SRMD: Sparse Random Mode Decomposition

Abstract

Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram. The randomization is both in the time window locations and the frequency sampling, which lowers the overall sampling and computational cost. The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes, and thus leads to a new data-driven mode decomposition. The applications include signal representation, outlier removal, and mode decomposition. On benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.

Funding

Natural Sciences and Engineering Research Council of Canada(RGPIN 50503-10842); Air Force Office of Scientific Research(MURI FA9550-21-1-0084); Directorate for Mathematical and Physical Sciences(1752116); Natural Sciences and Engineering Research Council of Canada