<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

Speaker recognition (identification/verification) is the computing task of validating a user’s claimed identity using speaker specific information included in speech waves: that is, it enables access control of various services by voice. Discrete Wavelet Transform (DWT) based systems for speaker recognition have shown robust results for several years and are widely used in speaker recognition applications.   This paper is based on text independent speaker recognition system that makes use of Discrete Wavelet Transform (DWT) as a feature extraction and kernel Support Vector Machine (SVM) approach as a classification tool for taking the decision through applying simplified-Class Support Vector Machine approach.   The proposed SVM approach can convert local Euclidean distances between frame vectors to angles by projecting these -dimensional vectors together, and get the minimum global distance from the non-linear aligned speech path in order to address audio classification, and hence, sound recognition.The DWT for each frame of the spoken word are taken as a tool for extracting the main feature as a data code vectors, next these data is normalized utilizing the normalized power algorithm that is used to reduce the number of feature vector coefficients then these data is scaled and tested with those stored of the training spoken words to achieve the speaker identification tasks, also the DWT gives fixed amount of data that can be utilized modesty by SVM.Finally, the proposed method is tested and trained upon a very large data base with results limited to ten speakers only (5 males and 5 females) with words of maximally 17 phenomena and its performance gives an accurate and stable results which rises the algorithm efficiency and reduce the execution time with 97% overall  accuracy.

Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5) to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle), and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform). The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform) algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R), achieving higher peak signal to noise ratio (PSNR), and the resulting images are of much smoother as compared to standard JPEG especially for high C.R.A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally, which approves the highest capability of Modified Haar between others