Algorithmic composition using signal processing and swarm behavior. : Evaluation of three candidate methods.

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Abstract: Techniques for algorithmic musical composition or generative music working directly with the frequencies of the sounds being played are rare today as most approaches rely on mapping of discrete states. The purpose of this work is to investigate how self organizing audio can be created in realtime based on pitch information, and to find methods that give both expressive control and some unpredictability. A series of experiments were done using SuperCollider and evaluated against criteria formulated using music theory and psychoacoustics. One approach was utilizing the missing fundamental phenomenon and pitch detection using autocorrelation. This approach generated unpredictable sounds but was too much reliant on user input to generate evolving sounds. Another approach was the Kuramoto model of synchronizing oscillators. This resulted in pleasant phasing sounds when oscillators modulating the amplitudes of audible oscillators were synchronized, and distorted sounds when the frequencies of the audible oscillators were synchronized. Lastly, swarming behavior was investigated by implementing an audio analogy of Reynolds’ Boids model. The boids model resulted in interesting independently evolving sounds. Only the boids model showed true promise as a method of algorithmic composition. Further work could be done to expand the boids model by incorporating more parameters. Kuramoto synchronization could viably be used for sound design or incorporated into the boids model.

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