Book contents
- Frontmatter
- Dedication
- Contents
- Acknowledgements
- Preface
- Notation
- 1 Introduction
- Part I Basics and Constraints
- Part II Geometry and Statistics
- 9 Spectrogram Geometry 1
- 10 Sharpening Spectrograms
- 11 A Digression on the Hilbert–Huang Transform
- 12 Spectrogram Geometry 2
- 13 The Noise Case
- 14 More on Maxima
- 15 More on Zeros
- 16 Back to Examples
- 17 Conclusion
- 18 Annex: Software Tools
- References
- Index
11 - A Digression on the Hilbert–Huang Transform
from Part II - Geometry and Statistics
Published online by Cambridge University Press: 22 August 2018
Book contents
- Frontmatter
- Dedication
- Contents
- Acknowledgements
- Preface
- Notation
- 1 Introduction
- Part I Basics and Constraints
- Part II Geometry and Statistics
- 9 Spectrogram Geometry 1
- 10 Sharpening Spectrograms
- 11 A Digression on the Hilbert–Huang Transform
- 12 Spectrogram Geometry 2
- 13 The Noise Case
- 14 More on Maxima
- 15 More on Zeros
- 16 Back to Examples
- 17 Conclusion
- 18 Annex: Software Tools
- References
- Index
Summary
Rather than considering the disentanglement of multicomponent nonstationary signals as a time-frequency post-processing, a possibility is to first decompose the observation into modes that are amenable to some further demodulations. In this spirit, this chapter reviews a technique that has recently gained popularity, namely “Empirical Mode Decomposition” and the associated “Hilbert-Huang Transform.” The rationale of those data-driven methods is presented, as well as their actual implementation, with a brief discussion of pros and cons with respect to more conventional time-frequency analysis.
- Type
- Chapter
- Information
- Explorations in Time-Frequency Analysis , pp. 98 - 105Publisher: Cambridge University PressPrint publication year: 2018