Hidden Markov Models and AI Music Composition

Natural systems are the source of inspiration for the human tendency to pursue creative endeavors. Music composition is a language for human expression and can therefore be utilized in conveying the expressive capabilities of other systems. Using a Hidden Markov Model (HMM) learning system, a computer can be taught to create music that is coherent and aesthetically sufficient given the correct tools. The tools selected for this project include: twenty-two years of sun spot data as the natural system from which to creatively draw; a compositional framework for structure, pitch, dynamics, and rhythm to facilitate a human understanding of the systems expressiveness; the jMusic1, open source, music composition software; and an HMM learning system2 with implementations of the Forward-Backward, Viterbi, and Baum-Welch algorithms. In composing a final piece of music the attempt was made to impose as few creative restrictions on the system as possible. Through these tools every aspect of the compositions generation can be repeated. In this way the robust analytical capabilities of the system are displayed via the piece and its generative procedures, thereby displaying an artificial intelligences potential for music composition and perhaps larger creative projects