Update README.md
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A ".wav" file with the denoised version, as well as the residual noise and the original signal in "mono", will be generated in the same directory as the input file.
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A ".wav" file with the denoised version, as well as the residual noise and the original signal in "mono", will be generated in the same directory as the input file.
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## Training
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## Training
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TODO
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To retrain the model, follow the instructions:
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Download the [Gramophone Noise Dataset](http://research.spa.aalto.fi/publications/papers/icassp22-denoising/media/datasets/Gramophone_Record_Noise_Dataset.zip), or any other dataset containing recording noises.
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Prepare a dataset of clean music (e.g. [MusicNet](https://zenodo.org/record/5120004#.YnN-96IzbmE))
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## Remarks
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## Remarks
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The trained model is specialized in denoising gramophone recordings, such as the ones included in this collection https://archive.org/details/georgeblood. It has shown to be robust to a wide range of different noises, but it may produce some artifacts if you try to inference in something completely different.
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The trained model is specialized in denoising gramophone recordings, such as the ones included in this collection https://archive.org/details/georgeblood. It has shown to be robust to a wide range of different noises, but it may produce some artifacts if you try to inference in something completely different.
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