  Ambisonics, a popular format of spatial audio, is the spherical harmonic (SH)
representation of the plane wave density function of a sound field. Many
algorithms operate in the SH domain and utilize the Ambisonics as their input
signal. The process of encoding Ambisonics from a spherical microphone array
involves dividing by the radial functions, which may amplify noise at low
frequencies. This can be overcome by regularization, with the downside of
introducing errors to the Ambisonics encoding. This paper aims to investigate
the impact of different ways of regularization on Deep Neural Network (DNN)
training and performance. Ideally, these networks should be robust to the way
of regularization. Simulated data of a single speaker in a room and
experimental data from the LOCATA challenge were used to evaluate this
robustness on an example algorithm of speaker localization based on the
direct-path dominance (DPD) test. Results show that performance may be
sensitive to the way of regularization, and an informed approach is proposed
and investigated, highlighting the importance of regularization information.
