<p> Music composition using Artificial Intelligence is a well-established area of study with research dating back over six decades. From the time the mathematical model of computation was developed by Alan Turing in the 1940s, the question of whether computers can be built to match human level intelligence has been debated. Creativity is certainly considered to be a sign of intelligence, and many areas of Artificial Intelligence have pursued ways to emulate the creative spark found in humans. Music Composition via Artificial Intelligence falls into this category. This thesis explores the application of Artificial Intelligence approaches towards the goal of composing music by implementing three approaches found in Artificial Intelligence and studying their results. </p>

<p> Sound, in its all forms, is a source of energy whose capabilities humankind is not yet fully aware of. Composition - the way of aggregating sounds into the form of music - still holds to be an unperceived methodology with lots of unknowns. Methodologies used by composers are generally seem as being innate talent, something that cannot be used or shared by others. Yet, as any other form of art, music actually is and can be interpreted with mathematics and geometry. The focus of this thesis is to propose a generative algorithm to compose structured music pieces using linear algebra as the mathematical language for the representation of music. By implementing the linear algebra as the scientific framework, a practical data structure is obtained for analysis and manipulation. Instead of defining a single structure from a certain musical canon, which is a type of limiting the frame of music, the generative algorithm proposed in this paper is capable of learning all kinds of musical structures by linear algebra operations. The algorithm is designed to build musical knowledge (influence) by analyzing music pieces and receive a new melody as the inspirational component to produce new unique and meaningful music pieces. Characteristic analysis features obtained from analyzing music pieces, serves as constraints during the composition process. The proposed algorithm has been successful in generating unique and meaningful music pieces. The process time of the algorithm varies due to complexity of the influential aspect. Yet, the free nature of the generative algorithm and the capability of matrical representation offer a practical linkage between unique and meaningful music creation and any other concept containing a mathematical foundation.</p><p>

<p> To create algorithmic art with documents available on the internet, artists must discover strategies for organizing those documents. In this project I used a graph structure based on Melissa Schilling's model of cognitive insight to reorganize sounds on the web using aural and lexical relationships. I was then able to generate music with these graphs using several different activation strategies. In section one I introduce my goals for this project. In section two I review other approaches to this problem and art that has influenced my approach. In section three I demonstrate techniques for organizing and collaging sounds from freesound.org. Sounds can be organized in a graph structure by exploiting aural similarity relationships provided by freesound.org, and lexical relationships provided by wordnik.com. Music can then be generated from these graphs in a variety of ways. In section four I show how my software was inspired by theories of creativity. Specifically I show how my software is an illustration of Melissa Schilling's graph model of cognitive insight. In section five, I elaborate on the pieces I've generated for this dissertation using this software and several other novel sound generating programs.</p>