Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey <s> IV. EXTRACTION OF RELEVANT FEATURES <s> The Teager Energy Operator is a nonlinear operator defined for both ::: continuous and discrete time signals. In this thesis we define ::: different versions and extensions of the one-dimensional operator, and ::: then look at the properties and positivity of the operator. Its ::: connection to Volterra systems is explained. The Teager Energy ::: Operator is used for demodulation of AM-FM signals, and we look at how ::: this can be done using the operator and nonlinear methods (Hilbert ::: transform, Prony's, Modified covariance, etc). As the Teager Energy ::: Operator is sensitive to noise, the multiband demodulation technique ::: is introduced to refine the estimates. The performance of linear and ::: nonlinear differentiators is investigated. An application to speech ::: analysis is shown, using a multiband method to locate formants in ::: speech. Finally, a performance comparison in different levels of noise ::: is done. <s> BIB001 </s> Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey <s> IV. EXTRACTION OF RELEVANT FEATURES <s> Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time-frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time-frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time-frequency representations, and feature classifiers. The results of the literature review indicate that time-frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future. <s> BIB002
The extraction of relevant features leads to reduced data dimensionality. It is often necessary to use some kind of decision making process in order to classify the data based on the extracted features. Methods such as PCA can also lead to the additional data dimensionality reduction. Sejdic et al. BIB002 have organised feature extraction and decision making using signal processing techniques according to Fig. 5 . Here, a classification of feature extraction methods in three subgroups has been made, depending on the domain in which the collected data are analysed. The reader will notice that various signal processing techniques (e.g. Wavelet and Fourier transforms, TEO, HHT, etc.) are often used in the surveyed literature and therefore they are often mentioned throughout this text. No special introduction to these techniques will be written because it is beyond the scope of this paper, however the reader is referred to the following literature  , BIB001     or any other signal processing textbook. 