Background Noise

Background noise, encompassing unwanted sounds interfering with desired signals, is a pervasive challenge across diverse fields, from industrial anomaly detection to speech enhancement and music generation. Current research focuses on developing sophisticated algorithms, including diffusion models and dynamic expectation maximization, to model and mitigate the effects of various noise types, ranging from white noise to spatially and temporally correlated "colored" noise. These advancements are crucial for improving the accuracy and robustness of numerous applications, such as audio fingerprinting, reinforcement learning, and state estimation in dynamic systems.