Comment on “The T-class of time–frequency distributions: Time-only kernels with amplitude estimation”

doi:10.1016/j.jfranklin.2011.07.009

Journal of the Franklin Institute

Volume 348, Issue 9, November 2011, Pages 2670-2673

https://doi.org/10.1016/j.jfranklin.2011.07.009 Get rights and content

Abstract

It is shown that the recently introduced T-class of time–frequency distributions is a subclass of the S-method distributions. From the generalization of the S-method distribution by rotating it in the time–frequency plane, a similar generalization of the T-class distribution follows readily. The generalized T-class distribution is then applicable to signals that behave chirp like, with their instantaneous frequency slowly varying around the slope of the chirp; this slope needs no longer be zero, as is the case for the original T-class distribution, but may take an arbitrary value.

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Cited by (4)

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This paper presents a tutorial review of recent advances in the field of time–frequency $(t, f)$ signal processing with focus on exploiting $(t, f)$ image feature information using pattern recognition techniques for detection and classification applications. This is achieved by (1) revisiting and streamlining the design of high-resolution quadratic time frequency distributions (TFDs) so as to produce adequate $(t, f)$ images, (2) using image enhancement techniques to improve the resolution of TFDs, and (3) defining new $(t, f)$ features such as $(t, f)$ flatness and $(t, f)$ entropy by extending time-domain or frequency-domain features. Comparative results indicate that the new $(t, f)$ features give better performance as compared to time-only or frequency-only features for the detection of abnormalities in newborn EEG signals. Defining high-resolution TFDs for the extraction of new $(t, f)$ features further improves performance. The findings are corroborated by new experimental results, theoretical derivations and conceptual insights.
A review of time-frequency matched filter design with application to seizure detection in multichannel newborn EEG
2014, Digital Signal Processing: A Review Journal
Citation Excerpt :
There are many different types of TFDs which can all be grouped into classes. A commonly used class is the class of QTFDs which includes the Modified B distribution (MBD) that was shown to be a sub-class of the S-method distributions [35,36] and the best for representing newborn EEG seizure signals [7]. It is desired in this paper to test whether the MBD is also suitable for detecting newborn EEG abnormalities using TFMFs or whether one needs to define a more efficient QTFD.
This paper presents a novel design of a time–frequency (t–f) matched filter as a solution to the problem of detecting a non-stationary signal in the presence of additive noise, for application to the detection of newborn seizure using multichannel EEG signals. The solution reduces to two possible t–f approaches that use a general formulation of t–f matched filters (TFMFs) based on the Wigner–Ville and cross Wigner–Ville distributions, and a third new approach based on the signal ambiguity domain representation; referred to as Radon-ambiguity detector. This contribution defines a general design formulation and then implements it for newborn seizure detection using multichannel EEG signals. Finally, the performance of different TFMFs is evaluated for different t–f kernels in terms of classification accuracy using real newborn EEG signals.
Experimental results show that the detection method which uses TFMFs based on the cross Wigner–Ville distribution outperforms other approaches including the existing TFMF-based ones. The results also show that TFMFs which use high-resolution kernels such as the modified B-distribution, achieve higher detection accuracies compared to the ones which use other reduced-interference t–f kernels.
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2023, Signal, Image and Video Processing
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Short communicationComment on “The T-class of time–frequency distributions: Time-only kernels with amplitude estimation”

Abstract

Journal of the Franklin Institute

Signal Processing

A method for time–frequency analysis

IEEE Transactions on Signal Processing

An analysis of some time–frequency and time-scale distributions

Annales des Télécommunications

Short communication
Comment on “The T-class of time–frequency distributions: Time-only kernels with amplitude estimation”