sFMt

sFMt stands for the sparse Frequency-Modulation transform, a proposed framework in signal processing for representing non-stationary signals with a compact, interpretable set of components. The central idea is that many real-world signals can be approximated by a small number of frequency-modulated elements whose instantaneous frequencies and envelopes vary in a structured, predictable way. By enforcing sparsity on the number of components and imposing alignment constraints on the modulation patterns, sFMt seeks to capture rapid spectral changes without overfitting.

In the sFMt model, a signal is viewed as a sum of components, each consisting of a carrier at a frequency that changes over time according to a modulation function, multiplied by an amplitude envelope. The approach emphasizes joint estimation of component amplitudes, instantaneous frequencies, and modulation shapes under a sparsity-promoting objective. Variants discuss convex relaxations, greedy pursuit methods, and Bayesian formulations to select the number of components and stabilize estimation.

Current status describes sFMt as a topic in theoretical discussions and early exploratory experiments rather than a standardized technique. There is no widely adopted algorithm, and performance depends on signal properties such as component separation and noise level. Researchers explore applications in audio analysis, speech processing, radar, and biomedical signal interpretation, where non-stationarity and compact representations are valuable.

See also: sparse representation, Fourier transform, frequency modulation, non-stationary signal processing.