High Frequency
High-frequency analysis focuses on extracting information from rapidly changing signals or data, aiming to improve accuracy and efficiency in various applications. Current research emphasizes leveraging frequency domain information alongside spatial or temporal data, employing techniques like wavelet transforms, Fourier transforms, and specialized neural network architectures such as Transformers and Graph Neural Networks. This approach is proving valuable across diverse fields, including financial modeling (high-frequency trading), image processing (super-resolution, compression), and signal processing (noise reduction, medical imaging), leading to improved model performance and more efficient algorithms.
Papers
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
Jingxia Jiang, Jinbin Bai, Yun Liu, Junjie Yin, Sixiang Chen, Tian Ye, Erkang Chen
Frequency bin-wise single channel speech presence probability estimation using multiple DNNs
Shuai Tao, Himavanth Reddy, Jesper Rindom Jensen, Mads Græsbøll Christensen