[4N1-GS-7] Vision, speech media processing:

[4N1-GS-7-04] Fundamental Study on Sound Field Estimation Using Deep Operator Networks with Room Shape

〇Gen Sato¹, Yusuke Ikeda¹ (1. Tokyo Denki University)

Keywords:Acoustic engineering, Sound field simulation, Room acoustics, Deep learning

Sound field simulation is used in various applications, such as acoustic design of concert halls, and generation of sound fields in virtual spaces. In particular, sound field simulation based on the wave equation can provide highly accurate results. However, estimation time needs to be improved. Therefore, sound field estimation using deep learning has been proposed to reduce the computation time required for the simulation. On the other hand, when deep learning is used, the estimated sound field is typically limited to a fixed grid. Therefore, we proposed Deep Operator Networks (DeepONets) to estimate the sound field at arbitrary locations. In particular, we aim to improve generalization performance of DeepONet for different room shapes by introducing a convolutional neural network into the Branch network of DeepONet. Experiments were conducted in rectangular room sound fields, and the results demonstrated that DeepONet can estimate the sound field with a high accuracy, achieving a mean SNR of approximately 24 dB.