Neural Mode Estimation

Peng Sun, Zhenyu Wen, Yejian Zhou, Zhen Hong, Tao Lin

Abstract: Mode decomposition methods are the current workhorse for the analysis of non-stationary signals. However, current attempts at these methods mainly focus on improving accuracy, leaving computational efficiency untouched. To this end, we leverage the neural mode decomposition technique and propose an open-source Neural Mode Estimation (NME) to deliver a large speedup (at least 50×) while maintaining accuracy. Specifically, we transform the mode decomposition problem into an extremum problem of a functional in the cosine transform domain and train a neural network to approximate the solution. We demonstrate in extensive empirical results that NME can provide an improved trade-off between speed and accuracy, enabling fast, high-quality, stable mode decomposition of non-stationary signals.