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Nowadays, a number of bio-inspired computational techniques such as artificial neural networks, genetic and evolutionary algorithms are applied in many engineering systems for information processing. One of the current trends in this field is swarm intelligence which is simulated by the behavior of insect colonies or group of animals to solve nonlinear design problems. In this model systems, the populations of ants, birds, fish, cats or wolfs, for example, are interacting with one another resulting in use of their environment and resources. Each in the group is responsible for a specific task individually, and sometimes they work together to find an optimal solution of a given task. Common applications include navigation control, interferometry, motion sensing and image processing. There are numerous examples of using swarm algorithms in music composition. However, it must be understood that the computational models of natural phenomena are only mathematical abstractions and lose in complexity presented in the original systems. In this work, another approach is suggested where the artificial agents are replaced by biological ones. Here, the behavior of maggot swarm is mapped on the parameters of sound synthesis algorithms through the video microscopy analyses in real-time. In addition to the synthesized sounds, the close-up recordings made with the different types of microphones are used.
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