AudioSR is a powerful tool for audio super-resolution. However, its performance can be significantly influenced by the characteristics of the input data, especially the cutoff pattern.
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Input Audio with Unfamiliar Cutoff Patterns
If the input audio file contains a cutoff pattern that is significantly different from those used in training, AudioSR may fail to perform effectively. -
Input Audio with Severe Distortions
Strong distortions such as excessive noise or reverb can degrade the performance of AudioSR.
During training, our data was simulated using low-pass filtering. The model was not trained to handle other causes of high-frequency loss, such as MP3 compression. As a result, AudioSR struggles when encountering unfamiliar cutoff patterns.
For example, MP3 compression can introduce a cutoff pattern that looks like this:
As you can see, there are spectrogram holes near the cutoff range, which differ significantly from the patterns seen during training. When you apply AudioSR to such data, the output may look like this:
The higher frequencies are not adequately inpainted due to the unfamiliar cutoff pattern.
To mitigate this issue, you can perform a low-pass filtering on the audio before feeding it into AudioSR. After low-pass filtering, the audio would resemble a standard low-pass cutoff pattern, like this:
When processed by AudioSR, the output will then be as expected, with improved high-frequency inpainting:
By understanding the limitations and addressing them with preprocessing, you can maximize the performance of AudioSR!