Sound Field Synthesis Model for Real-Time Virtual Acoustics

The patent applies preliminary action by pre-training a neural network model using high-precision simulation data before actual application. The model is trained offline with comprehensive training data, then deployed for real-time inference. This separates the heavy computational workload of training from the real-time operation phase, enabling both high precision and real-time performance.

The patent creates a computational model that copies the essential characteristics of high-precision sound field simulations. Instead of performing complex physical simulations in real-time, the system uses a neural network that has learned the mapping relationships from input parameters to sound field characteristics, producing similar results much faster.

The patent changes the fundamental parameters of the calculation model by replacing traditional physics-based simulation algorithms with a data-driven neural network model. The model takes input parameters (sound source information, virtual space information) and outputs sound field characteristics through learned transformations, achieving both speed and accuracy.

The patent substitutes traditional mechanical/computational simulation methods with a neural network-based system. Instead of performing explicit physical calculations involving wave propagation, reflection, and absorption, the system uses a trained neural network to directly compute sound field characteristics, dramatically reducing computational complexity while maintaining accuracy.