Audio Restoration

Audio restoration aims to recover high-fidelity audio from degraded recordings, driven by increasing demands for better sound quality in entertainment and communication. Current research heavily utilizes deep learning, focusing on generative models like GANs and diffusion models, often incorporating techniques like frequency band splitting and multi-guidance strategies to improve restoration accuracy across various audio types (speech and music) and degradation forms (noise, gaps, bandwidth limitations). These advancements significantly enhance the quality of restored audio, impacting fields ranging from music archiving to speech processing, and offering improved user experiences in various applications.