Audio bandwidth extension involves the realistic reconstruction of high-frequency spectra from bandlimited observations. In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem. This paper introduces a novel method called BABE (Blind Audio Bandwidth Extension) that addresses the blind problem in a zero-shot setting, leveraging the generative priors of a pre-trained unconditional diffusion model. During the inference process, BABE utilizes a generalized version of diffusion posterior sampling, where the degradation operator is unknown but parametrized and inferred iteratively. The performance of the proposed method is evaluated using objective and subjective metrics, and the results show that BABE surpasses state-of-the-art blind bandwidth extension baselines and achieves competitive performance compared to informed methods when tested with synthetic data. Moreover, BABE exhibits robust generalization capabilities when enhancing real historical recordings, effectively reconstructing the missing high-frequency content while maintaining coherence with the original recording. Subjective preference tests confirm that BABE significantly improves the audio quality of historical music recordings. Examples of historical recordings restored with the proposed method are available on the companion webpage: (http://research.spa.aalto.fi/publications/papers/ieee-taslp-babe/)

翻译：音频带宽扩展涉及从带限观测中逼真重建高频频谱。当低通退化未知时（例如在修复历史音频录音中），这成为一个盲问题。本文提出了一种名为BABE（盲音频带宽扩展）的新方法，该方法利用预训练无条件扩散模型的生成先验，在零样本场景中解决盲问题。在推理过程中，BABE采用扩散后验采样的一种广义版本，其中退化算子未知但通过参数化方式迭代推断。通过客观和主观指标评估所提方法的性能，结果显示：在合成数据测试中，BABE超越了最先进的盲带宽扩展基线方法，并与有信息方法相比达到具有竞争力的性能。此外，在增强真实历史录音时，BABE展现出强大的泛化能力，能够在保持与原录音一致性的同时有效重建缺失的高频内容。主观偏好测试证实，BABE显著提升了历史音乐录音的音频质量。采用所提方法修复的历史录音示例可在配套网页获取：（http://research.spa.aalto.fi/publications/papers/ieee-taslp-babe/）