  Training convolutional recurrent neural networks on first-order Ambisonics
signals is a well-known approach when estimating the direction of arrival for
speech/sound signals. In this work, we investigate whether increasing the order
of Ambisonics up to the fourth order further improves the estimation
performance of convolutional recurrent neural networks. While our results on
data based on simulated spatial room impulse responses show that the use of
higher Ambisonics orders does have the potential to provide better localization
results, no further improvement was shown on data based on real spatial room
impulse responses from order two onwards. Rather, it seems to be crucial to
extract meaningful features from the raw data. First order features derived
from the acoustic intensity vector were superior to pure higher-order magnitude
and phase features in almost all scenarios.
