What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study
Neural networks outperform classical GCC-PHAT for Time-Difference-of-Arrival (TDOA) estimation in noise and reverberation, yet their internal strategy remains unexplored. To uncover it, we turn GCC-PHAT's mathematical steps into diagnostic targets, probing hidden layers of three architectures (MLP, CNN, Transformer) and complementing with gradient attribution and causal frequency masking. We find that cross-power computation consistently emerges across all architectures and conditions, while PHAT whitening, the defining step of GCC-PHAT, fails to emerge. Instead, networks learn a magnitude-aware frequency weighting that preserves per-frequency reliability information discarded by PHAT. Th...
| Authors | Yaozhong Kang, Jiang Wang, Runwu Shi et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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