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ON INSTANTANEOUS FREQUENCY

HUANG, NORDEN E., WU, ZHAOHUA, LONG, STEVEN R., ARNOLD, KENNETH C., CHEN, XIANYAO, BLANK, KARIN·2009·Advances in Adaptive Data Analysis

Abstract

Instantaneous frequency (IF) is necessary for understanding the detailed mechanisms for nonlinear and nonstationary processes. Historically, IF was computed from analytic signal (AS) through the Hilbert transform. This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF. The first approach is to compute the quadrature (defined here as a simple 90° shift of phase angle) directly. The second approach is designated as the normalized Hilbert transform (NHT), which consists of applying the Hilbert transform to the empirically determined FM signals. Additionally, we have also introduced alternative methods to compute local frequency, the generalized zero-crossing (GZC), and the teager energy operator (TEO) methods. Through careful comparisons, we found that the NHT and direct quadrature gave the best overall performance. While the TEO method is the most localized, it is limited to data from linear processes, the GZC method is the most robust and accurate although limited to the mean frequency over a quarter wavelength of temporal resolution. With these results, we believe most of the problems associated with the IF determination are resolved, and a true time–frequency analysis is thus taking another step toward maturity.

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Metadata

Authors
HUANG, N. E., WU, Z., LONG, S. R., ARNOLD, K. C., CHEN, X., & BLANK, K.
Venue
Advances in Adaptive Data Analysis
Published
2009-04
Volume
01
Issue
02
Pages
177-229
ISSN
  • 1793-7175 (ISSN-L)
  • 1793-5369
OpenAlex
W2028497691
Cited by
670
References
1
FWCI
47.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: ON INSTANTANEOUS FREQUENCY. https://app.cochat.ai/papers/10.1142/s1793536909000096
The paper
HUANG, N. E., WU, Z., LONG, S. R., ARNOLD, K. C., CHEN, X., & BLANK, K. (2009). ON INSTANTANEOUS FREQUENCY. Advances in Adaptive Data Analysis. https://doi.org/10.1142/s1793536909000096

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.