Spectral Information

Spectral information, encompassing the distribution of energy across different wavelengths, is crucial for diverse applications, from material classification to astronomical observations and speech enhancement. Current research focuses on leveraging this information effectively, often employing deep learning architectures like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), sometimes integrated with state-space models (like Mamba) or graph-based methods, to address challenges such as high dimensionality and limited labeled data. These advancements improve accuracy and efficiency in tasks like hyperspectral image classification, multichannel speech enhancement, and source-count distribution estimation in astronomy, impacting fields ranging from agriculture to remote sensing and medical imaging.