_Algorithms_**2009**, 2, 907-924; doi:10.3390/a2030907  
_Algorithms_  
**ISSN 1999-4893**  
[[http://www.mdpi.com/journal/algorithms](http://www.mdpi.com/journal/algorithms)]([http://www.mdpi.com/journal/algorithms](http://www.mdpi.com/journal/algorithms))  
_Article_  
**Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks**  
**[PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 1** 1**  
Footnote 12  
and proximity to conspecifics [4]. Call structure can vary by gender [5], and may also change as an individual bat ages [5-7]. Furthermore, the recording equipment used by researchers and the effects of the physical environment, for example frequency-dependent atmospheric attenuation, may introduce additional variability [6]. Individuals and species sharing a habitat or ecological niche may produce echolocation calls that are similar both temporally and spectrally to deal with similar sensorial challenges. One method that researchers employ to identify bats from their echolocation calls relies on subjective analysis of call structure, often supplemented by direct observation of the bat as it flies [8]. While such methods can provide results comparable to some computer-based machine learning techniques [9], they are of limited use where repeatable (over time and between researchers), quantifiable measures of species identity are required [10]. Removal of bias associated with researcher experience and enhancement of repeatability of results requires more objective quantifiable methods. Multivariate discriminant functions have been used to identify calls of bats recorded in south-east England and Italy for habitat-use assessment [11-12] while a decision tree was used to classify zero-crossed echolocation call recordings from eight Australian species [13]. Neural networks have also been used to identify species of British bats flying over organic and conventional farms [14]. Finally, machine learning techniques commonly used in automated (human) speech recognition have been used to detect and classify calls from five North American bat species [15]. Although these methods allow satisfactory identification of several species, others are so similar that the development of new methods may be required to separate them. Convergence in call structure among species makes acoustic identification of _Myotis_ bats especially challenging. In this study we aimed to test the ability of two classifiers, support vector machines (SVM) and ensembles of artificial neural networks (ENN), to classify the echolocation calls of bats with the hope that they would produce consistently high correct classification rates. A third technique, discriminant function analysis, was used as a benchmark against which the performance of these classifiers could be compared. A relatively recent advance in machine learning techniques, SVMs work by creating separation boundaries between classes by training iteratively on a dataset. The separation boundaries between classes are modified for each case (in this study, a case is the parameters derived from a single call) in the dataset. Modifications are based on data points that lie on the boundary of each class, i.e., support vectors in the dataset [16]. SVMs have been used successfully for multi-dimensional classification tasks such as cancer tissue classification from microarray data [17]. They have also been applied in acoustic research for tasks such as classification of musical audio [18]. Ensembles use a group of classifiers and a voting system to decide on the classification of an input [19]. They work by presenting the inputs to each classifier within the ensemble and collating the results to reach a consensus classification. Provided the members of the ensemble have a classification rate greater than 50%, the ensemble will achieve a higher classification rate than any one member alone [19].