A Dual-Step-U-Net for Crystal-Clear Restoration of Audio Recordings

This work, which is at the forefront of innovation, uses the powerful capabilities of ConvNet. The proposed method leverages the power of deep learning by developing a novel Dual-Step-U-Net architecture to significantly enhance audio restoration. The proposed approach analyses the audio's time-frequency representation and trained on real-world noisy data to concurrently eliminate common additive disturbances from vintage analog discs, such as hiss, clicks, and thumps. Embarking on the forefront of innovation, this thesis pioneers a revolutionary paradigm in audio restoration and denoising through the formidable prowess of a fully convolutional deep neural network.