Single Channel Speech Enhancement

Single-channel speech enhancement aims to improve the quality and intelligibility of speech recordings degraded by noise, focusing on methods that process only a single audio channel. Current research emphasizes developing deep learning models, including transformers, conformers, autoencoders, and U-Nets, often incorporating techniques like complex spectral processing, loss function modifications (e.g., artifact-aware losses), and multiscale architectures to enhance performance. This field is crucial for improving the robustness of speech recognition systems and other audio applications in real-world noisy environments, with ongoing efforts to minimize processing artifacts that can hinder downstream tasks.