  Knowledge of loudspeaker responses are useful in a number of applications,
where a sound system is located inside a room that alters the listening
experience depending on position within the room. Acquisition of sound fields
for sound sources located in reverberant rooms can be achieved through labor
intensive measurements of impulse response functions covering the room, or
alternatively by means of reconstruction methods which can potentially require
significantly fewer measurements. This paper extends evaluations of sound field
reconstruction at low frequencies by introducing a dataset with measurements
from four real rooms. The ISOBEL Sound Field dataset is publicly available, and
aims to bridge the gap between synthetic and real-world sound fields in
rectangular rooms. Moreover, the paper advances on a recent deep learning-based
method for sound field reconstruction using a very low number of microphones,
and proposes an approach for modeling both magnitude and phase response in a
U-Net-like neural network architecture. The complex-valued sound field
reconstruction demonstrates that the estimated room transfer functions are of
high enough accuracy to allow for personalized sound zones with contrast ratios
comparable to ideal room transfer functions using 15 microphones below 150 Hz.
