Audio Reconstruction

Audio reconstruction focuses on recovering high-fidelity audio signals from various representations, such as spectrograms, compressed data, or even brain activity. Current research emphasizes developing efficient and robust methods using generative models like diffusion models, autoencoders, and implicit neural representations, often incorporating techniques from digital signal processing and optimal transport theory to improve reconstruction quality and speed. These advancements have implications for applications ranging from audio enhancement and compression to brain-computer interfaces and forensic audio analysis, driving progress in both fundamental signal processing and its practical applications.