A dual-branch fusion network for footstep sound source localization in non-line-of-sight corridors

Non-line-of-sight (NLOS) acoustic source localization using microphone arrays has attracted increasing attention in robotics due to its non-intrusive nature, low cost, and ease of integration. However, existing methods typically rely on accurate environmental models and ideal propagation assumptions, resulting in limited robustness in complex multipath environments. To address these issues, this paper proposes CorridorLocNet, a dual-branch fusion network for footstep sound source localization in NLOS corridors. Specifically, Mel-spectrogram and generalized cross correlation with phase transform features are first concatenated into a joint input. Subsequently, a dual-branch architecture is proposed, comprising a residual convolutional branch to extract local time-frequency patterns and a lightweight Conformer branch to capture global temporal dependencies. Furthermore, a cross-attention module adaptively fuses high-dimensional representations from both branches. Finally, a multi-layer perceptron outputs the estimated position. By learning the complex mapping from spatial sound field variations to source positions, the proposed method provides a robust solution in NLOS corridors. Additionally, a real-world dataset of footsteps occurring behind a corridor corner is constructed for evaluation. Experimental results demonstrate that CorridorLocNet achieves 98.83% classification accuracy and reduces the average error by 2.56 m compared with the reflection-aware localization approach, validating its feasibility in NLOS localization scenarios.