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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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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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  1/2010 - 3

Fundamental Frequency Estimation of the Speech Signal Compressed by MP3 Algorithm Using PCC Interpolation

MILIVOJEVIC, Z. N. See more information about MILIVOJEVIC, Z. N. on SCOPUS See more information about MILIVOJEVIC, Z. N. on IEEExplore See more information about MILIVOJEVIC, Z. N. on Web of Science, MIRKOVIC, M. D. See more information about  MIRKOVIC, M. D. on SCOPUS See more information about  MIRKOVIC, M. D. on SCOPUS See more information about MIRKOVIC, M. D. on Web of Science, MILIVOJEVIC, S. M. See more information about MILIVOJEVIC, S. M. on SCOPUS See more information about MILIVOJEVIC, S. M. on SCOPUS See more information about MILIVOJEVIC, S. M. on Web of Science
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Download PDF pdficon (417 KB) | Citation | Downloads: 937 | Views: 4,346

Author keywords
fundamental frequency, speech compression, speech processing, signal representation

References keywords
frequency(18), signal(9), estimation(9), fundamental(8), speech(7), processing(6), process(5), milivojevic(5), interpolation(5), cubic(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 18 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01003
Web of Science Accession Number: 000275458900003
SCOPUS ID: 77954638982

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In this paper the fundamental frequency estimation results of the MP3 modeled speech signal are analyzed. The estimation of the fundamental frequency was performed by the Picking-Peaks algorithm with the implemented Parametric Cubic Convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville and Greville two-parametric kernel. Depending on MSE, a window that gives optimal results was chosen.

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

[1] K. Brandenburg, G. Stoll, Y. F. Dehery, J. D. Johnston, L. V. Kerkhof, E. F. Schroeder, "The ISO/MPEG Audio Codec: A Generic Standard for Coding of High Quality Digital Audio", 92nd. AES-convention, preprint 3336, Vienna, 1992

[2] ISO/IEC 13818-3, Information Technology. Generic Coding of Moving Pictures and Associated Audio: Audio. International Standard, 1994

[3] S. Hacker, MP3: The Definitive Guide, O'Reilly & Associates, Sebastopol, CA 95472, 2000 [PermaLink]

[4] B. Joen, S. Kang, S. J. Baek, K. M. Sung, "Filtering of a Dissonant Frequency Based on Improved Fundamental Frequency Estimation for Speech Enhancement", IEICE Trans. Fundamentals, vol. E86-A, No. 8, pp. 2063-2064, August 2003

[5] S. Kang, "Dissonant frequency filtering technique for improving perceptual quality of noisy speech and husky voice", IEEE Signal Processing, vol. 84, pp. 431-433, 2004
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]

[6] S. Kang, Y. Kim, A Dissonant Frequency Filtering for Enhanced Clarity of Husky Voice Signals, Lect Notes Comp Science, Springer, vol. 4188, pp. 517-522. Berlin, 2006

[7] A. Cheveigne, H. Kawahara, "YIN, a fundamental frequency estimator for speech and music", J. Acoust. Soc. Am. vol. 111 (4), pp. 1917-1930, 2002
[CrossRef] [PubMed] [Web of Science Times Cited 625] [SCOPUS Times Cited 2]

[8] L. Qiu, H. Yang, S. N. Koh, "Fundamental frequency determination on instantaneous frequency estimation", IEEE Signal Processing, vol. 44, pp. 233-241, 1995
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]

[9] S. C. Sekhar, T. V. Sreenivas, "Effect of interpolation on PWVD computation and instantaneous frequency estimation", IEEE Signal Processing, vol. 84, pp. 107-116, 2004
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 15]

[10] P. Rao, A. Barman, "Speech formant frequency estimation: evaluating a nonstationary analysis method", IEEE Signal Processing, vol. 80, pp. 1655-1667, 2000
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]

[11] Z. M. Hussain, B. Boashash, "Adaptive instantaneous frequency estimation of multicomponent signals using quadratic time-frequency distributions", IEEE Trans. Signal Process. vol. 50 (8), pp. 1866-1876, 2002
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 101]

[12] A. Kacha, F. Grenez, K. Benmahammed, "Time-frequency analysis and instantaneous frequency estimation using two-sided linear prediction", IEEE Signal Process. vol. 85, pp. 491-503, 2005
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 17]

[13] R. G. Keys, "Cubic convolution interpolation for digital image processing", IEEE Trans. Acoust., Speech & Signal Process., Vol. 29, No. 6, pp. 1153-1160, Dec. 1981
[CrossRef] [Web of Science Times Cited 1261] [SCOPUS Times Cited 1629]

[14] K. S. Park, R. A. Schowengerdt, "Image reconstruction by parametric cubic convolution", Comput. Vision, Graphics & Image Process. vol. 23, pp. 258-272, 1983
[CrossRef] [Web of Science Times Cited 197] [SCOPUS Times Cited 246]

[15] E. Meijering, M. Unser, "A Note on Cubic Convolution Interpolation", IEEE Trans. on Image Process., vol. 12, No. 4, pp. 447-479, April 2003
[CrossRef] [PubMed] [Web of Science Times Cited 51] [SCOPUS Times Cited 69]

[16] H. S. Pang, S. J. Baek, K. M. Sung, "Improved Fundamental Frequency Estimation Using Parametric Cubic Convolution", IEICE Trans. Fund. Vol. E83-A, No. 12, pp. 2747-2750, Dec. 2000

[17] Z. Milivojevic, M. Mirkovic, P. Rajkovic, "Estimating of the fundamental frequency by the using of the parametric cubic convolution interpolation", Proceedings of International Scientific Conference UNITECH '04, Session: Electronics and Communication Engineering, pp. 138-141, Gabrovo, Bulgaria, 2004

[18] M. Mirkovic, Z. Milivojevic, P. Rajkovic, "Performances of the system with the implemented PCC algorithm for the fundamental frequency estimation", Proceedings of XII Telecommunications Forum TELFOR '04, Section. 7, Signal processing, Beograd, 2004

[19] Z. Milivojevic, M. Mirkovic, S. Milivojevic, "An Estimate of Fundamental Frequency Using PCC Interpolation - Comparative Analysis", Inf Technol Control, vol. 35, No. 2, pp. 131-136, 2006

[20] B. Yarman, U. Guz, H. Gurkan, "On the comparative results of SYMPES: A new method of speech modeling", Int J Electron Commun (AEU) 2006; 60:421-7
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]

[21] Z. Milivojevic, D. Mirkovic, "Estimation of the fundamental frequency of the speech signal modeled by the SYMPES method", Int J Electron Commun (AEU), vol 63, pp. 200-208, 2009
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 7]

References Weight

Web of Science® Citations for all references: 2,274 TCR
SCOPUS® Citations for all references: 2,142 TCR

Web of Science® Average Citations per reference: 108 ACR
SCOPUS® Average Citations per reference: 102 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 2019-01-13 15:45 in 96 seconds.

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Faculty of Electrical Engineering and Computer Science
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