• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1210-1216
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• 2014

This paper deals with an application of Teager Energy Operator (TEO) and Energy Separation Algorithm(ESA) to detect and determine various voltage waveform distortions like harmonics, inter-harmonics and frequency variation. Because the TEO and DESA algorithm was initially proposed for speech or communication analysis, its applications are limited to some types of waveform in the power quality analysis area. For example, an undistorted voltage signal is similar with a pure sinusoid. A voltage fluctuation is very similar with an amplitude-modulated signal, from the viewpoint of signal theory. And a continuous frequency variation is similar with a frequency-modulated signal, which is also known as a chirp signal. This paper is written to show that the TEO and DESA algorithm can be used for detecting occurrences of the representative waveform distortions and determining their instantaneous information of amplitude and frequency.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.42-46
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• 2012

With the development of industry, more large-scale non-linear loads are added to existing power systems and they cause the serious power quality (PQ) problems to the nearby sensitive installations more and more. To protect the important loads and mitigate the impact of PQ disturbances on them, various compensating devices are installed. One of the most important control skills used in the compensating equipment at the load side is how fast they can recognize or detect the discontinuous abnormal PQ events from the normal voltage signal. This paper deals with two estimation methods for the fast detection and tracking of general PQ disturbances: Teager Energy Operator (TEO), which is a non-linear operator and used for a short time energy calculation, and Kalman Filter (KF), which is one of the most universally used estimation techniques. And it is also shown how to apply the TEO and the KF to detect the PQ disturbances such as voltage sag, swell, interruption, harmonics and voltage fluctuation.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.417-426
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• 2010

We present a speech processing strategy incorporating instantaneous frequency (IF) encoding for the enhancement of melody recognition performance of cochlear implants. For the IF extraction from incoming sound, we propose the use of a Teager energy operator (TEO), which is advantageous for its lower computational load. From time-frequency analysis, we verified that the TEO-based method provides proper IF encoding of input sound, which is crucial for melody recognition. Similar benefit could be obtained also from the use of a Hilbert transform (HT), but much higher computational cost was required. The melody recognition performance of the proposed speech processing strategy was compared with those of a conventional strategy using envelope extraction, and the HT-based IF encoding. Hearing tests on normal subjects were performed using acoustic simulation and a musical contour identification task. Insignificant difference in melody recognition performance was observed between the TEO-based and HT-based IF encodings, and both were superior to the conventional strategy. However, the TEO-based strategy was advantageous considering that it was approximately 35% faster than the HT-based strategy.

• Smart Structures and Systems
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• v.26 no.2
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• pp.135-146
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• 2020

This paper deals with an application of adaptive blind source separation (BSS) method, equivariant adaptive separation via independence (EASI), and Teager Energy Operator (TEO) for online identification of structural modal parameters. The aim of adaptive BSS methods is recovering a set of independent sources from their unknown linear mixtures in each step when a new sample is received. In the proposed approach, firstly, the EASI method is used to decompose structural responses into independent sources at each instance. Secondly, the TEO based demodulation method with discrete energy separation algorithm (DESA-1) is applied to each independent source, and the instantaneous frequencies and damping ratios are extracted. The DESA-1 method can provide the fast time response and has high resolution so it is suitable for online problems. This paper also compares the performance of DESA-1 algorithm with Hilbert transform (HT) method. Compared to HT method, the DESA-1 method requires smaller amounts of samples to estimate and has a smaller computational complexity and faster adaption due to instantaneous characteristic. Furthermore, due to high resolution of the DESA-1 algorithm, it is very sensitive to noise and outliers. The effectiveness of the proposed approach has been validated using synthetic examples and a benchmark structure.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.1
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• pp.41-46
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• 2014

In this paper, we propose a novel voice activity detection (VAD) algorithm using feature vectors based on TE (teager energy). Specifically, power spectral deviation (PSD), which is used as the feature for the VAD in the IS-127 noise suppression algorithm, is obtained after the input signal is transfomed by Teager energy operator. In addition, the TE-based likelihhod ratio are derived in each frame to modifiy the PSD for further VAD. The performance of our proposed VAD algorithm are evaluated by objective testing (total error rate, receiver operating characteristics, perceptual evaluation of speech quality) under various environments, and it is found that the proposed method yields better results than conventional VAD algorithms in the non-stationary noise environments under 5 dB SNR (total error rate = 2.6% decrease, PESQ score = 0.053 improvement).

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2442-2447
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• 2015

Because an electric railway vehicle is a large scale moving load, it can cause various kinds of power quality problems in the railroad power system. The power quality impacts are considered as the strong instantaneous stresses to the related power systems and can cause an accelerating aging and a malfunction of the power supplying components. Therefore, it is necessary to detect the small and intermittent symptoms mixed in the voltage waveform. However, they cannot be detected by the triggering functions of the existing power analyzers installed in the railway systems. This paper will examine the drawback of some fast detection tools and propose an advanced detecting and analyzing method based on a combined TEO and STFT algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.816-823
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• 2018

The Auto-synchronizer is essential equipment for synchronizing a generator to the power system. It is performing that measurement of the magnitude, frequency and phase of the voltage signal of the power system and generator. It is important to select the appropriate measurement algorithm for preventing various problem such as mechanical stress and Electrical problem. Teager Energy Operator(TEO) and Discrete separation algorithm(DESA) is measurable the instantaneous parameters of a sine wave using 5 samples and can be measured at a fast and with a simple operation. Therefore it has many advantages in measuring the parameters. In this paper, it confirmed measurement results using matlab simulations when there are synchronized in order of frequency, magnitude. Also it presented methods using digital filters and sample intervals to improve accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.