Peak Detection

Peak detection, the identification of local maxima in data, is crucial across diverse scientific fields, aiming to accurately distinguish significant events from background noise. Current research focuses on improving robustness and efficiency through various approaches, including hidden Markov models, mask-based beamformers, and dynamic programming algorithms like FLOPART, tailored to specific data types such as audio spectrograms, ECG signals, and optical coherence tomography scans. These advancements enhance applications ranging from audio fingerprinting and source localization to medical diagnostics and image analysis, improving accuracy and reducing computational demands in challenging conditions.