  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.
