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 represen-tation,outlier removal,and mode decomposition.On benchmark tests,we show that our approach outperforms other state-of-the-art decomposition methods.
Sparse random featuresSignal decompositionShort-time Fourier transform
Nicholas Richardson、Hayden Schaeffer、Giang Tran
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University of Waterloo,Waterloo,ON,Canada
University of British Columbia,Vancouver,BC,Canada
NETL
NSTL
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