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.