Prior works fix the initial and the
target sampling rates. However, in real-world application scenarios, it cannot be guaranteed that input speech has identical
bandwidth. Thus, each audio super-resolution model must be
trained separately and needs to be adapted for each pair of sampling rates. To address the above problem, we aim to generate
high-resolution audio signals of a fixed sampling rate from inputs of various sampling rates, so that high-quality audio signals
can be obtained from low-quality inputs of any sampling rate.
We call it as general neural audio upsampling task.

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