A Dual-Step-U-Net for Crystal-Clear Restoration of Audio Recordings
Prabhakar Marry, Arukali Preethi, Kandhi Bhuvan, Aluvala Ravali, Cherupalli Linesh · 2023
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.