@inproceedings{balakrishnan-thayasivam-2026-quality,
    title = "Quality-Aware Adversarial Ensemble for Singer Identification in 1960s {T}amil Film Music",
    author = "Balakrishnan, Sathiyakugan  and
      Thayasivam, Uthayasanker",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.10/",
    pages = "150--159",
    ISBN = "979-8-89176-383-8",
    abstract = "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."
}