1960s Tamil cinema’s musical heritage lacks adequate metadata identifying playback singers in archival recordings. We present a quality-aware adversarial ensemble approach addressing two critical challenges: (1) variable audio degradation requiring adaptive model selection, and (2) instrumentation leakage confounding singer-specific features. We curate 348 annotated clips (12 hours) spanning 48 singers from 179 films. Our methodology introduces: a reliability estimation network dynamically gating five complementary pre-trained speaker models (Wav2Vec2, ECAPA-TDNN, WeSpeaker, CAM++, ERes2NetV2) based on degradation characteristics; adversarial training disentangling singer identity from accompaniment style; and uncertainty-calibrated predictions for human-in-the-loop workflows. On a held-out test set of 52 clips, we achieve 96.2% accuracy (95% CI: [87.5%, 99.2%]) and 2.0% EER (95% CI: [1.2%, 3.1%]), representing 7.7% absolute improvement over the best single model and 2.0% over static ensemble fusion. Ablations show quality-aware gating contributes 2.0% and adversarial disentanglement 2.0% beyond standard ensembles. We publicly release the dataset and code with fixed splits.