iosr-JVSP   Volume-2 ~ Issue-5

ABSTRACT: Speaker verification is the method of automatically identifying who is speaking on the basis of individual information integrated in speech waves. An important application of speaker verification is for forensic purposes. Speaker verification has seen an appealing research field for the last decades which still yields a number of unsolved problems. Many algorithms have been developed to accomplish, some of which include Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), Artificial Neural Network. All the before mentioned algorithms serve the feature matching mechanism while the MFCC (Mel Frequency Cepstral Coefficients) are the features extracted of a voice signal. The Mel scale is mainly based on the study of observing the pitch or frequency perceived by the human.The simplest of the algorithms is calculating the distortion distance between the various codebooks of the speakers, but its efficiency is less compared to other algorithms. Here, we have tried to increase the efficiency of this method. The two phases of this system is the training phase and the testing phase. The training phase involves the feature extraction using MFCC and storing the codebooks in the database. The testing phase involves all these plus the distortion distance calculation using the codebook of the unknown speaker against all the speakers whose codebook is already stored in the database and is verified if the speaker matches with the claimed identity.

Keywords: Distortion Distance, Feature Extraction, Feature Matching, MFCC, Mel scale, Speaker Recognition, Training Phase, Testing Phase, Vector Quantisation

[1]. Journal paper: Sujit Kumar Behera, Jatindra Kumar Singh, Speaker Verfication using Mel frequency cepstral coefficient and artificial neural network
[2]. Journal paper: Mohd Zaizu Ilyas, Salina Abdul Samad, Aini Hussain, Khairul Anuar Ishak, Speaker Verification using Vector Quantization and Hidden Markov Model.
[3]. Thesis submitted on Kernel Based Learning Methods for Pattern and Feature Analysis by WU Zhili, Hong Kong University.
[4]. The physiology of speech production
[5]. Paper by Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbai Md. Saifur Rahman on Speaker Identification Using Mel Frequency Cepstral Coefficients.
[6]. Review of different techniques for speaker recognition system by Bansod.N.S., Seema Kawathekar and Dabhade S.B.
[7]. ICME 2004 Tutorial on Audio Feature Extraction by George Tzanetakis, University of Victoria, Canada.
[8]. About the voice: http://www.lionsvoiceclinic.umn.edu/page2.htm (last viewed May, 2013).

ABSTRACT: A complex number can be interpreted as a vector in imaginary plane. The vector rotation in the x/y plane can be realized by rotating a vector through a series of elementary angles. These elementary angles are chosen such that the vector rotation through each of them may be approximated easily with a simple shift and add operation, and their algebraic sum approaches the required rotation angle. This can be exercised by CORDIC ('CO-ordinate Rotation Digital Computer) algorithm in rotation mode. In this paper, we have proposed a pipelined architecture new memory less z-path eliminated CORDIC algorithm for FFT computation. Pipelined architecture by pre computation of direction of micro rotation, radix-4 number representation, and the angle generator has been processed in terms of hardware complexity, iteration delay and memory reduction. Comparison of the proposed architecture with the conventional radix-4 architectures is elaborated. The proposed algorithm also exercises an addressing scheme and the associated angle generator logic in order to eliminate the ROM usage for bottling the twiddle factors. It incorporates parallelism and pipe line processing. The latency of the system is n/2 clock cycles. The throughput rate is one valid result per eight clock cycles. Additionally VLSI implementation on Virtex -4 FPGA is done. The implemented design operates at 450.654 MHZ of clock rate with a power consumption of 175.90mW.

Keywords-CORDIC, , FPGA, latency, Radix-4 , memory less systems, speed, throughput.

[1] Sathish Sharma , Implementation of ParaCORDIC Algorithm and it's Applications in Satellite Communication ,2009 . International conference in Advances in Recent in Technologies in communication & computing
[2] Meng Qian, Applications of CORDIC Algorithm to Neural Networks in VLSI Design ,IMACS 2006,Beijing, China
[3] Javier Valls, The use of CORDIC in Software Defined Radios: A Tutorial, IEEE Communications Magazine, Sep ‗2006
[4] Ayan Benerjee, Swapna Banerjee, ‗FPGA realization of a CORDIC based FFT processor for biomedical signal processing, Microprocessors and Microsystems, Feb 2011.
[5] .Bu-chin wang Digital signal processing Techniques and Applications in Radar Image processing.
[6] Bum Sikkim , Low power pipelined FFT architecture for Synthetic Aperture Radar, IEEE 39th Midwest Symposium, Circuits & Systems 1996.
[7] Sadat A , FFT for high speed OFDM wireless multimedia system, IEEE Circuits & Systems 2001 , MWSAS 2001
[8] Volder, J. (1959). The CORDIC trigonometric computing technique., IEEE Transactions on Electronic Computers
[9] B. Lakshmi , A.S. Dhar VLSI architecture for low latency radix-4 CORDIC, Elseiver Computers and Electrical Engineering, July 2011
[10] francisco j. jaime,enhanced scaling-free cordic, ieee transactions on circuits and systems—i: regular papers, vol. 57, no. 7, july 2010

ABSTRACT: The design of introduces a multi-mode transmulti- plexer (TMUX) structure capable of generating a great set of user-bandwidths and center frequencies. The structure utilizes fixed integer sampling rate conversion (SRC) blocks, Farrow- based variable interpolation and decimation structures, and variable frequency shifters. A main advantage of this TMUX is that it needs only one filter design beforehand. Specifically, the filters in the fixed integer SRC blocks as well as the subfilters of the Farrow structure are designed only once. Then, all possible combinations of bandwidths and center frequencies are obtained by properly adjusting the variable delay parameter of the Farrow-based filters and the variable parameters of the frequency shifters. The paper includes examples for demonstration. It also shows that, using the rational SRC equivalent of the Farrow- based filters, the TMUX can be described in terms of conventional multirate building blocks which may be useful in further analysis of the overall system.

Index Terms—Multi-mode communications, transmultiplexers, sampling rate conversion.

[1] Amir Eghbali, H°akan Johansson, Senior Member, IEEE, and Per L¨owenborg, Member, IEEE "A Multi-Mode Transmultiplexer Structure," IEEE Transactions on circuits and systems 02 volume 02 2008.

[2] A. Eghbali, H. Johansson, and P. Lo¨ wenborg, "An arbitrary bandwidth transmultiplexer and its application to flexible frequency-band realloca- tion networks," in Proc. European Conf. Circuit Theory Design, Seville, Spain, Aug. 2007.

[3] A. N. Akansu, P. Duhamel, L. Xueming, and M. de Courville, "Orthog- onal transmultiplexers in communication: a review," IEEE Trans. Signal Processing, vol. 46, no. 4, pp. 979–995, Apr. 1998.

[4] B. Arbesser-Rastburg, R. Bellini, F. Coromina, R. D. Gaudenzi, O. del Rio, M. Hollreiser, R. Rinaldo, P. Rinous, and A. Roederer, "R&D directions for next generation broadband multimedia systems: an ESA perspective," in Proc. 20th AIAA Int. Commun. Satellite Syst. Conf.Exhibit, Montreal, Canada, May 2002.

[5] H. Johansson and P. Lo¨ wenborg, "Flexible frequency-band reallocation drawn as shown in Fig. 6 and is similar (with some differences networks using variable oversampled complex-modulated filter banks," EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 63714, 15 pages, 2007.

[6] P. P. Vaidyanathan, Multirate Systems and Filter Banks.Englewood Cliffs, NJ: Prentice-Hall, 1993.

[7] H. Johansson and P. Lo¨ wenborg, "On the design of adjustable fractional delay FIR filters," IEEE Trans. Circuits Syst. II, vol. 50, no. 4, pp. 164–169, Apr. 2003.

[8] H. Johansson and O. Gustafsson, "Linear-phase FIR interpolation, decimation, and M -th band filters utilizing the Farrow structure," IEEE Trans. Circuits Syst. I, vol. 52, no. 10, pp. 2197–2207, Oct. 2005.

ABSTRACT: The compression of medical images is essential for reducing the cost of data storage and transmission time which in turn helps in better utilization of Bandwidth. The demand for images, video sequences and computer animation has increased drastically over the years which have also resulted in image and video compression. Image compression is broadly classified into lossy and lossless compression. Fractal image compression (FIC) is a lossy compression method. In fractal image compression an image is coded as a set of contractive transformations in a complete metric space. The set of contractive transformations is guaranteed to produce an approximation to the original image. In this paper quad-tree FIC is implemented on different Imaging modalities like Medical Resonance (MR) Image of Brain, Computerized tomography(CT) of Bone.The quality factors like Mean Square Error (MSE) , Peak Signal–to–Noise-Ratio (PSNR) Compression ratio(CR), Encoding time and decoding time for different imaging modalities with different threshold values are analyzed in this paper. From the matlab simulated results it is observed that Quad-tree FIC works better on medical image as it provides better PSNR, CR values over the other images. This paper also includes a comparison between standard FIC and Quad-tree FIC on MR image of Brain and study of the parameters reveals that Quad-tree FIC works better than Standard FIC. Keywords— Medical Imaging Fractal image compression, Quad-tree partitioning, objective quality measures

[1] M. Barnsley, Fractals Everywhere. New York: Academic,(1988).
[2] A.E. Jacquin, "Image coding based on a fractal theory of iterated contractive image transformation", IEEE Trans. On Image Processing, 1(1): (1992
[3] Y. Fisher, Fractal Image Compression: Theory and Application. New York: Springer-Verlag, (1994).
[4] A.E Jacquin, "Fractal image coding: A review", Proceeding of tile IEEE, 81(10): (1993)
[5] M.S.Soyjaudah and I.Jahmeerbacus "Fractal image compression using quad-tree partitioning" International Journal of Electrical Engineering Education 39/1
[6] Dr. Fakhiraldeen H. Ali Quad-tree Fractal Image Compression University of Mosul
[7] Sumathi Poobaland G. Ravindran, "Arriving at an OptimumValue of Tolerance Factor for Compressing Medical Images," world Academy of Science,Engineering and Technology, vol. 24, pp. 169-173, 2006.
[8] Pamela Cosman, Gray R.M. and Olshen A.(1994b)"Evaluating Quality of Compressed Medical Images: SNR, Subjective Rating and Diagnostic Accuracy‟, Proc. of the IEEE, Vol. 82, pp. 920-931.
[9] S. Bhavani et. al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010, 1429-1434 A Survey On Coding Algorithms In Medical Image compression