SRMD: Sparse Random Mode Decomposition

TL;DR

SRMD introduces a sparse, randomized approach to time-frequency analysis that efficiently decomposes signals into intrinsic modes, outperforming existing methods in accuracy and computational cost.

Contribution

The paper presents a novel sparse random feature method for signal decomposition that improves efficiency and mode separation in time-frequency analysis.

Findings

Outperforms state-of-the-art decomposition methods on benchmarks.

Reduces sampling and computational costs through randomization.

Effectively separates time-frequency clusters for intrinsic mode identification.

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 the benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.