Optimizing Audio Performance through Speaker Equalization Calibration Heuristics in Controlled Acoustic Environments

Achieving optimal audio performance in controlled environments presents a significant challenge due to the complex effects of room acoustics, which introduce distortions. While automated speaker equalization (EQ) solutions exist, they are often limited by proprietary algorithms that lack transparency and accessibility. This study aims to explore and compare various heuristics for speaker calibration, incorporating both traditional and innovative algorithmic approaches, to assess their effectiveness in optimizing audio performance. The research is conducted in an acoustically treated environment, utilizing a calibrated measurement microphone and tone sweeps to capture baseline frequency responses. Four distinct heuristics are evaluated: deviation-based EQ adjustments, K-Means clustering, logarithmic band distribution, and an AI-driven approach leveraging machine learning techniques. EQ corrections are applied through specialized software, and post-calibration frequency responses are analyzed using the Manhattan distance metric. The study employs statistical comparisons to evaluate the accuracy, efficiency, and practicality of each method.