Noise Spectroscopy

Noise spectroscopy aims to identify and characterize noise sources affecting various systems, from images and quantum computers to audio signals, ultimately improving data quality and system performance. Current research employs machine learning techniques, including deep neural networks (like autoencoders and WaveNets) and reinforcement learning, alongside traditional algorithms like DBSCAN, to analyze noise characteristics and develop effective denoising strategies. These advancements are crucial for enhancing the accuracy of image classification, improving the reliability of quantum computing, and achieving high-fidelity audio processing in applications such as slow-motion video.