提出通用自回归建模框架，提升音频信号修复效果
Regularized autoregressive modeling and its application to audio signal reconstruction
- 构建可正则化的通用自回归建模框架
- 在音乐和语音去削波任务中表现优于现有方法
- 适用于音频修复场景，尤其适合语音去削波
自回归（AR）建模在信号处理中具有重要价值，尤其在语音与音频领域。已有研究通过正则化时间域信号值或AR系数来引入先验信息或实现数值稳定，但缺乏统一且通用的建模框架。本文提出一种通用框架及相应的优化问题与算法，分析其计算开销，并探讨多种改进对收敛速度的影响。实验部分验证了该方法在音频去削波与去量化任务中的有效性，与当前最优方法对比显示，在音乐信号去削波中具备竞争力，在语音去削波中表现更优。评估包含一种新提出的广义线性预测（GLP）启发式算法，该算法此前仅以专利形式出现，尚未在学术界公开。
原文摘要 · Abstract (English)
Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community.
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