Teager Energy Operator Research Articles

Classification of speech under stress using harmonic peak to energy ratio

This paper explores the analysis and classification of speech under stress using a new feature, harmonic peak to energy ratio (HPER). The HPER feature is computed from the Fourier spectra of speech signal. The harmonic amplitudes are closely related to breathiness levels of speech. These breathiness levels may be different for different stress conditions. The statistical analysis shows that the proposed HPER feature is useful in characterization of various stress classes. Support Vector Machine (SVM) classifier with binary cascade strategy is used to evaluate the performance of the HPER feature using simulated stressed speech database (SSD). The performance results show that the HPER feature successfully characterizes different stress conditions. The performance of the HPER feature is compared with the mel frequency cepstral coefficients (MFCC), the Linear prediction coefficients (LPC) and the Teager-Energy-Operator (TEO) based Critical Band TEO Autocorrelation Envelope (TEO-CB-Auto-Env) features. The proposed HPER feature outperforms the MFCC, LPC and TEO-CB-Auto-Env features. The combination of the HPER feature with the MFCC feature further increases the system performance.

Baseline-free damage identification of metallic sandwich panels with truss core based on vibration characteristics

A baseline-free damage identification method is proposed to identify damages in metallic sandwich panels with truss core in the article. The method is based on flexibility matrix and gapped smoothing method, with damage index defined DIm. The weight coefficient m is introduced to consider the effect of damages on both low-order modes and high-order modes. Numerical simulations and experiments are conducted to evaluate the present method. Besides, damage index is also defined by processing DIm with Teager energy operator, and comparisons between DIm and are also carried out. Results show that the proposed method is effective in detecting single damage and multiple damages of the same or different extent. The weight coefficient m plays a very important role in identification of multiple damages of different styles. When comparing with , it is found that the present index DIm is better at suppressing the singularity caused by contact nodes and detecting of multiple damages which contain small or slight damages.

Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

Abstract Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signalto-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).

Real‐time ballistocardiographic artifact reduction using the k‐teager energy operator detector and multi‐channel referenced adaptive noise cancelling

ABSTRACTThe simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) recording technique has recently received considerable attention and has been used in many studies on cognition and neurological disease. EEG‐fMRI simultaneous recording has the advantage of enabling the monitoring of brain activity with both high temporal resolution and high spatial resolution in real time. The successful removal of the ballistocardiographic (BCG) artifact from the EEG signal recorded during an MRI is an important prerequisite for real‐time EEG‐fMRI joint analysis. We have developed a new framework dedicated to BCG artifact removal in real‐time. This framework includes a new real‐time R‐peak detection method combining a k‐Teager energy operator, a thresholding detector, and a correlation detector, as well as a real‐time BCG artifact reduction procedure combining average artifact template subtraction and a new multi‐channel referenced adaptive noise cancelling method. Our results demonstrate that this new framework is efficient in the real‐time removal of the BCG artifact. The multi‐channel adaptive noise cancellation (mANC) method performs better than the traditional ANC method in eliminating the BCG residual artifact. In addition, the computational speed of the mANC method fulfills the requirements of real‐time EEG‐fMRI analysis. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 209–215, 2016

Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator

Vibration signal of wind turbine has the non-linear and non-stationary characteristic, thus it is difficult to extract the fault feature. In this study, a novel method based on variational mode decomposition (VMD) and Teager energy operator (TEO) is proposed to diagnose the bearing faults of wind turbine. First, vibration signal is decomposed into several intrinsic mode function (IMF) components by means of VMD, which is a recently proposed signal decomposition method. Then, the most sensitive IMF component is selected according to kurtosis criterion. Moreover, TEO is applied to the most sensitive IMF in order to highlight impact signal. Finally, spectrum is obtained by applying Fourier transform to Teager energy of the selected IMF, thus extracting the fault feature to diagnose bearing fault. The effectiveness of the proposed method for fault diagnosis is validated by simulation and experimental signal analysis results, and comparison studies show its advantage over empirical mode decomposition and conventional spectrum analysis for wind turbine bearing fault diagnosis.

Source Digital Voice Recorder Identification by Wavelet Analysis

Identification of the speech signal origin is an important issue since it may play a vital role for criminal and forensic investigations. Yet, in the media forensics field, source digital voice recorder (DVR) identification has not been given much attention. In this paper we study the effect of subband based features obtained using uniform wavelet packet decomposition and Teager energy operator on the DVR model and brand identification performance. In order to assess the effects of these features on the proposed system, one-class classifiers (OCCs) with two reference multi-class classifiers were carried out. The performance of the DVR identification system is tested on a custom database of twelve portable DVRs of six different brands. The results showed that the proposed system can effectively identify the correct DVR brands/models with a high accuracy. Moreover, it was observed that the combination of the traditional speech features with subband Teager energy cepstral parameters (STEC) and short time frame energy as a feature improved recognition accuracy under both silent and noisy recording conditions.

Traveling‐wave‐based protection of parallel transmission lines using Teager energy operator and fuzzy systems

This study proposes an intelligent traveling-wave (TW)-based protection algorithm for parallel transmission lines. In the proposed algorithm, Karenbauer's phase to modal transform is applied on three phase current signals. Teager energy operator is then applied on the modal components to extract TWs. First extracted TW of each modal component along with well-designed fuzzy systems are used for internal fault identification and fault type classification. In order to find the fault location, the time difference between the first and the second TWs and the TW propagation speed are utilised. Test signals are generated in PSCAD/EMTDC software and the algorithm is implemented in MATLAB. Results show that the proposed algorithm is an ultra-high-speed algorithm for the reliable protection of parallel transmission lines.

Improved Teager Energy Operator and Improved Chirp-Z Transform for Parameter Estimation of Voltage Flicker

Effective estimation of voltage flicker components plays an important role in distribution systems for either flicker meters or flicker compensators. A novel approach has been presented in this paper to accurately estimate voltage flicker components by using the improved Teager energy operator (ITEO) and the improved chirp-Z transform (ICZT). The error correction factor $K$ of the Teager energy operator is presented and ITEO is established to reduce the extraction errors of voltage flicker waveform. ICZT is used to extract the frequency and magnitude of the voltage envelope which is corrected by the $K$ factor of ITEO. The effects of signal sampling rate, sampling number, spectrum subdivision points of ICZT, voltage harmonics and interharmonics, frequency fluctuation, and white noise are investigated. The implementation of the proposed approach in the digital-signal-processor platform is also introduced. Multiple simulation and experimental test application results validate the accuracy and efficiency of the proposed approach.

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis.

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

Over the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition (EMD) or the Ensemble EMD (EEMD). The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed.

A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

Ultra-high-speed protection of transmission lines using traveling wave theory

A new traveling-wave-based algorithm is proposed in this paper for the protection of transmission lines. In the proposed algorithm, Teager energy operator is used to extract traveling waves. Teager energy operator has a high computational efficiency and extracts traveling waves with high resolution. The amplitude of the first detected traveling wave as well as that of the traveling waves which are detected in 2L/v and 2(L−x)/v seconds (“L” is the line length, x is an initial guess about the fault location, and “v” is the traveling wave propagation speed) after the first one are used to discriminate internal and external faults. Then the traveling wave propagation speed and the time difference between the first and the second traveling waves are used to find the fault location. This process can also be used for the internal fault identification. Finally, two decision trees and the amplitude of initial current traveling waves in different modes are used for the fault type classification and faulted phase selection. The feasibility of the proposed algorithm is investigated by test signals which are simulated in PSCAD/EMTDC, and the algorithm is implemented in MATLAB.

Single-Phase Grid Voltage Frequency Estimation Using Teager Energy Operator-Based Technique

This paper reports the performance of a technique for the estimation of single-phase grid voltage fundamental frequency under distorted grid conditions. The technique combines a Teager energy operator (TEO) with a frequency adaptive bandpass filter (BPF). The TEO is based on three consecutive samples and is used to estimate the fundamental frequency. The BPF relies on a recursive discrete Fourier transform (RDFT) and an inverse RDFT, and is used to extract the normalized amplitude of the grid voltage fundamental component. The technique is computationally efficient and can also reject the negative effects caused by dc offset and harmonics. It requires less computational effort, can provide faster estimation, and is also less affected by harmonics as compared with a technique relying on the RDFT-based decomposition of the single-phase system into orthogonal components. The performance of the technique is verified using both simulation and experimental results.

A novel approach for analysis of altered gait variability in amyotrophic lateral sclerosis.

Gait variability reflects important information for the maintenance of human beings' health. For pathological populations, changes in gait variability signal the presence of abnormal motor control strategies. Quantitative analysis of the altered gait variability in patients with amyotrophic lateral sclerosis (ALS) will be helpful for either diagnosing or monitoring pathological progression of the disease. Thus, we applied Teager energy operator, an energy measure that can highlight the deviations from moment to moment of a time series, to produce an instantaneous energy time series. Then, two important features were extracted to assess the variability of the new time series. First, the standard deviation statistics were used to measure the magnitude of the variability. Second, to quantify the temporal structural characteristics of the variability, the permutation entropy was applied as a tool from the nonlinear dynamics. In the classification experiments, the two proposed features were input to the support vector machine classifier, and the dataset consists of 12 ALS patients and 16 healthy control subjects. The experimental results showed that an area of 0.9643 under the receiver operating characteristic curve was achieved, and the classification accuracy evaluated by leave-one-out cross-validation method could reach 92.86%.

Adaptive Energy-Based Acoustic Sperm Whale Echolocation Click Detection

Sperm whales (Physeter macrocephalus) generate a series of broadband impulsive echolocation signals called clicks when diving in search of prey. These acoustic signals can be divided into usual clicks and creak clicks. They have a frequency spectrum that lies primarily between 2 and 18 kHz. In this paper, an adaptive energy-based click detector called the adaptive Teager energy operator (ATEO) is developed. A windowing technique is used to account for the time-varying characteristics of these signals, and the window length is adapted by estimating the interclick interval (ICI). The usual clicks and creak clicks are classified via ICI differences. The proposed method is compared with several well-known techniques using real data provided by the Atlantic Undersea Test and Evaluation Center (AUTEC). Performance results are presented which show that the proposed technique is a robust and efficient detector that can improve the click detection true positive rate (TPR) as well as reduce the false positive rate (FPR).

Automatic identification of Cyclic Alternating Pattern (CAP) sequences based on the Teager Energy Operator.

The Cyclic Alternating Pattern (CAP) is a periodic cerebral activity prevalent during Non-Rapid Eye Movement (NREM) sleep-stages. The CAP is composed by A-phases that are related to a change in amplitude, frequency or both from the background activity epochs, called B-phases. Depending on the type of increase the A-phase could be classified as A1, A2 or A3 subtype. This paper proposes the usage of the Teager Energy Operator (TEO) to analyze the amplitude changes in the different frequency-bands to detect A-phases subtypes. The TEO classification performance is compared with the performance of a state-of-the art EEG feature, applied previously for CAP scoring and referred as the macro-micro structure descriptor (MMSD). In general, the TEO is the best feature and the improved results were obtained in the delta band for the A1 and A2 sub-types. More precisely, a sensitivity and specificity of 80.31% and 82.93% were obtained for the A1 subtype, respectively. A2 phases were detected with 76.96% of sensitivity and 73.22% of specificity. The two features detected A3 subtype with approximately the same sensitivity (approx. 70%) and specificity (approx. 75%), however the results were improved by considering the highest frequency band. These results are consistent with the frequency content of the different sub-phases.

Improving the accuracy of speech emotion recognition using acoustic landmarks and Teager energy operator features

Affective computing can help us achieve more intelligent user interfaces by adding the ability to recognize users’ emotions. Human speech contains information about the emotional state of the speaker and can be used in emotion recognition systems. In this paper, we present a machine learning approach using acoustic features which improves the accuracy of speech emotion recognition. We used 698 speech samples from ”Emotional Prosody Speech and transcripts” corpus to train and test the classifiers. The emotions used were happy, sadness, hot anger, panic, and neutral. Mel-frequency Cepstral Coefficients (MFCC), Teager Energy Operator (TEO) features, and acoustic landmark features were extracted from speech samples. Models were trained using multinomial logistic regression, k-Nearest Neighbors(k-NN) and Support Vector Machine(SVM) classifiers. The results show that adding landmark and TEO features to MFCC features improves the accuracy of classification. SVM classifiers with a Gaussian kernel had the best performance with an average accuracy of 90.43%. We achieved significant improvement in the accuracy of the classification compared to a previous study using the same dataset.

Two-dimensional curvature mode shape method based on wavelets and Teager energy for damage detection in plates

Vibration-based damage detection in plates has been investigated by various methods relying on mode shapes, among which the 2D curvature mode shape is a damage feature attracting much attention of researchers. Unlike the sound understanding of the use of the 1D curvature mode shape for detecting damage in beams, however, use of the 2D curvature mode shape to detect damage in plates is not yet well elucidated, major unresolved issues including lack of clarity about the mechanism of characterizing damage, susceptibility to noise, and insensitivity to sight damage. These deficiencies severely hamper use of the 2D curvature mode shape to portray damage in plates. To deal with these deficiencies, the mechanism of using 2D curvature mode shape to depict damage is analytically clarified in light of thin plate theory. On the basis of this clarification, a synergy between wavelet transform and a Teager energy operator is proposed to tackle the other deficiencies of susceptibility to noise and insensitivity to sight damage, leading an enhanced 2D curvature mode shape. The efficacy of the enhanced 2D curvature mode shape is numerically demonstrated using finite element simulations and experimentally validated through noncontact measurement by a scanning laser vibrometer, whereby its advances of clear mechanism of characterizing damage, robustness against noise, and sensitivity to slight damage are sufficiently corroborated.

Rolling bearing fault diagnosis based on LCD–TEO and multifractal detrended fluctuation analysis

A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition–Teager Energy Operator (LCD–TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert–Huang transform (HHT) combined with MF-DFA or LCD–TEO combined with mono-fractal analysis.