The by-catch of small cetaceans in fishing nets has been identified as a widespread problem, but attempts to reduce this require an understanding of the way these animals behave around the nets. One of the problems with assessing changes in behaviour between encounters is the difficulty of identifying individuals. Acoustic identification techniques overcome some of the problems associated with visual ID, and field research has shown that the presence of a sonobuoy and hydrophone have no effect on dolphin behaviour in the field. Dolphins produce whistles that can be used for identification, although current theory suggests these identify small groups rather than individuals. Novel algorithms have been developed to detect and process these tonal whistles, and their characteristic time-frequency-intensity contours extracted from the raw signals. Feature extraction techniques were developed for the contours based on timefrequency 'shape' of the contours, allowing a syntactic pattern recognition approach based around hidden Markov modelling to be employed for classification. The algorithms have enabled the whistles from concurrent whistles to be separated and analysed. Contours of 101 wild bottlenose dolphin whistles were successfully characterised. Analysis of the resulting classes indicated one group occurring only once and two other groups occurred twice but on different days. Another study was conducted of three groups of common dolphin, with a total of 49 recorded whistles analysed. The first group was found to contain whistles significantly different to either of the other two, although neither similarity nor dissimilarity could be inferred on the second and third. Further analysis suggested there were indeed two separate groups of dolphins for the last two groups, but that there was a period of overlap in their recording. A significant difference could be found between them once certain classes were re-assigned. It should be possible to apply these same techniques to a wider range of odontocete species, since most of those studied have been found to exhibit similar whistles. The tasks of whistle detection, isolation, and encoding can be applied automatically by computer with no loss of identity information, and these encoded contours can subsequently be quantitatively classified by their shape.

The value of studying elephant vocalizations lies in the abundant information that can be retrieved from it. Recordings of elephant rumbles can be used by researchers to determine the size and composition of the herd, the sexual state, as well as the emotional condition of an elephant. It is a difficult task for researchers to obtain large volumes of continuous recordings of elephant vocalizations. Recordings are normally analysed manually to identify the location of rumbles via the tedious and time consuming methods of sped up listening and the visual evaluation of spectrograms. The application of speech processing on elephant vocalizations is a highly unexploited resource. The aim of this study was to contribute to the current body of knowledge and resources of elephant research by developing a tool for recording high volumes of continuous acoustic data in harsh natural conditions as well as examining the possibilities of applying human speech processing techniques to elephant rumbles to achieve automatic detection of these rumbles in recordings. The recording tool was designed and implemented as an elephant recording collar that has an onboard data storage capacity of 128 gigabytes, enough memory to record sound data continuously for a period of nine months. Data is stored in the wave file format and the device has the ability to navigate and control the FAT32 file system so that the files can be read and downloaded to a personal computer. The collar also has the ability to stamp sound files with the time and date, ambient temperature and GPS coordinates. Several different options for microphone placement and protection have been tested experimentally to find an acceptable solution. A relevant voice activity detection algorithm was chosen as a base for the automatic detection of infrasonic elephant rumbles. The chosen algorithm is based on a robust pitch determination algorithm that has been experimentally verified to function correctly under a signal-to-noise ratio as low as -8 dB when more than four harmonic structures exist in a sound. The algorithm was modified to be used for elephant rumbles and was tested with previously recorded elephant vocalization data. The results obtained suggest that the algorithm can accurately detect elephant rumbles from recordings. The number of false alarms and undetected calls increase when recordings are contaminated with unwanted noise that contains harmonic structures or when the harmonic nature of a rumble is lost. Data obtained from the recording collar is less prone to being contaminated than far field recordings and the automatic detection algorithm should provide an accurate tool for detecting any rumbles that appear in the recordings. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

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