5.7.3 Models
Audio super-resolution has been extensively explored using deep learning architectures [8, 40,
168, 253, 290, 320, 333, 392, 453, 624]. One notable paper by Rakotonirina [453] proposes a novel
network architecture that integrates convolution and self-attention mechanisms for audio super-
resolution. Specifically, they use Attention-based Feature-Wise Linear Modulation (AFiLM) [453]
to modulate the activations of the convolutional model. In another recent work by Yoneyama et al.
[624], the super-resolution task is decomposed into domain adaptation and resampling processes
to handle acoustic mismatch in unpaired low- and high-resolution signals. To address this, they
jointly optimize the two processes within the CycleGAN framework.
AReviewofDeepLearningTechniquesforSpeechProcessing