Quantile-Frequency Analysis and Deep Learning for Signal Classification

Abstract

This paper proposes a new method for signal classification based on a combination of recently introduced nonlinear spectral analysis technique called quantile-frequency analysis (QFA) and deep-learning (DL) image classifiers. The QFA method converts a one-dimensional signal into a two-dimensional representation of the signal's oscillatory behavior in the frequency domain at different quantiles or a sequence of such representations that are localized in time. The DL image classifiers utilize these representations for signal classification. The benefit of this QFA-DL classification method, especially in comparison with the traditional method based on the power spectrum and spectrogram, is demonstrated by a numerical experiment using real-world data from a nondestructive evaluation (NDE) application.