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Quantile-Frequency Analysis and Deep Learning for Signal Classification

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Abstract

This paper proposes a new method for signal classification based on a combination of deep-learning (DL) image classifiers and recently introduced nonlinear spectral analysis technique called quantile-frequency analysis (QFA). The QFA method converts a one-dimensional signal into a two-dimensional representation of quantile periodograms (QPER) which represent the signal’s oscillatory behavior in the frequency domain at different quantiles. With a moving window, the QFA method can also covert a signal into a sequence of such two-dimensional representations, called short-time quantile periodograms, that are localized in time to represent the signal’s time-dependent or nonstationary properties. The DL image classifiers take these representations as input for signal classification. The benefit of this QFA-DL classification method in comparison with the traditional frequency-domain method based on the power spectrum and spectrogram is demonstrated by a numerical experiment using real-world ultrasound signals from a nondestructive evaluation application.

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Data Availability

data and computer code available at https://www.math.umd.edu/~bnk/DATA/ and https://github.com/IBM/qfa.

Notes

  1. D. Prahbu, personal communication to B. Kedem, May 25, 1994.

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Acknowledgements

The author would like to thank the anonymous reviewers for constructive comments and suggestions which led to an improved presentation.

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  1. IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598-0218, USA

    Ta-Hsin Li

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Li, TH. Quantile-Frequency Analysis and Deep Learning for Signal Classification. J Nondestruct Eval 42, 40 (2023). https://doi.org/10.1007/s10921-023-00952-y

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