EVALUATION OF A WAVELET BASED ASR FRONT-END

2007 ◽  
Vol 05 (04) ◽  
pp. 641-654 ◽  
Author(s):  
OMAR FAROOQ ◽  
SEKHARJIT DATTA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recognition Performance ◽  
Wavelet Packet ◽  
Additive White Gaussian Noise ◽  
Discrete Wavelet ◽  
Mel Frequency Cepstral Coefficients ◽  
Admissible Wavelet ◽  
Transform Coefficients ◽  
Short Time

In this paper, we propose the use of the wavelet transform for the extraction of features for phonemes in order to overcome some of the shortcomings of short time Fourier transform. New log-energy based features are proposed using discrete wavelet transform as well as wavelet packets and their recognition performance has been evaluated. These features overcome the problem of shift variance as encountered in the features based on the discrete wavelet transform coefficients. The effect on the recognition performance by choosing different mother wavelets for the decomposition and window duration is also studied. Finally, a scheme based on the admissible wavelet packet has also been proposed and the results are discussed and compared with the frequently used Mel Frequency Cepstral Coefficients based features. The recognition performance of these features is further evaluated in the presence of different level of additive white Gaussian noise.

Traditional Chinese Medicine Pulse-Condition Simulation and Wavelet Recognition Method

2010 ◽  
Vol 139-141 ◽  
pp. 2029-2032
Author(s):  
Dong Cao ◽  
Jian Wei Ye ◽  
Jun Yi ◽  
Wen Jie Ruan ◽  
Chong Chen
Keyword(s):  
Wavelet Transform ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Discrete Wavelet Transform ◽  
Subjective Experience ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Energy Characteristics ◽  
Pulse Condition

The human pulse-condition diagnosis is an important part of the traditional Chinese medicine (TCM) which is difficult to recognize accurately by doctor’s subjective experience. Objective identification of pulse-conditions has important meanings for modernization of TCM. In this paper human pulse-condition system transfer function model and model parameter estimation were introduced, which are used to construct four kinds of typical pulse-conditions simulation signals. There are normal pulse, taut pulse, slippery pulse and thready pulse. And then, discrete wavelet transform for extracting the multi-scale energy characteristics and wavelet packet decomposition for extracting the multi-band energy characteristics are proposed so as to recognize the pulse-conditions simulation signals. The results show that the recognition effect of discrete wavelet transform method is better. Moreover, the data features of characteristic parameters demonstrate the reality of simulation signals.

scholarly journals Comparison of different feature extraction methods for the analysis of uterine magnetomyography signals to predict term labor

2018 ◽  
Vol 7 (3.29) ◽  
pp. 1
Author(s):  
T Ananda Babu ◽  
Dr P. Rajesh Kumar
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Wavelet Packet Transform ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Term Labor

The prediction of term labor by analyzing the uterine magnetomyographic signals attempted in this research. The existing works did not focus on the classification of the signals. Publicly available MIT-BIH database records were divided into term-labor and term-nonlabor groups. This research presents two methods for feature extraction, discrete wavelet transform and wavelet packet transform. Energy, standard deviation, variance, entropy and waveform length of transform coefficients used in the first method. The normalized logarithmic energy of wavelet coefficients from each packet of the total wavelet packet tree used as the feature space for the second method. The labor assessment done through the classification of the features by using five different classifiers for different mother wavelet families. Discrete wavelet transform features extracted using coif5 wavelet with random subspace classification gives the accuracy, precision and FPrates of 93.9286%, 94.2014% and 5.7986% respectively. Using sym8 wavelet for wavelet packet transform features classified with SVM classifier performed well with 95.8763% accuracy, 95.9719% precision and 4.0281% FPrate. The results obtained from the research will be helpful in term labor assessment and understanding the parturition process.  

A novel variable filter band discrete wavelet transform: Application

2014 ◽  
Vol 12 (04) ◽  
pp. 1460007
Author(s):  
Zhong Zhang ◽  
Jin Ohtaki ◽  
Hiroshi Toda ◽  
Takashi Imamura ◽  
Tetsuo Miyake
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Wavelet Shrinkage ◽  
Ecg Signals ◽  
Fetal Ecg ◽  
Band Filter ◽  
Best Basis Algorithm ◽  
Better Than

In this study, in order to verify the effectiveness of the variable filter band discrete wavelet transform (VFB-DWT) and construction method of the variable-band filter (VBF), a fetal ECG extraction has been carried out and the main results obtained are as follows. The approach to configuration VBF by selecting the frequency band only where the fetal ECG component is present was effective to configure the optimal base sensible signal. The extraction of the fetal ECG was successful by applying the wavelet shrinkage to VFB-DWT, which used the constructed VBF. The information entropy was selected as an evaluation index, and two kinds of ECG signals are used to evaluate the wavelet transform basis between the wavelet packet transform (WPT) and the VFB-DWT. One is a synthesized signal composed of white noise, the maternal ECG and the fetal ECG. The other signal is the real target signal separated by independent component analysis (ICA) and has the mother's body noise, the maternal ECG and the fetal ECG. The result shows that the basis by VBF of the VFB-DWT is better than the basis of the WPT that was chosen by the best basis algorithm (BBA).

PERFECT-TRANSLATION-INVARIANT CUSTOMIZABLE COMPLEX DISCRETE WAVELET TRANSFORM

2013 ◽  
Vol 11 (04) ◽  
pp. 1360003 ◽  
Author(s):  
HIROSHI TODA ◽  
ZHONG ZHANG ◽  
TAKASHI IMAMURA
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Frequency Domain ◽  
Wavelet Transforms ◽  
Wavelet Packet ◽  
Variable Density ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Translation Invariant ◽  
High Degree

The theorems, giving the condition of perfect translation invariance for discrete wavelet transforms, have already been proven. Based on these theorems, the dual-tree complex discrete wavelet transform, the 2-dimensional discrete wavelet transform, the complex wavelet packet transform, the variable-density complex discrete wavelet transform and the real-valued discrete wavelet transform, having perfect translation invariance, were proposed. However, their customizability of wavelets in the frequency domain is limited. In this paper, also based on these theorems, a new type of complex discrete wavelet transform is proposed, which achieves perfect translation invariance with high degree of customizability of wavelets in the frequency domain.

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