| 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. |
