复兴Etter方法用于自回归修复：泛化、评估与实现
Reviving Etter method for autoregressive inpainting: Generalization, evaluation, implementation
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- Brno University of Technology（布尔诺理工大学）
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中文总结 AI 辅助
本文复兴Etter自回归音频修复方法，提供开放实现与两种扩展，在80毫秒间隙的音乐片段上评估，性能与多数基线相当，但不及Janssen方法。
中文摘要 AI 辅助
音频修复旨在恢复音频波形中缺失的片段，例如在丢帧和丢包情况下出现的缺失。本文重新审视了Etter提出的自回归（AR）插值方法，该方法通过一个结构化线性系统结合前向和后向AR预测，但缺乏广泛使用的音频信号完整实现。我们提供了一个开放实现，并提出了两个实用扩展：一种允许在间隙长度短于模型阶数时使用高阶AR模型的公式，以及一种针对丢包隐藏的因果变体。该方法在长达80毫秒间隙的音乐片段上进行了评估，使用信噪比（SNR）和感知动机的客观差异等级，并辅以听力测试。结果表明，Etter修复的性能与大多数基于AR和稀疏性的基线方法相当，但迭代的、逐间隙的Janssen方法除外。
英文摘要
Audio inpainting aims to restore missing segments in an audio waveform, as encountered in dropouts and packet losses. This paper revisits the autoregressive (AR) interpolation method proposed by Etter, which combines forward and backward AR prediction through a structured linear system, yet lacks a widely used full implementation for audio signals. We provide an open implementation and propose two practical extensions: A formulation that allows high-order AR models even when the gap length is shorter than the model order, and a causal variant tailored to packet loss concealment. The method is evaluated on musical excerpts with gaps up to 80 ms, using SNR and perceptually motivated objective difference grades, complemented by a listening test. The results show that Etter inpainting matches the performance of most AR- and sparsity-based baseline methods, with the exception of the iterative, gap-wise Janssen approach.
