Signal-Informed Temporal Routing for Vinyl Defect Regime Detection

Abstract:Vinyl restoration systems must distinguish isolated clicks, short bursts, dense crackle, and overlapping damage before selecting a repair operation. We present a lightweight two-stage detector in which signal-informed sparse, burst, and dense experts produce complementary defect evidence, and a temporal backend converts that evidence into stable repair regimes. The backend factors the five-way decision hierarchically, applies a validation-only mixed regime gate, and decodes with validation-selected transition penalties that outperform a maximum-likelihood transition matrix on the same emissions. On a source-separated synthetic benchmark of 597 non-overlapping 15 s excerpts drawn from 21 recordings, the held-out system reaches 0.730 pooled five-class macro F1 (95% CI 0.694-0.761), an improvement of 0.043 over a flat frame-level router (paired bootstrap p=0.001). Clean and dense frames are highly reliable, while sparse, burst, and mixed frames remain far more ambiguous.

5 pages, 1 figure, 4 tables. Columbia University Nonlinear Control Lab Technical Report CUNLC-20260919-01. Submitted to ICASSP 2026

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)

arXiv:2609.23164 [eess.AS]

(or arXiv:2609.23164v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.23164 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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