Speech Enhancement Network

Speech enhancement networks aim to improve the quality of speech signals degraded by noise, reverberation, or other distortions, primarily focusing on enhancing intelligibility and perceptual quality. Recent research emphasizes developing lightweight, efficient models—often employing U-Net architectures, transformers, or two-stage designs—that incorporate techniques like multi-attention mechanisms, self-supervised learning, and complex spectral processing (including explicit phase estimation). These advancements are crucial for deploying speech enhancement in resource-constrained environments like mobile devices and real-time communication systems, impacting fields such as hearing aids, voice assistants, and telecommunications.