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Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription — arXiv:2609.21610

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21610
License (as archived): CC0-1.0
Archived: 2026-10-10T03:12:15.609Z
SHA-256 of the archived text: 989599d909a62345497994e4e29de96e2e57561e96daa17fb199ac8665dfbb58
Stable URL of this page: https://metraipolis.com/documento/dc_38effbc7780f

How to cite

Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription — arXiv:2609.21610 — arXiv (en), https://arxiv.org/abs/2609.21610 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_38effbc7780f (Metraipolis archive dc_38effbc7780f, sha256 989599d909a62345497994e4e29de96e2e57561e96daa17fb199ac8665dfbb58)

BibTeX

@misc{dc38effbc7780f,
  title = {Confidence-Guided Markov Weighting for Semi-Supervised Tabla Stroke Transcription — arXiv:2609.21610},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21610},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_38effbc7780f; sha256 989599d909a62345497994e4e29de96e2e57561e96daa17fb199ac8665dfbb58},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21610. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tabla Stroke Transcription (TST) converts tabla audio into symbolic stroke sequences, but the scarcity of annotated recordings makes fully supervised training challenging. We propose a semi-supervised framework that uses sequence-level labelled and unlabelled tabla recordings. A teacher model generates pseudo-label sequences, while a Stroke-Level Confidence Estimation Model (S-CEM) estimates confidence for each predicted stroke. To improve training with uncertain pseudo-labels, we propose Confidence-Guided Markov Weighted Alternative Temporal Classification (CMW-ATC), which weights candidate sequences within uncertain spans using learned stroke transitions and reliable neighbouring strokes. Experiments across three TST evaluation settings show consistent improvements over conventional teacher--student training and Alternative Temporal Classification in its replacement form (ATC-R). Ablation studies further show the contributions of S-CEM, Markov transition weighting, and the use of reliable strokes on both sides of uncertain spans.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-21 in eess.AS).
Categorie arXiv: Audio and Speech Processing (eess.AS).
Pagina del lavoro: https://arxiv.org/abs/2609.21610 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:01:34.415Z dalla pagina https://arxiv.org/catchup/eess.AS/2026-09-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).