Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 115-117). Also available via the Internet.

This dissertation explores various ways of working with acoustic analyses of speech in music composition. The first chapter presents an overview of whistled languages and discusses their potential to act as blueprints for optimizing phonetic data for compositional use. The second chapter details my workflow for incorporating formant and fundamental frequency analysis data from the phonetics software Praat into my compositional methodology. Broadly inspired by the ways in which whistled utterances transform spoken language, the workflow consists of an analysis phase in Praat followed by the conversion, optimization and orchestration of the extracted phonetic data in the computer-assisted composition environments OpenMusic and bach. Also included in the dissertation are two compositions that are both informed by phonetics. The first composition, ŠÀ {karāz} for large ensemble, contains, among the various ways it attempts to instrumentally imitate speech, a section that is constructed with the help of the workflow described in the second chapter. The second composition, eschaton according to bēl-rē’u-šu for percussion trio, engages in a deconstruction of the established roles of speech and instruments in my music, in which the performers are, at times, asked to imitate the sounds of percussion instruments with their voice, in an attempt to blur the line between speech as “the imitated” and instruments as “the imitators.”

Bottlenose dolprlin (Tursiops trunratus) whistles are currently studied by subjective visual comparison of whistle spectrograms. This thesis describes the novel use of stochastic modeling to automate the comparison of dolphin whistles and to yield an objective, quantitative measure of whistle similarity. The relationship of bottlenose dolphin whistle production to a model of human speech production is discussed, providing a basis for the use of human speech recognition techniques for creating whistle models. Discrete hidden Markov models based on vector quantization of linear prediction coefficients are used to create whistle models based on statistical information derived from a sample set of dolphin whistles. Whistle model comparison results are presented indicating that evaluation of bottlenose dolphin whistles via hidden Markov modeling provides an objective measure of similarity between whistles. The results also demonstrate that hidden Markov models provide robustness against the effects of temporal and frequency variance in the comparison of whistles. The extensibility of stochastic modeling techniques to other animal vocalizations is discussed and possibilities for further work in areas such as the determination of possible structural components, similar to phonemes in human speech, is provided. / Master of Science