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 non-blind
filter-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/)Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language
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