  We introduce a block-online variant of the temporal feature-wise linear
modulation (TFiLM) model to achieve bandwidth extension. The proposed
architecture simplifies the UNet backbone of the TFiLM to reduce inference time
and employs an efficient transformer at the bottleneck to alleviate performance
degradation. We also utilize self-supervised pretraining and data augmentation
to enhance the quality of bandwidth extended signals and reduce the sensitivity
with respect to downsampling methods. Experiment results on the VCTK dataset
show that the proposed method outperforms several recent baselines in both
intrusive and non-intrusive metrics. Pretraining and filter augmentation also
help stabilize and enhance the overall performance.
