Full Band Speech Enhancement

Full-band speech enhancement aims to improve the quality of audio recordings by removing noise and artifacts across the entire audible frequency spectrum (up to 24 kHz), exceeding the capabilities of traditional wideband methods. Current research focuses on developing efficient deep learning models, often employing multi-stage or sub-band processing architectures with attention mechanisms and learnable spectral compression to address computational constraints and improve high-frequency performance. These advancements are significant for improving speech intelligibility and quality in various applications, such as hearing aids, voice assistants, and telecommunications, particularly in noisy environments.