Comparison and Evaluation of Machine Learning Models for Acoustic Optimization in Live Events

Acoustic quality in live events directly affects audience engagement, yet static calibration methods fail to adapt to real-time environmental and occupancy changes. This study compiles impulse-response data from diverse venues and systematically evaluates linear regression, decision trees, CNN, LSTM, and a CNN–LSTM hybrid for predicting RT60, EDT, D50, C50, and C80. The hybrid model achieved the highest accuracy (RT60 RMSE = 0.097 s, R2 = 0.9997) with sub-100 ms inference, enabling real-time deployment. Integrated with IoT sensors tracking temperature, humidity, and audience density, it dynamically adjusts acoustic parameters and outperforms traditional approaches. These results demonstrate the feasibility of ML- and IoT-driven autonomous acoustic control, enhancing sound fidelity, speech intelligibility, and operational efficiency in live-event environments.