SRMD: Sparse Random Mode Decomposition
This provides a more efficient tool for signal processing tasks like mode decomposition, but it appears incremental as it builds on existing random feature and sparsification techniques.
The paper tackled the problem of time-frequency analysis by proposing a sparse random mode decomposition method that reduces sampling and computational costs, and it outperformed other state-of-the-art decomposition methods in benchmark tests.
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 the benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.