Supervised Learning for Multi Zone Sound Field Reproduction under Harsh Environmental Conditions

TL;DR

This paper introduces a supervised learning approach to improve multi zone sound field reproduction in harsh environmental conditions by accounting for nonlinear effects like wind and temperature stratification, significantly enhancing acoustic contrast and reducing reproduction error.

Contribution

It presents a novel supervised learning method that incorporates environmental effects into sound field reproduction, overcoming limitations of traditional models assuming constant sound speed.

Findings

Acoustic contrast improved by up to 16 dB with wind consideration.

Reproduction error reduced significantly when environmental effects are modeled.

Method effective even at relatively small wind speeds.

Abstract

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.