Monaural Speech Enhancement

Monaural speech enhancement aims to isolate clean speech from noisy single-channel recordings, improving speech intelligibility and quality. Current research heavily utilizes deep neural networks, particularly exploring variations of convolutional and recurrent architectures, transformers, and generative adversarial networks, often incorporating attention mechanisms and complex-valued processing to better capture temporal and spectral dependencies. These advancements are driving improvements in speech recognition systems and assistive listening technologies, particularly in challenging acoustic environments like those encountered by drones or in noisy public spaces. The field is also actively investigating efficient model designs and self-supervised learning techniques to reduce computational costs and data requirements.