{"ID":2832117,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06999","arxiv_id":"2512.06999","title":"Singing Timbre Popularity Assessment Based on Multimodal Large Foundation Model","abstract":"Automated singing assessment is crucial for education and entertainment. However, existing systems face two fundamental limitations: reliance on reference tracks, which stifles creative expression, and the simplification of complex performances into non-diagnostic scores based solely on pitch and rhythm. We advocate for a shift from discriminative to descriptive evaluation, creating a complete ecosystem for reference-free, multi-dimensional assessment. First, we introduce Sing-MD, a large-scale dataset annotated by experts across four dimensions: breath control, timbre quality, emotional expression, and vocal technique. Our analysis reveals significant annotation inconsistencies among experts, challenging the validity of traditional accuracy-based metrics. Second, addressing the memory limitations of Multimodal Large Language Models (MLLMs) in analyzing full-length songs, we propose VocalVerse. This efficient hybrid architecture leverages a lightweight acoustic encoder to model global performance features and long-term dependencies. Third, to address automated metric shortcomings, we establish the H-TPR (Human-in-the-loop Tiered Perceptual Ranking) benchmark, which evaluates a model's ability to generate perceptually valid rankings rather than predicting noisy ground-truth scores.","short_abstract":"Automated singing assessment is crucial for education and entertainment. However, existing systems face two fundamental limitations: reliance on reference tracks, which stifles creative expression, and the simplification of complex performances into non-diagnostic scores based solely on pitch and rhythm. We advocate fo...","url_abs":"https://arxiv.org/abs/2512.06999","url_pdf":"https://arxiv.org/pdf/2512.06999v1","authors":"[\"Zihao Wang\",\"Ruibin Yuan\",\"Ziqi Geng\",\"Hengjia Li\",\"Xingwei Qu\",\"Xinyi Li\",\"Songye Chen\",\"Haoying Fu\",\"Roger B. Dannenberg\",\"Kejun Zhang\"]","published":"2025-12-07T21:06:16Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}