Noise Reduction

Noise reduction research aims to improve the quality of signals by mitigating unwanted interference, focusing on enhancing speech intelligibility, image clarity, and overall signal fidelity across various applications. Current efforts concentrate on developing efficient deep learning models, including convolutional neural networks, variational autoencoders, and generative adversarial networks, often incorporating techniques like Kalman filtering and multi-modal data fusion to achieve superior performance with reduced computational costs. These advancements have significant implications for diverse fields, from improving hearing aids and assistive technologies to enhancing medical imaging and industrial processes.