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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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  2/2012 - 10

The Effects of the Acute Hypoxia to the Fundamental Frequency of the Speech Signal

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, MILIVOJEVIC, M. See more information about  MILIVOJEVIC, M. on SCOPUS See more information about  MILIVOJEVIC, M. on SCOPUS See more information about MILIVOJEVIC, M. on Web of Science, BRODIC, D. See more information about BRODIC, D. on SCOPUS See more information about BRODIC, D. on SCOPUS See more information about BRODIC, D. on Web of Science
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Download PDF pdficon (629 KB) | Citation | Downloads: 450 | Views: 2,942

Author keywords
speech analysis, speech processing, fundamental frequency, dissonant frequency, acute hypoxia

References keywords
speech(10), signal(6), frequency(5), milivojevic(4), hypoxia(4), fundamental(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-05-30
Volume 12, Issue 2, Year 2012, On page(s): 57 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.02010
Web of Science Accession Number: 000305608000010
SCOPUS ID: 84865296358

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When people that live at the small altitudes (up to 400 m above the sea level) climb on the mountain, they are exposed to the effects of an acute hypoxia. As a consequence, theirs oxygen concentration decreases in the tissue. This paper presents the analysis of the acute hypoxia effects to the speech signal at the altitudes up to 2600 m above the sea level. For the experiment, the articulation of vowels (A, E, I, O, U) from the test group of persons was recorded at different altitudes, which creates the speech signal database. The speech signal from database is processed by the original algorithm. As the results, the fundamental frequency and the energy of dissonant intervals of speech signal are obtained. Furthermore, the acute hypoxia effect to the energy distribution in the dissonant intervals of the speech signal is analyzed. At the end, the comparative analysis of the acute hypoxia effects shows that the level of the hypoxia can be determined by the change of the fundamental frequency and the energy of the dissonant intervals of speech signal. Hence, it is possible to bring conclusions about the degree of hypoxia, which in many situations can be of importance for avoiding catastrophic consequences.

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

[1] V. Liere, J. Edward, J. Clifford, Hypoxia. The University of Chicago and London, 1963.

[2] Richalet J. P., R. Kacimi, A. M. Antezana, "The control of chronotropic function in hypobaric hypoxia," Int. J. Sports Med. 13:S22-S24, 1992
[CrossRef] [PubMed] [Web of Science Times Cited 32]

[3] J. Obrenovic, "Fundamental frequency of the voice in relation to hypoxia as a stressor," Facta Universitatis, Series: Philosophy, Sociology and Psyhology, Vol. 2, No. 9, pp. 683-689, 2002.

[4] J. Obrenovic, M. Nesic, Cogntive values of speech signal under the conditions of acute hypoxia, Psihologiya, University of Nis, Vol. 2, No. 2, pp. 127-137, 2003.

[5] B. Yang , M. Lugger, "Emotion recognition from speech signals using new harmony features", Signal Processing, Vol. 90, pp. 1415-1423, 2010
[CrossRef] [Web of Science Times Cited 55]

[6] I. Murray, C. Baber, A. South, "Towards a definition and working model of stress and its effects on speech", Speech Communication, Vol. 20, pp. 3-12, 1996
[CrossRef] [Web of Science Times Cited 37]

[7] R. Ruiz, E. Absil, B. Harmegnies, C. Legros, D. Poch, "Time and spectrum related variabilities in stressed speech under laboratory and real conditions", Speech Communication, Vol. 20, pp. 111-129, 1996
[CrossRef] [Web of Science Times Cited 36]

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

[9] Z. Milivojevic, D. Balaneskovic, "Enhancement Of The Perceptive Quality Of The Noisy Speech Signal By Using Of DFF-FBC Algorithm", Facta Universitatis, Ser.: Elec. Energ. Vol. 22, No. 3, pp. 379-392, December 2009.

[10] H. S. Pang, S. J. Baek, Sung K. M., "Improved Fundamental Frequency Estimation Using Parametric Cubic Convolution", IEICE Trans. Fundamentals, E83-A, pp. 2747-50, 2000.

[11] Z. Milivojevic, M. Mirkovic, S. Milivojevic, "Fundamental Frequency Estimation of the Speech Signal Compressed by MP3 Algorithm Using PCC Interpolation", Advances in Electrical and Computer Engineering AECE, Vol. 10, No. 1, pp. 18-22, 2010
[CrossRef] [Full Text] [Web of Science Times Cited 3]

[12] Z. Milivojevic, M. Mirkovic, "Estimation of the fundamental frequency of the speech signal modeled by the SYMPES method", AEU - International Journal of Electronics and Communications, Vol. 63, pp. 200-208, 2009
[CrossRef] [Web of Science Times Cited 5]

References Weight

Web of Science® Citations for all references: 174 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 0

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 2018-12-17 22:52 in 54 seconds.

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