UDSS-BWE：不确定性驱动的Swin频带扩展
UDSS-BWE: Uncertainty- and Decision-Science Inspired Swin BandWidth Extension
- George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对频带扩展中罕见高频瞬态失真问题，提出UDSS-BWE框架，融合五个决策科学判别器与Swin生成器，以更少参数实现更优感知质量。
中文摘要 AI 辅助
频带扩展（BWE）本质上是局部性的：最具感知失真的并非平均情况失真，而是罕见的高频（HF）瞬态，标准风险中性的目标函数往往会将其平滑掉。为弥合这一差距，我们从风险敏感和不确定性感知的决策科学规则中寻求解决方案，提出了UDSS-BWE，引入了五个决策科学和不确定性感知判别器：CVaRD（通过尾部池化放大高频伪影）、CCD（采用原始-对偶增广拉格朗日方法防止高频过度增强）、MCUD（在频谱平坦度/质心/滚降上学习可学习的效用函数）、EDD（捕获认知不确定性）以及DROD（捕获熵KL-DRO聚合）。UDSS-BWE还被设计为一个复数对抗性BWE框架，采用基于Swin的生成器，即轻量级双流移位窗口骨干网络，以高效捕获局部和长程结构，同时可学习的格点耦合提供受控的跨流交换。UDSS-BWE经过广泛优化，在干净和噪声条件下的英语和法语两个数据集上，以3.89倍更少的参数（72M对比18.5M）实现了更好的感知质量。据我们所知，这项工作展示了如何成功利用多学科决策科学启发和不确定性理论来设计高效判别器，以生成更细腻的音频，为BWE任务建立了新的基线。
英文摘要
Bandwidth extension (BWE) is fundamentally localized: the most perceptual distortions are not average-case distortions, but rare high-frequency (HF) transients that standard, risk-neutral objectives tend to smooth away. To close this gap, we seek solutions in the risk-sensitive and uncertainty-aware decision science rules and present UDSS-BWE, which introduces five decision-science and uncertainty-aware discriminators: CVaRD (does tail pooling to amplify HF artifacts), CCD (a primal-dual augmented Lagrangian to prevent HF overboost), MCUD (a learnable utility over spectral flatness/ centroid/ rolloff), EDD (captures epistemic uncertainty), and DROD (captures entropic KL-DRO aggregation). UDSS-BWE is also designed as a complex valued adversarial BWE framework that uses Swin-based generators, a lightweight dual-stream shifted-window backbone, to capture local and long-range structure efficiently, while learnable lattice coupling provides controlled cross-stream exchange. UDSS-BWE is optimized extensively and achieves better perceptual quality with 3.89x fewer parameters (72M vs.18.5M) over two English and French datasets under clean and noisy conditions. To the best of our knowledge, this work shows how multi disciplinary decision-science-inspired and uncertainty theories can be successfully used to design efficient discriminators for producing more nuanced audios, establishing a new baseline in the BWE task.
