Supervised Learning for Multi Zone Sound Field Reproduction under Harsh Environmental Conditions
This addresses the challenge of accurate sound reproduction in real-world environments for audio engineering applications, but it appears incremental as it applies a known method (supervised learning) to a specific domain issue.
The paper tackles the problem of multi zone sound field reproduction under harsh environmental conditions like wind and temperature stratification, which traditional methods neglect, and shows that using supervised learning to model these effects can improve acoustic contrast and reproduction error by up to 16 dB in their setup.
This manuscript presents an approach for multi zone sound field reproduction using supervised learning. Traditional multi zone sound field reproduction methods assume constant speed of sound, neglecting nonlinear effects like wind and temperature stratification. We show how to overcome these restrictions using supervised learning of transfer functions. The quality of the solution is measured by the acoustic contrast and the reproduction error. Our results show that for the chosen setup, even with relatively small wind speeds, the acoustic contrast and reproduction error can be improved by up to 16 dB, when wind is considered in the trained model.