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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  3/2011 - 10

ADPCM Using a Second-order Switched Predictor and Adaptive Quantizer

DESPOTOVIC, V. See more information about DESPOTOVIC, V. on SCOPUS See more information about DESPOTOVIC, V. on IEEExplore See more information about DESPOTOVIC, V. on Web of Science, PERIC, Z. See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on Web of Science
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Download PDF pdficon (1,829 KB) | Citation | Downloads: 768 | Views: 2,881

Author keywords
adaptive coding, correlation, predictive coding, speech processing, signal to noise ratio

References keywords
speech(11), coding(5), packet(4), loss(4), algorithm(4), adaptive(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-08-31
Volume 11, Issue 3, Year 2011, On page(s): 61 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.03010
Web of Science Accession Number: 000296186700010
SCOPUS ID: 80055075779

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Adaptive differential pulse code modulation (ADPCM) with forward gain-adaptive quantizer and second-order switched predictor based on correlation is presented in this article. Predictor consists of a bank of predetermined predictors for each block of speech samples, avoiding the need to solve, or quantize predictor coefficients during the coding process. The adaptation consists of switching to one of this predictors based on the values of the first and second order correlation coefficients. The theoretical model is generalization of the DPCM with the first order switched predictor for an arbitrary prediction order. Experimental results for ADPCM with the second-order four/eight state switched prediction based on correlation are provided.

References | Cited By  «-- Click to see who has cited this paper

[1] J. H. Chen, A. Gersho, "Gain adaptive vector quantization with application to speech coding", IEEE Trans. on. Comm., vol. COM-35, no. 9, pp. 918-930, 1987.
[CrossRef] [SCOPUS Times Cited 33]

[2] A. Gersho, R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Press/Springer, 1992.

[3] V. M. Despotovic, Z. H. Peric, L. Velimirovic, V. D. Delic, "DPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals," Advances in Electrical and Computer Engineering, vol. 10, no. 4, pp. 95-98, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]

[4] C. C. Evci, R. Steele, C.S. Xydeas, "DPCM-AQF using second-order adaptive predictors for speech signals". IEEE Transactions on Audio, Speech and Signal Processing, vol. 29, no.3, pp. 337-341, 1981.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 2]

[5] N. S. Jayant, P. Noll, Digital Coding of Waveforms, Prentice-Hall, New Jersey, Chapter 4, pp. 129-139, 1984.

[6] S. Dowdy, S. Wearden, Statistics for Research, John Wiley and Sons, New York, 1983.

[7] W. C. Chu, Speech Coding Algorithms, John Wiley & Sons, New Jersey, Chapters 5-6, pp. 143-183, 2006.

[8] C. C. Cho, N. I. Park, H. K. Kim, "A Packet Loss Concealment Algorithm Robust to Burst Packet Loss for CELP-type Speech Coders", Proc. of ITC-CSCC 2008, pp. 941-944, 2008. [Local Repository]

[9] J. R. Deller, J. H. L. Hansen, J. G. Proakis, Discrete-Time Processing of Speech Signals, IEEE Press, 2000.

[10] J. Nikolic, Z. Peric, "Lloyd-Max’s Algorithm Implementation in Speech Coding Algorithm Based on Forward Adaptive Technique", Informatica, vol. 19, (2), pp. 255-270, 2008.

[11] A. Kondoz, Digital Speech, Coding for Low Bit Rate Communication Systems, JohnWiley & Sons, New Jersey, 2004.

[12] O. Hersent, J. Petit, D. Gurle, Beyond VoIP Protocols- Understanding Voice Technology and Networking Techniques for IP Telephony, John Wiley & Sons, New Jersey, Chapters 1-2, pp. 1-88, 2005.
[CrossRef] [SCOPUS Times Cited 24]

[13] D. Minoli, Voice over MPLS - Planning and Designing Networks, McGraw-Hill, Chapters 1-2, pp. 1-134, 2002.

[14] R. Goldberg, L. Riek, A Practical Handbook of Speech Coders, CRC Press, 1. edition, 2000.

[15] ITU-T Recommendation G.712: Transmission Performance Characteristics of Pulse Code Modulation (PCM), 1992.

References Weight

Web of Science® Citations for all references: 11 TCR
SCOPUS® Citations for all references: 68 TCR

Web of Science® Average Citations per reference: 1 ACR
SCOPUS® Average Citations per reference: 5 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-07-17 06:10 in 39 seconds.

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