  Audio bandwidth extension aims to expand the spectrum of narrow-band audio
signals. Although this topic has been broadly studied during recent years, the
particular problem of extending the bandwidth of historical music recordings
remains an open challenge. This paper proposes BEHM-GAN, a model based on
generative adversarial networks, as a practical solution to this problem. The
proposed method works with the complex spectrogram representation of audio and,
thanks to a dedicated regularization strategy, can effectively extend the
bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is
designed to be applied as a second step after denoising the recording to
suppress any additive disturbances, such as clicks and background noise. We
train and evaluate the method using solo piano classical music. The proposed
method outperforms the compared baselines in both objective and subjective
experiments. The results of a formal blind listening test show that BEHM-GAN
significantly increases the perceptual sound quality in early-20th-century
gramophone recordings. For several items, there is a substantial improvement in
the mean opinion score after enhancing historical recordings with the proposed
bandwidth-extension algorithm. This study represents a relevant step toward
data-driven music restoration in real-world scenarios.
