Low Frequency

Low-frequency signal processing focuses on extracting information and improving the quality of signals with dominant low-frequency components, a challenge due to their limited resolution and susceptibility to noise. Current research emphasizes developing novel algorithms and architectures, such as convolutional neural networks and diffusion models, to enhance low-frequency sound localization, speech enhancement (including super-resolution and bandwidth extension), and image processing tasks like deblurring and inpainting. These advancements have significant implications for various fields, including audio processing, speech recognition, medical imaging, and environmental noise mitigation, by improving the accuracy and efficiency of signal analysis and manipulation.