Multi Component Signal

Multi-component signal processing focuses on analyzing signals composed of multiple overlapping components, aiming to accurately extract individual component characteristics like frequency and amplitude. Current research emphasizes developing robust algorithms, including Bayesian models and convolutional neural networks, for tasks such as signal detection, denoising, and instantaneous frequency estimation, often leveraging time-frequency representations like spectrograms. Benchmarking efforts are underway to standardize evaluation and comparison of these methods, facilitating progress and promoting reproducibility within the field. Improved signal processing techniques have significant implications for diverse applications, including biomedical imaging and speech analysis.