BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
TIM LUCAS
sing a machine learning framework to construct an automatic, acoustic classifier for bats is a
research and conservation priority for this large and often vulnerable group of animals. Here I
use a large dataset of 1,340 calls from 34 of the 40 European species to construct a Gaussian
process classifier. I obtain accuracies of 54.6% at the species level and 83.6% at the genus level for the
two best Gaussian process classifiers. These classifiers are less accurate than nearest neighbour or neural
network classifiers. A hierarchical Gaussian process classifier is suspected to have accuracies comparable
to neural networks — this would be a profitable avenue for future research.
U
Introduction
he ability to acoustically detect and identify bats (Order: Chiroptera) is a major goal in bat
conservation. Such a system would allow the detection of bats automatically without physical
handling or roost disturbance. The large amounts of data this would make available is vital for
our understanding, and therefore protection, of these highly vulnerable animals.
Bats are the second largest order of mammals 1 with 40 species native to Europe, five species of which
are vulnerable or endangered. 2 All species are extremely sensitive to handling and roost disturbance
— this is exacerbated by the fact that they often use human buildings as roosts. Also, as they are
migratory, an effective protection strategy must be continental in scale. Furthermore, they may be useful
as an indicator group, so knowing the health of bat populations will also give proxy information on the
health of other taxa. 3 They are, therefore, a conservation priority. This is reflected in the eminence of
the Bat Conservation Trust and the fact that the EuroBats agreement is one of only 11 pan-European,
taxon-specific conservation agreements. 4
Bats pose particular problems to the field biologist. They are small, fast flying and nocturnal. There
are a large number of species, many of which are similar in appearance; in flight only one European
species can be positively identified. To be identified reliably they must therefore be handled but contact
and roost disturbance is a major contributer to their population declines 1 and is now strictly regulated
by the EuroBats agreement. 5
All European bats are in the suborder microchiroptera (as apposed to megachiropteran fruitbats) and
therefore all navigate by echolocation. This provides a potential signature that may allow identification
of bats to species level wthout harmful and time-consuming trapping and handling. However, there are
two difficulties inherent in this approach. Firstly, the echolocation of most bat species is adapted to
similar needs and environments and so the calls can be quite similar. This is in contrast to many bird
groups whose calls are under disruptive selection due to their function in identification for mating. 6 The
second issue is that there is a large amount of call variation within a species; between individuals and
within a single individual. 7–9 It is possible that there is simply not enough information in the calls to
fully distinguish between species and it is likely that classification accuracy will never be perfect.
The use of machine learning to acoustically identify species is an inherently inter-disciplinary research area drawing on expertise from statistics and computer science to solve important problems in
ecology, conservation, evolution and other areas of biology. 10 This is the first time Gaussian process
learning has been used for a species classification task. However, many authors have developed other
machine learning models to identify bats. Discriminant function analysis 11–17 and artificial neural networks (aNNs) 12,14,16–20 are the two most commonly used methods although other methods have been
used. 14,16,17,21 With only a few exceptions, 14 aNNs are found to be the most effective method. Machine
learning has also been used to classify a wide variety of other taxa such as frogs 22,23 , birds 22 , trees 24–26 ,
fish 27 and pinnipeds 20 to species or individual level. Methods are relatively applicable between taxa and
so this work gives insight into the utility of Gaussian process learning in species classification in other
taxa as well as bats. Gaussian process learning specifically has found applications in biology including
T
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
2
classification of canopy types 28 , molecular regions and characteristics 29,30 , assignment of post-operation
treatment regimes 31 and bird flight patterns. 32
Walters et al. 33 created ensembles of neural networks (eANNs) using the same dataset as used in this
paper and achieved high accuracies (85.5% for genus and 98.1% for species level) with classification within
the genus Myotis being difficult. This classifier was hierarchical with the data being classified into one of
five broad call types with one neural network and then classified to species or genus level with seperate
ensembles. The call types relate to the broad shapes of the calls. Within each genera, all the species are
of the same call type. Therefore this classification reflects bat phylogeny and evolutionary history.
The classifiers in this report and in Walters et al. 33 attempt to classify 34 species of bat. The next
largest ensemble of bat species a classifier has attempted to identify between is 22 species. 13 This work
achieved accuracies of 81.8% and 94% for species and genera respectively. Even a classifier of this size is
of little use as it does not include all the species found in the region of Southern Italy where the study
was performed. It is a difficult task to collect enough data to train a classifier on more than 20 classes
and even within data-rich species, classification is not easy. The dataset used in this paper includes all
relevant species of European bat (but see the Methods section) and so any classifier built can be used in
the field anywhere in Europe.
Bayesian Gaussian process machine learning is a relatively new machine learning technique. It has
been shown to be as effective as aNNs, which have emerged as a favourite machine learning classification
method. 34 Indeed, aNNs have been shown to become equivalent to Gaussian process models as the number
of hidden units tends to infinity. It is an explicitly statistical method, in contrast to the more computer
science based methods such as neural networks.
The method uses Gaussian processes as a function that maps the trait values to a probability for
each class. If Gaussian distributions are considered as the noise around a point, Gaussian processes can
be seen as the noise around a function. When formulated correctly they can be amenable to Bayesian
analysis so that the data is used to inform the distribution over the function.
Gaussian process learning has a number of advantages over aNNs. Firstly, it can be computationally
faster than aNNs, taking hours instead of days — for a dataset as large as the one used here — to train a
model on a desktop computer. However, computation time and memory requirements are still a limiting
factor (due to it’s cubic scaling with both number of classes and number of datapoints) which has led to
much work in finding approximations and computational optimizations.
Secondly, the flexibility in choice of covariance function allows control over a number of factors such
as a) the smoothness of the probability distributions can be a safegaurd against overfitting or b) a
covariance function with different length scales for each input dimension can enable automatic relevance
determination (ARD).
Finally, the output from a Gaussian process model is the probability of an unknown record belonging
to each class. This has a number of advantages to the single class given as output from an aNN. One
knows the confidence of a prediction which allows the user to set a threshold confidence below which a
‘null’ result is given. The output also includes a ‘second best guess.’ sANNs and k nearest neighbour
methods can have similar properties by having multiple outputs and voting for the final output. However,
any measure of confidence calculated is only relevant within the context of the model it is from and cannot
be easily compared to other models; in the simplest instance a model which takes a vote from only three
outputs will always appear more confident than one that considers many outputs. This is in contrast
with Gaussian process learning where class probabilities have a rigorous, statistical underpinning.
Methods
Data The data 33 was collected from EchoBank 35 and contains 1,340 calls from 34 of the 40 European
species of bats. This includes all mainland species except two (Plecotus kolmbatovici and Plecotus alpinus)
which have very low intensity calls and so are not usually picked up by acoustic recording equipment.
Four island endemic species (Plecotus sardus, Plecotus teneriffae, Nyctalus azoreum and Pipistrellus
maderensis) are also not included. In practice as long as the classifier is not used in the Canaries, Azores
or Sardinia these species will not affect the reliability of the classifiers. The dataset has 100% coverage
in 93.9% of Europe. 33
SonoBat 36 was used to isolate calls from each recording. A minimum of 26 calls from each species
was collected. When possible, only one call was taken from each recording to prevent pseudoreplication
of individuals but this was not always possible. Overall, between 26 and 104 calls were taken from each
species (see Table 1) with half the data used for training and half for testing in all cases. Due to the
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
Genus
Species
Barbastella
Eptesicus
barbastellus
bottae
nilssonii
serotinus
savii
schreibersii
alcathoe
bechsteinii
blythii
brandtii
capaccinii
dasycneme
daubentonii
emarginatus
myotis
mystacinus
nattereri
punicus
lasiopterus
leisleri
noctula
kuhlii
nathusii
pipistrellus
pygmaeus
auritus
austriacus
blasii
euryale
ferrumequinum
hipposideros
teniotis
murinus
mehelyi
Hypsugo
Miniopterus
Myotis
Nyctalus
Pipistrellus
Plecotus
Rhinolophus
Tadarida
Number of occurances in Output
153
11
15
53
9
5
11
8
2
10
7
12
6
8
4
15
8
14
11
27
23
30
4
27
50
16
14
11
9
17
21
18
12
25
Sample Size in
Training Data
14
13
14
40
16
15
13
22
13
23
15
13
25
13
13
27
33
13
13
38
23
23
13
23
52
20
13
13
13
15
26
17
13
20
3
Accuracy (%)
85.7
61.5
78.6
80
43.8
33.3
38.5
9.1
15.4
21.7
20
69.2
12
7.7
30.8
11.1
18.2
46.2
84.6
50
73.9
82.6
23.1
87
86.5
50
46.2
84.6
69.2
93.3
69.2
100
46.2
85
Table 1. Species specific statistics for the Gaussian process classifier with squared exponential
covariance function. Data columns show the number of records predicted as each class, class
specific sample size and accuracy.
different number of species in each genus there is large variation in the number of records per genus
(Table 3). Myotis has 224 records while five genera have less than 20. SonoBat was used to extract 24
frequency parameters from each call. F-ratios were used to select 12 parameters that were most likely to
be useful in discriminating between species. Classifiers are created using the training data D = {xi , yi }
containing i input vectors, x — each of length N where N is the number of parameters used — and i
outputs y ∈ c where c = {c1 · · · cK=12 } is the set of possible classes.
Models and Validation In this project I used the dataset to construct nearest neighbour classifiers and
Gaussian process classifiers. Neither method used a hierarchical framework for classification. Both the
nearest neighbour classifiers and previously constructed hierarchical eANN 33 were used as benchmarks
for the Gaussian process classifiers. I created classifiers to identify individuals to species and genus level.
In the eANN 33 and all models below, half of the data — half of each class selected randomly — was used
as training data while half was used as test data.
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
Classifier
Species Accuracy (%)
Nearest neighbour
63.5
Gaussian Process: ARD exponential
49.4
Gaussian Process: Neural network
36.3
Gaussian Process: SE
54.6
4
Genus Accuracy (%)
85.7
83.6
81.4
79.2
Table 2. Accuracies for nearest neighbour and Gaussian process classifiers.
In all cases accuracy is measured as the proportion of correct classifications or ‘hits’. Thus for a whole
classifier
Accuracy =
# Correctly Classified
# of Test Datapoints
(1)
while specific accuracy for class cK within a classifier is calculated as
AccuracyK =
True Positives
# Correctly Classified as cK
=
# of Test Datapoints in cK
True Positives + False Negatives
(2)
While this does not consider the differing importance of false positives and false negatives it is a reasonable
metric, especially for multi-class problems where metrics such as sensitivity and specificity are difficult
to interpret. Random classification would yield an expected accuracy of 2.9% and 9.1% for the species
and genus task respectively.
Nearest Neighbour Nearest neighbour classifiers were made using R 37 and the kknn 38 and class 39
packages. Three groups of classifiers were built: unweighted k nearest neighbour, weighted k nearest
neighbour with a linear kernel and weighted k nearest neighbour with a Gaussian kernel.
For each test data point xj∗ we calculate the euclidean distance dij , in 12 dimensional trait space,
between xj∗ and all xi . We examine the datapoints for the smallest k euclidean distances, dmin
1···k . In
∗
min
unweighted nearest neighbour, yj is predicted to be the modal value of y1···k , the output classes for these
min
is weighted by w1···k which is a
nearest datapoints. In weighted nearest neighbour algorithms, y1···k
min
function of d1···k . I used a linear kernel and a Gaussian kernel for this function. yj∗ is predicted to be the
min
modal value of the weighted y1···k
.
I applied these three nearest neighbour algorithms with k as odd values up to 19. I split the data in
half and used each half in turn as the training and test data.
Gaussian Process Classification To build the guassian process classifiers I used pre-written scripts
which implement a multinomial probit regression classifier. 40 I used half the data as the training data
and half as the test data to enable comparisons with the eANN. 33 The classifier is not hierarchical —
only two models are built; one each for classification to genus and species level.
One of the benefits of Gaussian process learning is the flexibility afforded by the different covariance
functions available. I used three different covariance functions: Automatic Relevance Determination
squared exponential, neural network and squared exponential. 41 Equation 3 shows the form of the simplest
covariance function used — the squared exponential function.
0
kSE (x, x ) =
σf2
exp
−(x − x0 )2
2`2
(3)
It is controlled by two hyper parameters: the process standard deviation σf (which controls the spread
of output probabilities) and the length scale ` which controls the length scale of the inputs x and so
has an important role in preventing overfitting. ‘Learning’ is the process of selecting values for these
hyperparameters.
The ARD covariance function is of a similar form except that it contains a matrix M of the form
diag(e` ) with `1 · · · `K=12 controlling the length-scale of each of the 12 call parameters seperately. 41 As
these are individually updated while the classifier is optimised they control the influence each parameter
has on the final output; they are the automatic relevance determination. The neural network covariance
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
Genus
Barbastella
Eptesicus
Hypsugo
Miniopterus
Myotis
Nyctalus
Pipistrellus
Plecotus
Rhinolophus
Tadarida
Vespertilio
Sample Size in Training Data
14
68
16
16
224
75
112
34
83
18
13
Number in Output
0
87
0
0
239
86
130
30
87
13
0
5
Accuracy (%)
0
79.1
0
0
98.2
74.3
98.2
73.5
100
76.5
0
Table 3. The genus specific accuracy, sample size, number of appearances and accuracy for the
Gaussian process classifier with ARD exponential covariance function. The classes with more
data are more prevelant in the output but also have a higher accuracy.
function is a large function based on the sigmoidal function sin−1 ∈ [0, 1] which is an analytical expression
of covariance within an aNN with one hidden layer as the number of hidden elements NH → ∞.
The contingency table of species misclassification was used to construct dendrograms which cluster
species by how likely they are to be misclassified as each other. It takes the proportion of species a that
gets classified as b and the proportion of b that gets classified as a. The mean of these two values is
used as the distance between the two species. The Ward clustering algorithm implemented in the class
package 39 is then used to cluster the species.
Results
The fully trained eANN has an accuracy of 85.5% for species level classification and 98.1% at the genus
level. The best nearest neighbour classifier was an unweighted k nearest neighbour algorithm with k = 1
giving accuracies of 64.0% and 87.8% (see Table 2). Weighted nearest neighbour and algorithms with
k > 1 were often almost as accurate. The Gaussian process classifiers were less accurate. At the genus
level the ARD exponential covariance function gave the highest accuracy with an accuracy of 83.6%. The
squared exponential covariance gave the highest accuracy at the species level with an accuracy of 54.6%.
Table 3 shows the genus specific classification accuracy using the ARD covariance function. This
classifier has an overall classification accuracy of 83.6%. It can be seen that the classifier exaggerates the
difference in number of records in the training data so that four of the five under-represented genera end
up not appearing in the output at all. The extreme case of this classification ‘strategy’ is to ignore all
classes that are rare. This, however, is not useful for a conservation tool where detection of rare species
is especially important.
For the most accurate species-level classifer (SE), the variation in accuracy is very large (see Table 1)
with some species being identified with an accuracy of 90–100% (T. teniotis and R. blasii) and other
species, particularly those in the Myotis genus having classification accuracies lower than 10% (Myotis
bechsteinii and Myotis emarginatus.) The species in Myotis have an average accuracy of 26.8%. Barbastella barbastellus is extremely over represented in the output with 153 individuals being classified as
B. barbastellus. This is largely due to the fact that Myotis species have an average misclassification to
B. barbastellus of 47% (see Table 4in the Appendix). Miniopterus schreibersii, Pipistrellus pipistrellus,
Plecotus auritus and R. blasii are all misclassified as B. batastellus over 10% of the time. Despite this,
B. batastellus still only has an accuracy of 85.7%. While most classes that are overrespresented in the
output have a large number of training examples, this is not true for B. barbastellus which only has 14
training records.
Although the total accuracies for the three covariance functions are not greatly different, the species
specific accuracies are very different. The classifiers with ARD and neural network covariance functions
had accuracies of zero for 13 and 16 species respectively while this was never the case using the SE
covariance function. E. serotinus (as apposed to B. batastellus) is overrespresented in both the ARD and
neural network classifiers, with 121 and 124 individuals being classified to this species.
6
60
40
20
Percent
80 100
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
0
ARD
NN
SE
10
20
30
40
50
Confidence Threshold (%)
10
20
30
40
50
Confidence Threshold (%)
Figure 1. Accuracies (solid lines and triangles) and proportion of null predictions (dashed lines
and diamonds) for genus (left) and species (right) levels. The three covariance functions are
shown in different colours.
The output of the Gaussian process classifier is an array of probabilities for each yj∗ being in each
class cK . This allows the probability to be thresholded and a ‘null’ output be returned if the confidence
is too low. This can be useful in a practical context when a null output might suggest that the bat
call needs to be recorded again or that the model can not identify it properly and trapping and visual
identification might be considered. Vagrant or invasive bat species will not be included in this classifier
and a worldwide classifier is unlikely to be built, so no classifier will have 100% covereage; this makes a
null output a useful option in practice. The accuracy and percentage of predictions which were returned
as null at each threshold value are shown in Figure 1.
At the genus level, thresholding works quite well to gently remove the predictions that have a high
likelihood of being incorrect. The percentage of null results is only slightly more than the increase in
accuracy (compare the gradients of the two set of lines) and so most of the results which recieved a
null classification were incorrect in the first instance. The situation is very different at the species level
as a large proportion of results have low probabilities. By a 10% threshold the classifiers with neural
network and squared exponential covariance functions have an unacceptable level of null classifications.
Furthermore, the squared exponential covariance function yields 91% null outputs at a 10% probability
threshold and 100% of predictions have a probability of less than 20%. Only the ARD covariance function
allows thresholding to be usefully used at the species level.
The proportion of each species that was misclassified as which other species (Table 4 in Appendix) can
be used a distance measure between each species. As misclassification is a one way process, I used the
mean of the misclassification in each direction. These distances can then be used to create a dendrogram
as in Figure 2. This dendrogram was created using the ward method. Although most sister pairs are of
the same call group, the only ‘clades’ that closely represent a genus or call type are the group of Myotis
species in the centre of the tree and the Plecotus group. This suggests that misclassification is not only
within genera, but between them. Futhermore, this tree suggests that when constructing a hierarchical
Gaussian process classifier, these call groups are not necessarily the best high level groups to classify
to. This tree supports using a three call type classification: a Plecotus group, a Myotis group and a
third group containing everything else. A fourth group could possibly contain Nyctalus, Eptesicus and
Vespertilio.
Discussion
Overall, the nearest neighbour classifier was actually more accurate (67.7% and 90.4%) than the guassian
process classifiers (ARD: 49.4%, 83.6% and SE: 54.6%, 79.2%), while the eANN 33 is the most accurate
classifier. There is, therefore, little practical use for this classifier as constructed here. However, Gaussian
process classifiers normally have similar accuracies to neural networks when compared like-with-like. The
Gaussian process classifier with a neural network covariance function should have very similar accuracy as
a one layer eANN as they converge as the number of hidden units tends to infinity (however the eANN 33
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
7
used both one and two hidden layers.) Therefore, the foremost task is to reconstruct the Gaussian process
classifier within a hierarchical framework similar to that used for the eANN. The calculations involved
in each Bayesian update scale as O(K 3 N 3 ) and therefore a hierarchy of classifiers (with a subset of cK
being used in each classifier) is likely to be faster to train than the classifier constructed here as long as
the number of classifiers being trained remains smaller than K.
Discriminant function analysis was used to select the 12 parameters most likely to be beneficial in
classifying test records. However, this is not necessarily the best way to select parameters as the ARD
covariance function does this automatically within the model and with fewer a priori assumptions. Furthermore, the ARD squared exponential covariance function used in this study is not the only ARD covariance function that can be used in Gaussian process learning. However, given the scaling of O(K 3 N 3 ),
using additional parameters will greatly increase computation time, but potentially not prohibitively so.
Model selection within Gaussian process learning, especially with respect to the covariance functions,
is a ubiquitous and open-ended problem. Model selection occurs at different levels. 41 The top level is
the selection of the form of the covariance function from the discrete set of covariance functions Hi . The
statistical features of the data should guide choices, such as whether to use a stationary or non-stationary
covariance function (do we expect the shape of the function to change), but at some point there has
to be a user choice on the functions to test between. How many different covariance functions to try
is constrained by computational time. The lower levels of model specification involve choosing hyper
parameters for the model and this is what occurs during the ‘learnig; phase if the methods implemented
here. At all levels the selection of a model can be informed by the marginal likelihood p(y|x, Hi ) which is
the likelihood of the response variable given the input variables and the model. This optimum solution
to the problem is a trade-off between highly complex models that overfit the data and overly simplistic
models which fail to capture the details in the data. In short, future work could examine a broader class
of covariance functions than was possible in the time available for this work.
One notable problem with the classifiers contructed here, especially with the genus level classifier, is
the tendency for the model to apply overly high probabilities to classes with many records in the training
data. In the genus classifier this becomes problematic as the variation in number of records covers two
orders of magnitude. Furthermore it is not obvious how to balance this as the different species within
a genus might occupy different areas of parameter space. Therefore including training data from all
species is important. If the genera were balanced to having 13 records each (to match the lowest value,
Vespertillio), only 13 × 11 = 143 records would be used overall. This leaves a training dataset less than
a tenth the size of the dataset used here.
While it is important to try and limit pseudo replication in the training data, there is no reason why
multiple calls cannot be used to try and increase accuracy within the test data. This is in fact the
most sensible approach when in the field. If these methods can be transferred to an on-the-fly medium it
would even be sensible to record the bat until the confidence of the prediction reaches a certain threshold.
However, this dataset contains, as far as possible, only one call from each individual as the data is used
for both training and testing. One avenue for future work would be to take only one call per individual
for the training data and take all calls above a certain threshold of quality (a parameter that is calculated
by SonoBat) for the test dataset.
Gaussian process methods offer the possibility of a machine learning method with the accuracy of
neural networks while avoiding the ‘black box’ problem inherent in neural networks. Due to the hidden
layers and nonlinearities in neural networks, interpretation of results and understanding how a model
maps inputs to outputs is difficult. Gaussian process models are much easier to interpret 41 . For example,
when using the ARD covariance function, it is a simple matter of printing the matrix containing the `K
scale lengths to discover which dimensions are considered important by the model.
Conclusions Overall, this work does not suggest that a Gaussian process classifier is an effective way
of classifying bats by their calls. Instead the eANN 33 is the most effective classifier. However, the time
limits of the project prevented the capabilities of Gaussian process models from being fully explored.
Further work should focus on constructing a hierarchical Gaussian process classifier as this may be as
accurate as the eANN. Further work would also include using a wider variety of covariance functions.
References
1. Greenaway, F. and Hutson, A. M. A Field Guide To British Bats. Bruce Coleman Books, Uxbridge,
(1990). 1
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
8
Pipistrellus pipistrellus (HQCF)
Myotis nattereri (BFM)
Rhinolophus ferrumequinum (CF)
Rhinolophus blasii (CF)
Tadarida teniotis (LQCF)
Nyctalus lasiopterus (LQCF)
Rhinolophus mehelyi (CF)
Rhinolophus hipposideros (CF)
Pipistrellus pygmaeus (HQCF)
Miniopterus schreibersii (HQCF)
Pipistrellus nathusii (HQCF)
Hypsugo savii (LQCF)
Pipistrellus kuhlii (HQCF)
Myotis emarginatus (BFM)
Myotis alcathoe (BFM)
Myotis bechsteinii (BFM)
Myotis daubentonii (BFM)
Myotis brandtii (BFM)
Myotis capaccinii (BFM)
Rhinolophus euryale (CF)
Myotis mystacinus (BFM)
Eptesicus bottae (LQCF)
Barbastella barbastellus (LQCF)
Myotis blythii (BFM)
Myotis dasycneme (BFM)
Nyctalus leisleri (LQCF)
Eptesicus nilssonii (LQCF)
Vespertilio murinus (LQCF)
Nyctalus noctula (LQCF)
Myotis punicus (BFM)
Myotis myotis (BFM)
Plecotus austriacus (NFM)
Plecotus auritus (NFM)
Eptesicus serotinus (LQCF)
Figure 2. Dendrogram of misclassification distance. Closely ‘related’ individuals are often
misclassified as each other. The five broad call types used by Walters et al. 33 are shown in
colour. The misclassification of individuals does not strongly reflect phylogeny or call type. The
two notable groups are the Myotis group in the centre of the table and the Plecotus group.
2. IUCN. Red list of threatened species. version 2010.1. www.iucnredlist.org, (2010). 1
3. Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R., and Racey, P. A. Carpe noctem: The importance
of bats as bioindicators. Endangered Species Research 8(1-2), 93–115 (2009). 1
4. Sands, P. Principles of international environmental law, pg. 609. Cambridge University Press,
Cambridge, 2nd edition (2003). 1
5. 1991 Agreement on the Conservation of Bats in Europe. London, (in force 16 Jan 1994). 1
6. Barclay, R. M. R. Bats are not birds: A cautionary note on using echolocation calls to identify bats:
A comment. Journal Of Mammalogy 80(1), 290–296 (1999). 1
7. Murray, K. L., Britzke, E. R., and Robbins, L. W. Variation in search-phase calls of bats. Journal
Of Mammalogy 82(3), 728–737 (2001). 1
8. Rydell, J., Arita, H. T., Santos, M., and Granados, J. Acoustic identification of insectivorous bats
(order chiroptera) of Yucatan, Mexico. Journal Of Zoology 257(1), 27–36 (2002).
9. Murray, K. L., Fraser, E., Davy, C., Fleming, T. H., and Fenton, M. B. Characterization of the
echolocation calls of bats from Exuma, Bahamas. Acta Chiropterologica 11(2), 415–424 (2009). 1
10. Harris, J. G. and Skowronski, M. D. Automatic speech processing methods for bioacoustic signal
analysis: A case study of cross-disciplinary acoustic research. In 2006 Ieee International Conference
On Acoustics, Speech, And Signal Processing, Vol V, Proceedings: Audio And Electroacoustics, Multimedia Signal Processing, Machine Learning For Signal Processing Special Sessions, International
Conference On Acoustics Speech And Signal Processing Icassp, 793+. Ieee Signal Proc Soc, (2006).
31st Ieee International Conference On Acoustics, Speech And Signal Processing, Toulouse, France,
May 14-19, 2006. 1
11. Betts, B. J. Effects of interindividual variation in echolocation calls on identification of big brown
and silver-haired bats. Journal Of Wildlife Management 62(3), 1003–1010 (1998). 1
12. Parsons, S. and Jones, G. Acoustic identification of twelve species of echolocating bat by discriminant
function analysis and artificial neural networks. Journal Of Experimental Biology 203(17), 2641–2656
(2000). 1
13. Russo, D. and Jones, G. Identification of twenty-two bat species (mammalia : Chiroptera) from
Italy by analysis of time-expanded recordings of echolocation calls. Journal Of Zoology 258(Part 1),
91–103 (2002). 2
14. Preatoni, D. G., Nodari, M., Chirichella, R., Tosi, G., Wauters, L. A., and Martinoli, A. Identifying
bats from time-expanded recordings of search calls: Comparing classification methods. Journal Of
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
9
Wildlife Management 69(4), 1601–1614 (2005). 1
15. Papadatou, E., Butlin, R. K., and Altringham, J. D. Identification of bat species in Greece from
their echolocation calls. Acta Chiropterologica 10(1), 127–143 (2008).
16. Armitage, D. W. and Ober, H. K. A comparison of supervised learning techniques in the classification
of bat echolocation calls. Ecological Informatics 5(6), 465–473 (2010). 1
17. Britzke, E. R., Duchamp, J. E., Murray, K. L., Swihart, R. K., and Robbins, L. W. Acoustic
identification of bats in the Eastern United States: A comparison of parametric and nonparametric
methods. Journal Of Wildlife Management 75(3), 660–667 (2011). 1
18. Parsons, S. Identification of New Zealand bats (Chalinolobus tuberculatus and Mystacina tuberculata)
in flight from analysis of echolocation calls by artificial neural networks. Journal Of Zoology 253(Part
4), 447–456 (2001).
19. Jennings, N., Parsons, S., and Pocock, M. J. O. Human vs. machine: Identification of bat species
from their echolocation calls by humans and by artificial neural networks. Canadian Journal Of
Zoology-Revue Canadienne De Zoologie 86(5), 371–377 (2008).
20. Charrier, I., Aubin, T., and Mathevon, N. Mother-calf vocal communication in Atlantic walrus: A
first field experimental study. Animal Cognition 13(3), 471–482 (2010). 1
21. Adams, M. D., Law, B. S., and Gibson, M. S. Reliable automation of bat call identification for Eastern
New South Wales, Australia, using classification trees and anascheme software. Acta Chiropterologica
12(1), 231–245 (2010). 1
22. Acevedo, M. A., Corrada-Bravo, C., Corrada-Bravo, H., Villanueva-Rivera, L., and Aide, T. Automated classification of bird and amphibian calls using machine learning: A comparison of methods.
Ecological Informatics 4(4), 206–214 (2009). 1
23. Huang, C., Yang, Y., Yang, D., and Chen, Y. Frog classification using machine learning techniques.
Expert Systems With Applications 36(2), 3737–3743 (2009). 1
24. Yinghai, K. E., Quackenbush, L. J., and Jungho, I. M. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sensing Of Environment
114(6), 1141–1154, JUN 15 (2010). 1
25. Tan, S. and Haider, A. A comparative study of polarimetric and non-polarimetric lidar in deciduousconiferous tree classification. In 2010 Ieee International Geoscience And Remote Sensing Symposium, IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 1178–1181. IEEE,
(2010). IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, JUN 25-30,
2010.
26. Foody, G. M., Atkinson, P. M., Gething, P. W., Ravenhill, N. A., and Kelly, C. K. Identification of
specific tree species in ancient semi-natural woodland from digital aerial sensor imagery. Ecological
Applications 15(4), 1233–1244 (2005). 1
27. Hauser-Davis, R. A., Oliveira, T. F., Silveira, A. M., Silva, T. B., and Ziolli, R. L. Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification
of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil
liza). Ecological Informatics 5(6), 474–478, NOV (2010). 1
28. Zhao, K., Popescu, S., Meng, X., Pang, Y., and Agca, M. Characterizing forest canopy structure with
lidar composite metrics and machine learning. Remote Sensing Of Environment 115(8), 1978–1996
(2011). 2
29. Lise, S., Archambeau, C., Pontil, M., and Jones, D. T. Prediction of hot spot residues at proteinprotein interfaces by combining machine learning and energy-based methods. Bmc Bioinformatics
10 (2009). 2
30. Tian, F., Zhang, C., Fan, X., Yang, X., Wang, X., and Liang, H. Predicting the flexibility profile of
ribosomal RNAs. Molecular Informatics 29(10), 707–715 (2010). 2
31. Van Loon, K., Guiza, F., Meyfroidt, G., Aerts, J. M., Ramon, J., Blockeel, H., Bruynooghe, M., Van
Den Berghe, G., and Berckmans, D. Prediction of clinical conditions after coronary bypass surgery
using dynamic data analysis. Journal Of Medical Systems 34(3), 229–239 (2010). 2
32. Mann, R., Freeman, R., Osborne, M., Garnett, R., Armstrong, C., Meade, J., Biro, D., Guilford, T.,
and Roberts, S. Objectively identifying landmark use and predicting flight trajectories of the homing
pigeon using gaussian processes. Journal Of The Royal Society Interface 8(55), 210–219 (2011). 2
33. Walters, C. L., Maltby, A., Barataud, M., Dietz, C., Fenton, M. B., Jennings, N., Jones, G., Obrist,
M. K., Puechmaille, S., Sattler, T., Siemers, B., Jones, K. E., and Parsons, S. A continental-scale
tool for acoustic identification of european bats. (In Press, 2011). 2, 3, 4, 6, 7, 8
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
10
34. Williams, C. K. I. and Barber, D. Bayesian classification with gaussian processes. IEEE Transactions
On Pattern Analysis And Machine Intelligence 20(12), 1342–1351 (1998). 2
35. Maltby, A. The Evolution of Echolocation. PhD thesis, University College London, (2011). 2
36. Szewczak, J. M. SonoBat v.3, (2010). 2
37. R Development Core Team. R: A Language And Environment For Statistical Computing. R Foundation For Statistical Computing, Vienna, Austria, (2010). ISBN 3-900051-07-0. 4
38. Schliep, K. and Hechenbichler, K. Kknn: Weighted K-Nearest Neighbors, (2010). R Package Version
1.0-8. 4
39. Venables, W. N. and Ripley, B. D. Modern Applied Statistics with S. Springer, New York, fourth
edition, (2002). 4, 5
40. Girolami, M. and Rogers, S. Variational bayesian multinomial probit regression with gaussian process
priors. Neural Computation 18(8), 1790–1817 (2006). 4
41. Rasmussen, C. E. and Williams, C. K. I. Gaussian Processes for Machine Learning. MIT Press,
Cambridge, (2006). 4, 7
Species
B. barbastellus
E. bottae
E. nilssonii
E. serotinus
H. savii
Mi. schreibersii
My. alcathoe
My. bechsteinii
My. blythii
My. brandtii
My. capaccinii
My. dasycneme
My. daubentonii
My. emarginatus
My. myotis
My. mystacinus
My. nattereri
My. punicus
N. lasiopterus
N. leisleri
N. noctula
Pi. kuhlii
Pi. nathusii
Pi. pipistrellus
Pi. pygmaeus
Pl. auritus
Pl. austriacus
R. blasii
Rh. euryale
R. ferrumequinum
R. hipposideros
T. teniotis
V.murinus
R. mehelyi
2
0
61.5
7.1
0
6.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
7.1
23.1
78.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
80
0
0
0
0
0
0
0
0
0
0
7.7
0
0
7.7
0
23.7
17.4
0
0
0
0
5
7.7
0
0
0
0
0
30.8
0
5
0
7.7
0
0
43.8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.3
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
33.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
0
0
38.5
0
0
0
20
0
0
0
0
11.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
0
0
9.1
0
0
6.7
0
8
7.7
0
3.7
0
7.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
15.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
4.5
0
21.7
0
0
4
0
0
7.4
0
0
0
0
0
4.3
0
0
0
0
0
0
0
0
0
0
0
0
11
0
0
0
0
0
0
7.7
0
0
0
20
0
0
0
0
11.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
0
0
0
0
0
0
0
4.5
0
4.3
0
69.2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
0
0
0
0
0
0
0
0
4.3
0
0
12
0
0
3.7
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
14
0
0
0
0
0
0
7.7
0
0
0
6.7
0
12
7.7
0
7.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15
0
0
0
0
0
0
0
0
7.7
0
0
7.7
0
0
30.8
0
0
7.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7.7
0
16
0
0
0
0
0
0
15.4
0
0
13
13.3
0
12
15.4
0
11.1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
0
4.5
0
0
0
7.7
0
0
0
0
18.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
0
0
0
0
0
0
0
4.5
0
0
0
0
0
0
46.2
0
3
46.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
19
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
84.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20
0
7.7
0
5
12.5
0
0
4.5
0
0
0
0
0
0
0
0
0
0
0
50
4.3
0
0
0
0
0
0
0
0
0
0
0
7.7
0
21
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7.7
7.9
73.9
0
0
0
0
0
0
0
0
0
0
0
0
0
22
0
0
0
0
31.2
0
0
4.5
0
0
0
0
0
0
0
0
0
0
0
0
0
82.6
38.5
0
0
0
0
0
0
0
0
0
0
0
23
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.3
23.1
0
0
0
0
0
0
0
0
0
0
0
24
0
0
0
0
0
6.7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30.8
87
3.8
0
0
0
0
0
0
0
0
0
25
0
0
0
0
0
33.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
86.5
0
0
0
0
0
0
0
0
0
26
7.1
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
50
23.1
0
0
0
0
0
0
0
27
0
0
0
2.5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.3
0
0
0
0
25
46.2
0
0
0
0
0
7.7
0
28
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
84.6
0
0
0
0
0
0
29
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
69.2
0
0
0
0
0
30
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15.4
0
7.7
93.3
0
0
0
0
31
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
69.2
0
0
15
32
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7.7
0
0
0
0
0
0
0
0
0
0
0
0
100
0
0
33
0
0
14.3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10.5
0
0
0
0
0
0
0
0
0
0
0
0
46.2
0
34
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
15.4
0
23.1
0
0
85
Table 4. Contingency table for species level classifier using squared exponential covariance function. Numbers are % of the row species classified as
the column species with the diagonal being species accuracy.
1
85.7
0
0
2.5
6.2
26.7
30.8
63.6
76.9
56.5
33.3
15.4
52
69.2
15.4
44.4
75.8
30.8
0
5.3
0
4.3
7.7
13
9.6
15
7.7
15.4
7.7
6.7
7.7
0
0
0
BAT IDENTIFICATION WITH GAUSSIAN PROCESS LEARNING
11