  This study introduces a progressive neural network (PNN) model for direction
of arrival (DOA) estimation, DOA-PNN, addressing the challenge due to
catastrophic forgetting in adapting dynamic acoustic environments. While
traditional methods such as GCC, MUSIC, and SRP-PHAT are effective in static
settings, they perform worse in noisy, reverberant conditions. Deep learning
models, particularly CNNs, offer improvements but struggle with a mismatch
configuration between the training and inference phases. The proposed DOA-PNN
overcomes these limitations by incorporating task incremental learning of
continual learning, allowing for adaptation across varying acoustic scenarios
with less forgetting of previously learned knowledge. Featuring task-specific
sub-networks and a scaling mechanism, DOA-PNN efficiently manages parameter
growth, ensuring high performance across incremental microphone configurations.
We study DOA-PNN on a simulated data under various mic distance based
microphone settings. The studies reveal its capability to maintain performance
with minimal parameter increase, presenting an efficient solution for DOA
estimation.
