Problem
Research questions and friction points this paper is trying to address.
Vinyl Restoration
Defect Detection
Signal-Informed
Temporal Routing
Signal-Informed Temporal Routing for Vinyl Defect Regime Detection
🤖 AI Summary
该研究提出一种轻量级两阶段检测器，通过信号指导的稀疏、突发和密集专家生成互补缺陷证据，并由时间后端转换为稳定的修复方案，以解决黑胶唱片不同损伤类型的识别问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
signal-informed temporal routing
two-stage detector
defect regime detection
hierarchical decision-making
validation-selected transition penalties
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