Book contents
- Frontmatter
- Dedication
- Contents
- Acknowledgements
- Preface
- Notation
- 1 Introduction
- Part I Basics and Constraints
- 2 Small Data Are Beautiful
- 3 Of Signals and Noise
- 4 On Time, Frequency, and Gauss
- 5 Uncertainty
- 6 From Time and Frequency to Time-Frequency
- 7 Uncertainty Revisited
- 8 On Stationarity
- Part II Geometry and Statistics
- References
- Index
6 - From Time and Frequency to Time-Frequency
from Part I - Basics and Constraints
Published online by Cambridge University Press: 22 August 2018
- Frontmatter
- Dedication
- Contents
- Acknowledgements
- Preface
- Notation
- 1 Introduction
- Part I Basics and Constraints
- 2 Small Data Are Beautiful
- 3 Of Signals and Noise
- 4 On Time, Frequency, and Gauss
- 5 Uncertainty
- 6 From Time and Frequency to Time-Frequency
- 7 Uncertainty Revisited
- 8 On Stationarity
- Part II Geometry and Statistics
- References
- Index
Summary
The simplest and most intuitive time-frequency transforms are the Short-Time Fourier Transform (STFT) and the associated spectrogram (squared STFT), which are in close connection with the Gabor transform when using a Gaussian window. Given a 1D signal, its STFT is a 2D function whose redundancy is controlled by a reproducing kernel. This kernel can be equivalently seen as an ambiguity function, i.e., a 2D time-frequency correlation function that is constrained by uncertainty. Building upon the classical duality which exists between “correlation functions” and “energy distributions,” we end up with the Wigner distribution as a central tool for time-frequency signal analysis. The Wigner distribution happens however to be a member of larger classes (such as Cohen's) to which the spectrogram belongs too.
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- Explorations in Time-Frequency Analysis , pp. 40 - 49Publisher: Cambridge University PressPrint publication year: 2018