP
10
43
5.4 ±0.9
16%
42.5 ±1.0
2%
45.7 ±1.4
3%
84.6 ±8.6
10%
Plecotus auritus
PA
15
90
3.3 ±0.9
27%
23.0 ±1.8
8%
33.3 ±4.4
13%
53.2 ±2.8
5%
Vespertilio murinus
VM
6
29
5.5 ±1.8
33%
22.5 ±1.0
4%
28.7 ±2.5
9%
49.0 ±5.3
11%
TABLE 64.2._ _ _ Average percentage of correctly classified calls relative to the total
number of calls passing the delimiter restrictions. Results are shown for 5 and 9
training calls with delimiter restrictions MSP > 0.5 and DSB > 0.2. The last column
indicates the percentage of all calls rejected by the delimiter restrictions. Species
indicated with an asterisk have too low sample sizes to be conclusive (see also Table
64.1).
% correctly
classified
Nr of training calls
MSP / DSB
% rejected
5
9
9
0/0
0.5 / 0.2
0.5 / 0.2
Species
Eptesicus serotinus (ES)
42
60
34
Myotis bechsteini (ME) *
74
94
77
Myotis blythii (ML) *
64
100
3
Myotis brandti (MR) *
31
90
18
Myotis daubentoni (MD)
18
42
43
Myotis myotis (MM)
16
53
51
Myotis mystacinus (MY)
34
64
42
Myotis nattereri (MN)
39
93
30
Nyctalus noctula (NN)
71
98
17
Pipistrellus pipistrellus (PP)
60
93
16
Plecotus auritus (PA)
36
80
42
Vespertilio murinus (VM)
58
90
19
Avg. Myotis
39
77
38
Avg. non-Myotis
53
84
26
Average %
45
80
33
Figure captions
FIG. 64.1._ _ _ Sonograms and waveform displays of representative calls of the 12
bat species under consideration. Calls are arranged according to the apparent
similarity of their spectrograms.
FIG. 64.2._ _ _ Schematics of the data processing, using synergetic computer
explained in text.
FIG. 64.3._ _ _ Single linkage cluster analysis of species according to mean call
parameters DUR, LFR, MFR and HFR.
FIG. 64.4._ _ _ Histogram of classification results: the percentage of calls assigned
to the various species are indicated. In this run, 82 of 148 test calls recorded from
Myotis nattereri (55%) were assigned to the correct species. Only 1 call from each
species was used as training base. Species abbreviations as in Table 64.1.
FIG. 64.5._ _ _ Three example results of the classification of 2 species‘ calls
(Eptesicus fuscus and Myotis myotis) with the synergetic computer. 1, 5 and 9 calls
were used to train the algorithm. When including 5 or more calls in the training base,
the maximum identification score is always (also in the other 10 species not shown
here) at the correct species (horizontally striped bars). Misclassified calls (vertically
striped bars) most often fall into classes of species, which proved very similar in the
cluster analysis (FIG. 64.3). Species abbreviations as in Table 64.1.
FIG. 64.6._ _ _ The effect of varying numbers of training calls (all taken from
different sequences), and delimiter values (MSP, DSB) on classification performance
and rejection of calls. Due to the limited number of recorded sequences and computer
memory restrictions, N varies: 25, 10 and 5 runs are averaged for 1, 5 and 9 training
calls respectively.
Frequency [kHz]
100
Nyctalus noctula
Ves pertilio murinus E ptes icus s erotinus
P ipis trellus
pipis trellus
P lecotus auritus
Myotis brandti
Myotis myotis
Myotis nattereri
50
0
Myotis daubentoni
Myotis bechs teini
Myotis mys tacinus
Myotis blythii
Amplitude [V]
+0.5
0
-0.5
0
5
10
Time [ms]
15
Fig. 64.1
pattern recognition system
preproces s ing
smooth signal
s ynergetic computer
locate peaks
Calling bat
data
recording
classifier
extract single calls
short-time Fourier
Transformation
combine windows
feature
extraction
learning
species
identity
prototypes
Fig. 64.2
Calls from Myotis nattereri(MN)
80
60
40
20
VM
PP
PA
NN
MY
MR
MN
MM
ML
ME
MD
0
ES
Percent classification
100
Species calls were assigned to
Fig. 64.4
Calls from Eptesicus serotinus (ES)
Percent classificatoins
100
80
60
40
20
PP
VM
PP
VM
PA
NN
MY
MR
MN
MM
ML
ME
MD
ES
0
Species, to which calls were assigned to
Calls from Myotis myotis (MM)
80
60
40
20
PA
NN
MY
MR
MN
MM
ML
ME
MD
0
ES
Percent classificatoins
100
Species, to which calls were assigned to
Percent correct
classification
Percent wrong
classification
1 learn-call per species
5 learn-calls per species
9 learn-calls per species,
with scalar product restrictions
Fig. 64.5
no delimiters
delimiters MSP > 0.5, DSB > 0.2
reject
Percent correct identified / rejected
100
80
60
40
20
0
1
5
number of training calls
9
Fig. 64.6