Average framing linear prediction coding with wavelet transform for text-independent speaker identification system

Daqrouq, Khaled; Azzawi, Khalooq Y. Al

doi:10.1016/j.compeleceng.2012.04.014

Cited by 37 publications

(50 citation statements)

References 45 publications

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“…In the last decade, a tremendous increase in the applications of wavelets, in various scientific fields, has emerged [46]. The applications of wavelets include signal processing, image processing, security systems, biomedicine, statistics, etc.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Classical entropy-based criterion describes information-related properties for the precise representation of a given image. Entropy is commonly used in image processing; it possesses information about the concentration of the image [46]. On the other hand, the method for measuring entropy appears as a supreme tool for quantifying the ordering of non-stationary processes.…”

Section: Feature Extraction Proceduresmentioning

confidence: 99%

“…It regulates the receptive field of the kernel. The input vector x and the centers ∈ of the kernel have a dimensionality d. exp stands for the exponential function, and the transpose of the vector is indicated by the superscript T [46]. …”

Section: Classificationmentioning

confidence: 99%

“…Also, by taking the inner product between the original signal and the wavelet functions, the DWT coefficients can be produced. Given that wavelet functions are simply translated and dilated versions of each other, a simpler algorithm, known as Mallat's pyramid tree algorithm, has been proposed ( Figure 2) [45][46][47].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

See 3 more Smart Citations

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Daqrouq

Sweidan

Balamesh

et al. 2017

Entropy

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Abstract:Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted as a DWT entropy neural network system (DWENN). Our investigation was conducted over several wavelet families and different entropy types. Identification tasks, as well as verification tasks, were investigated for a comprehensive signature system study. Several other methods used in the literature were considered for comparison. Two databases were used for algorithm testing. The best recognition rate result was achieved by WPENN whereby the threshold entropy reached 92%.

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Section: Feature Extraction Proceduresmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Discrete Wavelet Transformmentioning

confidence: 99%

See 2 more Smart Citations

Off-Line Handwritten Signature Recognition by Wavelet Entropy and Neural Network

Daqrouq

Sweidan

Balamesh

et al. 2017

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accuracy of this system was about 90% with Arabic speech data. Wavelet Transform (WT) also used alongside with Linear Prediction Coding (LPC) [5]. At that research, some extraction feature method for speaker identification was compared with proposed method.…”

Section: Introductionmentioning

confidence: 99%