Artificial reverberation is a fundamental process in music production; audio post-production. However, the large ; highly interdependent parameter spaces of modern reverberation algorithms make the identification of perceptually optimal configurations difficult, particularly when attempting to minimize audible artifacts. This paper presents a knowledge-driven framework for reverberation parameter optimization that evaluates candidate configurations using rule-based audio quality constraints derived from perceptual; signal-processing principles. The system automatically detects; prevents common artifacts including spectral obfuscation, clipping, spatial collapse,; ringing phenomena. Instead of relying on data-driven training procedures, the proposed approach employs declarative reasoning to model audio engineering knowledge; systematically constrain parameter exploration. Experimental evaluation demonstrates that the framework successfully reduces artifact occurrence across diverse audio material while maintaining computational feasibility. The results suggest that knowledge-based reasoning can provide an interpretable; controllable alternative to data-driven optimization strategies in audio signal processing.
