7 papers with code • 0 benchmarks • 0 datasets
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In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension.
Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its low-resolution corrupted observation.
TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining
We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension.
Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.