Reliability-Conditioned Product-Tangent Learning for Degraded Acoustics

Synchronized acoustic descriptors offer complementary views of one signal, but they do not fail uniformly: impulsive, co-channel, and additive interference perturb their coordinates, norms, and temporal structure in different ways. A corrupted high-energy descriptor can therefore dominate Euclidean relation learning even when another domain retains useful evidence. We study this failure as reliability-conditioned geometric representation learning. The proposed RMAL CRNN first estimates coordinate reliability, then maps every descriptor domain to its own learned spherical tangent space. Head-specific tangent-metric attention learns cross-domain relations only after this correction, and a factorized nonnegative sparse affinity refines the fused sequence over time. Controlled simulations isolate the response of these stages to individual and compound degradations. Across a public ten-class UAV benchmark and an independent eight-class Glasgow/ACSAC2022 acoustic-identity release, the complete model reaches 86.30% and 93.88% on the ten-class benchmark at −5 and 0 dB, and 81.18% at 0 dB on Glasgow versus 74.28% for CRNN. Multi-seed ablations and Holm corrected paired tests consistently favor the complete path. These results support reliability-before-relation learning for the evaluated descriptors, data, and corruption regimes; they do not imply universal robustness to unseen domains or degradations.