Using Genetic Algorithms to Find and Evaluate Parameters for Adaptive Digital Audio Effects
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Cross-adaptive audio effects are audio effects whose parameters are controlled or influenced by features of audio signals other than the one being affected. In other words, there is a mapping from one or several analyzed features of some signal(s) to one or more parameters of an effect processing a different signal. This thesis aims to find and evaluate such mappings using AI methods, specifically a genetic algorithm, with the evaluation of a mapping s fitness being a measure of musical applicability. A possible design and its implementation are presented, and experiments which show that the idea is feasible are carried out.