## 3 Experiments  
### Asian Elephant (Elephas maximus): Repertoire Analysis  
Elephants are intelligent, long-living animals that live in a complex and fluid society in which several modes of communication play a role in maintaining group cohesion and social order, and in locating and assessing reproductive state of potential mates [32,33]. Although the social and behavioral contexts of Asian elephant communication signals are well studied [34-37], the study of the elephant vocal repertoire through categorization of call types by acoustic parameters is still needed as a basis for future research on individuality and for comparisons of acoustic communication among elephant species and populations [32]. [PERSON] [38] described ten basic vocalizations for Asian elephants, divided into three major categories. However, only the low frequency rumble has been described by spectral and temporal features [39]. This study focuses on call type classification for Asian elephant repertoire analysis and validation. Vocalizations were recorded from captive elephants housed at the Oregon Zoo in Portland, OR (USA) and from domesticated elephants in Thailand [40,41]. Vocalizations were collected in a variety of social contexts, time frames, and environmental noise conditions in both urban and rural settings. Data was recorded from seven known individuals and between 10 and 25 unknown individuals, at distances ranging from 1 to 100 meters. The primary sampling rate was 44.1 kHz, although a few calls were alternatively recorded at 32 kHz. Call types were manually classified using perceptual aural cues and visual inspection of spectrograms for differentiation of fundamental frequency contour, tonality, and signal duration. [PERSON] _et al._[40,41] defined an acoustic repertoire of Asian elephants based on acoustic parameters, and validated structural distinction among six basic call types (Bark, Roar, Rumble, Squeak, Squeal, and Trumpet) and the non-laryngeal Blow. In addition to these basic call types, five call combinations with these basic calls forming their constituent parts are defined (Roar-Rumble, Squeal-Squeak, Squeak train, Squeak-Bark, and Trumpet-Roar). The frequency range of the signal energy ranges from 14 Hz up to 9 kHz, and call durations range from 0.1 to 14 seconds. Trumpets, Squeaks, Squeals have energy in the higher frequency range (54 Hz to 9 kHz) and are differentiated perceptually and through visual inspection by frequency modulation and duration. Rumbles, Roars, and Barks are limited to the lower frequency range (14 Hz to 5 kHz), and are differentiated by amplitude modulation and duration. A total of 2,044 vocalizations were manually segmented into all call categories. All vocalizations are included in the data set, regardless of signal degradation by noise or presence of overlapping signals. To maximize training set size given the limited number of examples, 5-fold cross validation is implemented. In the cross-validation methodology, the data set is divided into multiple equal subsets (five for this experiment), with one set used for testing and the remaining for training. The process is then repeated once for each test subset, so that each example is used as a test point exactly once. For analysis, GFCC features are extracted from the vocalizations using a moving Hamming window with a window size of 25 ms with 15 ms overlap. A 39 element feature vector (standard in human speech recognition) is used, with 12 GFCC coefficients with cepstral mean normalization plus log-energy, appended by velocity and acceleration, as described previously. The Greenwood frequency warping constants are calculated using 10 Hz-10 kHz to encompass the signal energy [40, 41]. A total of 60 triangular filterbanks (more than is usually used in human speech) are spaced in accordance with the Greenwood warping function across that frequency range to guarantee enough filterbanks in the low frequencies, where a large amount of signal energy is located.