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.

翻译：本研究提出了一种用于波达方向估计的渐进式神经网络模型——DOA-PNN，以解决在适应动态声学环境时因灾难性遗忘带来的挑战。传统方法如广义互相关、多重信号分类和可控响应功率相位变换在静态环境下表现良好，但在噪声和混响条件下性能下降。深度学习模型，特别是卷积神经网络，虽有所改进，但在训练与推理阶段的配置失配问题上仍存在困难。所提出的DOA-PNN通过引入持续学习中的任务增量学习机制，克服了这些限制，能够在不同声学场景中自适应调整，同时减少对已学知识的遗忘。该模型采用任务特定子网络和缩放机制，有效管理参数增长，确保在增量式麦克风配置下保持高性能。我们在基于不同麦克风间距的模拟数据上对DOA-PNN进行了研究。结果表明，该模型能够以最小的参数增加维持性能表现，为波达方向估计提供了一种高效解决方案。