Configurable DOA Estimation using Incremental Learning
<|reference_start|>Configurable DOA Estimation using Incremental Learning: 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.<|reference_end|>
@article{xiao2024configurable,
    title={Configurable DOA Estimation using Incremental Learning},
    author={Yang Xiao and Rohan Kumar Das},
    journal={arXiv preprint arXiv:2407.03661},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03661},
    primaryClass={eess.AS cs.SD}
}