Geometry-Conditioned Pair Representation and Covariance Reconstruction for Multi-Source Direction-of-Arrival Estimation with Microphone Arrays

Microphone-array direction-of-arrival (DOA) estimators must cope with changes in array layout, sensor availability, and the number of active sources. We present GCPR-DOA, a geometry-conditioned pair representation that encodes each cross-spectral pair together with its microphone baseline and frequency before masked aggregation, so that one network processes arrays with 4 to 32 microphones. Two decoders use this representation: a set decoder supervised through analytic positive-semidefinite covariance reconstruction, and a spectral-moment decoder that forms a geometry-weighted steered coherence spectrum and counts sources from energy-aware features. On the LOCATA corpus, paired three-seed ablations show that geometry conditioning lowers the optimal subpattern assignment (OSPA) error by 1.35 degrees and the fixed-output angular error by 9.7 degrees. Trained on the same queries as an adapted SRP-DNN and evaluated on 46,457 common queries, the spectral-moment decoder reaches median single-source errors of 5.5–5.8 degrees, compared with 8.8–9.0 degrees, and higher active-query F1 (0.51–0.54 versus 0.47–0.49); on the three arrays other than DICIT, the two methods are comparable (5.7–5.8 versus 6.3 degrees). On the original 33,497-frame evaluation, localized F1 at ten degrees rises from 0.137 to 0.568–0.597, above all classical references.