Wavelet Packet Feature Extraction Research Articles

Wavelet Packet and Granular Computing With Application to Communication Emitter Recognition

Individual recognition of communication emitter can obtain the working mechanism, system parameters, and the usage model of the emitters based on the separation and interception of the specific emitter signals. In order to realize the individual recognition of communication emitter, a method of communication emitter recognition based on wavelet packet feature extraction and attribute reduction of granular computing is proposed. The method uses hierarchical and granularity to represent the subtle feature information of different emitter signals contained in the original data after the decomposition of wavelet packet, so that the feature attributes of different emitters are efficiently mapped to different levels of granular structure which can be used to search for an optimal reduced attributes operation for signal recognition. And then, the reduced attributes are used to distinguish the emitter signals correctly by label propagation method. The experimental results show that the proposed method based on wavelet packet feature extraction and granular computing attribute reduction has good classification performance in individual recognition of communication emitters.

Classification of Welding Flaws in Gamma Radiography Images Based on Multi-scale Wavelet Packet Feature Extraction Using Support Vector Machine

This paper has introduced a digital process aimed at automatically identify and classify flaws in the weld joints. Therefore, various algorithms are presented for computer aided detection (CAD) and classification. These algorithms include preprocessing algorithms using Gaussian pyramidal transform, contrast enhancement algorithm using contrast stretch and normalization method, noise reduction algorithm by blind image separation (BIS), measurement of the noise separation quality and image segmentation algorithm based on expectation-maximization (EM) method. Also, an algorithm for detection and classification of welding defects from radiographic images is presented. This algorithm is based on multi-scale wavelet packet (MWP) technique for feature extraction. Also, extraction of features from its transform domains is proposed to assist in achieving a higher classification rate. Moreover, the support vector machine (SVM) is applied for matching the extracted features. Consequently, classification error was computed for both normal and defect images. The obtained results confirm that much higher classification error was computed for defect image due to various objects may be in the image. Moreover, the accuracy of the considered algorithms is determined by statistical measurements. These algorithms have the potential for further improvement because of their simplicity and encouraging results. Therefore, it will motivate real-time flaws detection and classification for many CAD applications.

A Wavelet Packet and Mel-Frequency Cepstral Coefficients-Based Feature Extraction Method for Speaker Identification

Abstract One of the most widely used approaches for feature extraction in speaker recognition is the filter bank-based Mel Frequency Cepstral Coefficients (MFCC) approach. The main goal of feature extraction in this context is to extract features from raw speech that captures the unique characteristics of a particular individual. During the feature extraction process, the discrete Fourier transform (DFT) is typically employed to compute the spectrum of the speech waveform. However, over the past few years, the discrete wavelet transform (DWT) has gained remarkable attention, and has been favored over the DFT in a wide variety of applications. The wavelet packet transform (WPT) is an extension of the DWT that adds more flexibility to the decomposition process. This work is a study of the impact on performance, with respect to accuracy and efficiency, when the WPT is used as a substitute for the DFT in the MFCC method. The novelty of our approach lies in its concentration on the wavelet and the decomposition level as the parameters influencing the performance. We compare the performance of the DFT with the WPT, as well as with our previous work using the DWT. It is shown that the WPT results in significantly lower order for the Gaussian Mixture Model (GMM) used to model speech, and marginal improvement in accuracy with respect to the DFT. WPT mirrors DWT in terms of the order of GMM and can perform as well as the DWT under certain conditions.

Acoustic Emission Source Identification Based on Pattern Recognition Method

A new pattern recognition method based on harmonic wavelet packet (HWPT) and hierarchy support vector machine (H-SVM) is proposed to solve the fatigue damage identification problem of helicopter component. In this approach, HWPT is used to extract the energy feature of acoustic emission (AE) signals on different frequency bands and to reduce the dimensionality of original data features. The H-SVM classifier is used to identify the AE source type. A subset of the experimental data for known AE source type is used to train the H-SVM classifier, the remaining set of data is used to test the H-SVM classifier. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The results indicate that the proposed approach can implement AE source type identification effectively, and has better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.3587

Gear fault detection based on adaptive wavelet packet feature extraction and relevance vector machine

In this article, a novel fault detection method based on adaptive wavelet packet feature extraction and relevance vector machine (RVM) is proposed for incipient fault detection of gear. First, ten statistical characteristics in time domain and all node energies of full wavelet packet tree are extracted as candidate features. Then, Fisher criterion is applied to evaluate the discrimination power of each feature. Finally, two optimal features from time domain and wavelet domain, respectively, are selected to be used as inputs to the RVM. Furthermore, moving average is applied to each feature to improve accuracy for online continuous fault detection. By combining wavelet packet transform with Fisher criterion, it is able to adaptively find the optimal decomposition level and select the global optimal features. The RVM, a Bayesian learning framework of statistical pattern recognition, is adopted to train the fault detection model. The RVM was compared with the popular support vector machine (SVM) with the increase of training samples. Experimental results validate the effectiveness of the proposed method, and indicate that RVM is more suitable than SVM for online fault detection.

Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor ( k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.

Adapted filter banks for feature extraction in transcranial magnetic stimulation evoked responses

A novel adaptive and approximate shift-invariant wavelet packet feature extraction scheme for event-related potentials (ERPs) in the electroencephalogram (EEG) is introduced in this paper. In this algorithm, the shift-invariant wavelet packed decomposition is done by integrating a cost function for decimation decision in each sub-band expansion. Additionally, a shape adaptation of the wavelet is implemented to find the best adapted wavelet shape for a given class of ERPs. This scheme is used to analyze the time course of the impact of single-pulse transcranial magnetic stimulation (TMS) to the auditory ERPs. We show that the proposed scheme is able to extract even slightest impacts of TMS, making it a promising tool for the extraction of weak ERPs components, particularly in hybrid TMS-EEG/ERP setups.

Estimation of the knee joint angle from surface electromyographic signals for active control of leg prostheses

The surface electromyographic (SEMG) signal is very convenient for prosthesis control because it is non-invasively acquired and intrinsically related to the user's intention. This work presents a feature extraction and pattern classification algorithm for estimation of the intended knee joint angle from SEMG signals acquired using two sets of electrodes placed on the upper leg. The proposed algorithm uses a combination of time-domain and frequency-domain approaches for feature extraction (signal amplitude histogram and auto-regressive coefficients, respectively), a self-organizing map for feature projection and a Levenberg–Marquardt multi-layer perceptron neural network for pattern classification. The new algorithm was quantitatively compared with the method proposed by Wang et al (2006 Med. Biol. Eng. Comput. 44 865–72), which uses wavelet packet feature extraction, principal component analysis and a multi-layer perceptron neural classifier. The proposed method provided lower error-to-signal percentage and peak error amplitudes, higher correlation and fewer error events. The algorithm presented in this work may be useful as part of a myoelectric controller for active leg prostheses designed for transfemoral amputees.

AUTOMATIC FROG CALLS MONITORING SYSTEM: A MACHINE LEARNING APPROACH

Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.

Wavelet packet feature extraction for vibration monitoring

Condition monitoring of dynamic systems based on vibration signatures has generally relied upon Fourier-based analysis as a means of translating vibration signals in the time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from the expansion coefficients because the information is diluted across the whole basis. The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures. The resulting WPT coefficients provide one with arbitrary time-frequency resolution of a signal. With the aid of statistical-based feature selection criteria, many of the feature components containing little discriminant information could be discarded, resulting in a feature subset having a reduced number of parameters without compromising the classification performance. The extracted reduced dimensional feature vector is then used as input to a neural network classifier. This significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability.

A Wavelet Packet Approach to Transient Signal Classification

Time–frequency transforms, including wavelet and wavelet packet transforms, are generally acknowledged to be useful for studying non-stationary phenomena and, in particular, have been shown or claimed to be of value in the detection and characterization of transient signals. In many applications time–frequency transforms are simply employed as a visual aid to be used for signal display. Although there have been several studies reported in the literature, there is still considerable work to be done investigating the utility of wavelet and wavelet packet time–frequency transforms for automatic transient signal classification. This paper contributes to this ongoing investigation through the development of a non-parametric wavelet packet feature extraction procedure which identifies features to be used for the classification of transient signals for which explicit signal models are not available or appropriate. Recent literature in this area is devoted to truly ad-hoc, high-dimensional, non-parametric types of classification in which one or more time–frequency transform forms the base from which a large number of features are determined by trial and error. In contrast, the wavelet-packet-based procedure presented in this paper was formulated to systematically adapt to any data dictionary within which several classes must be distinguished. This method is aimed at focusing the information in the data set to find the smallest number of features for robust, reliable classification. The promise of our method is illustrated by performing our procedure on a set of biologically generated underwater acoustic signals. For this example the wavelet-packet-based features obtained by our method yield excellent classification results when used as input for a neural network and a nearest neighbor rule.