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Neural audio super-resolution models are typically trained on low- andhigh-resolution audio signal pairs. Although these methods achieve highlyaccurate super-resolution if the acoustic characteristics of the input data aresimilar to those of the training data, challenges remain: the models sufferfrom quality degradation for out-of-domain data, and paired data are requiredfor training. To address these problems, we propose Dual-CycleGAN, ahigh-quality audio super-resolution method that can utilize unpaired data basedon two connected cycle consistent generative adversarial networks (CycleGAN).Our method decomposes the super-resolution method into domain adaptation andresampling processes to handle acoustic mismatch in the unpaired low- andhigh-resolution signals. The two processes are then jointly optimized withinthe CycleGAN framework. Experimental results verify that the proposed methodsignificantly outperforms conventional methods when paired data are notavailable. Code and audio samples are available fromhttps://chomeyama.github.io/DualCycleGAN-Demo/.

Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs