  Deep learning (DL) based direction of arrival (DOA) estimation is an active
research topic and currently represents the state-of-the-art. Usually, DL-based
DOA estimators are trained with recorded data or computationally expensive
generated data. Both data types require significant storage and excessive time
to, respectively, record or generate. We propose a low complexity online data
generation method to train DL models with a phase-based feature input. The data
generation method models the phases of the microphone signals in the frequency
domain by employing a deterministic model for the direct path and a statistical
model for the late reverberation of the room transfer function. By an
evaluation using data from measured room impulse responses, we demonstrate that
a model trained with the proposed training data generation method performs
comparably to models trained with data generated based on the source-image
method.
