Adaptive Filter

Adaptive filters are algorithms designed to dynamically adjust their parameters to optimize signal processing in non-stationary environments, aiming for accurate signal estimation and noise reduction. Current research emphasizes robust adaptive filtering techniques, particularly those employing neural networks, graph neural networks, and reinforcement learning to handle impulsive noise, missing data, and complex signal dependencies, often within subband or frequency-domain frameworks. These advancements are significantly impacting various fields, including audio processing (echo cancellation, speech enhancement), image processing (super-resolution), and sensor networks (signal estimation), by improving the accuracy and efficiency of signal processing tasks.