Modern day musicians rely on a variety of instruments for musical expression. Tones produced from electronic instruments have become almost as commonplace as those produced by traditional ones as evidenced by the plethora of artists who can be found composing and performing with nothing more than a personal computer. This desire to embrace technical innovation as a means to augment performance art has created a budding field in computer science that explores the creation and manipulation of sound for artistic purposes. One facet of this new frontier concerns timbral creation, or the development of new sounds with unique characteristics that can be wielded by the musician as a virtual instrument. This thesis presents Timcat, a software system that can be used to create novel timbres from prerecorded audio. Various techniques for timbral feature extraction from short audio clips, or grains, are evaluated for use in timbral feature spaces. Clustering is performed on feature vectors in these spaces and groupings are recombined using concatenative synthesis techniques in order to form new instrument patches. The results reveal that interesting timbres can be created using features extracted by both newly developed and existing signal analysis techniques, many common in other fields though not often applied to music audio signals. Several of the features employed also show high accuracy for instrument separation in randomly mixed tracks. Survey results demonstrate positive feedback concerning the timbres created by Timcat from electronic music composers, musicians, and music lovers alike.

Audiovisual concatenative synthesis is an analysis-driven granular technique using a corpus of multimedia data to sequence audio and video streams on a microtemporal level. This text outlines my development of this technique as a tool for multimedia composition, using my work, Replica, as a case study. The paper illustrates how the concepts of granular structure, gesture capture, and replication are integral not only to the software but to the architecture of the composition. In doing so, machine learning approaches to music and visual art are reviewed and related to my personal compositional practice. Additionally, I attempt to show how audiovisual concatenative synthesis provides a composer with strategies for shaping one's sense of time through disorienting audiovisual cues and tightly organized counterpoint between sound and image, stage and screen, and the real and virtual.