Deep Noise Suppression

Deep noise suppression (DNS) aims to enhance speech quality by removing unwanted noise using deep learning models. Current research focuses on improving model efficiency (e.g., through early exiting and lightweight architectures like FullSubNet and its variants), developing reference-free training methods using perceptual metrics like PESQ, and addressing challenges posed by real-world scenarios such as VoIP communications and diverse acoustic environments. These advancements are significant for improving speech recognition, human-computer interaction, and the overall user experience in various applications, particularly in noisy or resource-constrained settings.