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@ -5,7 +5,7 @@ Official repository of the paper:
> E. Moliner and V. Välimäki,, "A two-stage U-Net for high-fidelity denosing of historical recordinds", in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, May, 2022 > E. Moliner and V. Välimäki,, "A two-stage U-Net for high-fidelity denosing of historical recordinds", in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Singapore, May, 2022
## Abstract ## Abstract
abstract here Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural network. A two-stage U-Net model architecture is designed to model and suppress the degradations with high fidelity. The method processes the time-frequency representation of audio, and is trained using realistic noisy data to jointly remove hiss, clicks, thumps, and other common additive disturbances from old analog discs. The proposed model outperforms previous methods in both objective and subjective metrics. The results of a formal blind listening test show that the method can denoise real gramophone recordings with an excellent quality. This study shows the importance of realistic training data and the power of deep learning in audio restoration.
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<img src="https://user-images.githubusercontent.com/64018465/131505025-e4530f55-fe5d-4bf4-ae64-cc9a502e5874.png" alt="Schema represention" <img src="https://user-images.githubusercontent.com/64018465/131505025-e4530f55-fe5d-4bf4-ae64-cc9a502e5874.png" alt="Schema represention"