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

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


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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
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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  2/2017 - 14

An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal Separation

AZAMIAN, M. See more information about AZAMIAN, M. on SCOPUS See more information about AZAMIAN, M. on IEEExplore See more information about AZAMIAN, M. on Web of Science, KABIR, E. See more information about  KABIR, E. on SCOPUS See more information about  KABIR, E. on SCOPUS See more information about KABIR, E. on Web of Science, SEYEDIN, S. See more information about  SEYEDIN, S. on SCOPUS See more information about  SEYEDIN, S. on SCOPUS See more information about SEYEDIN, S. on Web of Science, MASEHIAN, E. See more information about MASEHIAN, E. on SCOPUS See more information about MASEHIAN, E. on SCOPUS See more information about MASEHIAN, E. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,750 KB) | Citation | Downloads: 102 | Views: 214

Author keywords
adaptive algorithms, feature extraction, gaussian noise, hyperspectral imaging, image classification

References keywords
signal(18), separation(16), processing(16), audio(12), sparse(11), representation(10), music(10), source(9), speech(8), dictionaries(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 103 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02014
Web of Science Accession Number: 000405378100014
SCOPUS ID: 85020131730

Abstract
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In this paper, a sparse method is proposed to synthesize the note-specific atoms for musical notes of different instruments, and is applied to separate the sounds of two instruments coexisting in a monaural mixture. The main idea is to explore the inherent time structures of the musical notes by a novel adaptive method. These structures are used to synthesize some time-domain functions called note-specific atoms. The note-specific atoms of different instruments are integrated in a global dictionary. In this dictionary, there is only one note-specific atom for each note of any instrument, resulting in a sparse space for each instrument. The signal separation is done by mapping the mixture signal to the global dictionary. The signal related to each instrument is estimated by a summation of the mapped note-specific atoms tagged for that instrument. Experimental results demonstrate that the proposed method improves the quality of signal separation compared to a recently proposed method.


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References Weight

Web of Science® Citations for all references: 10,101 TCR
SCOPUS® Citations for all references: 13,793 TCR

Web of Science® Average Citations per reference: 281 ACR
SCOPUS® Average Citations per reference: 383 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 2017-12-14 06:08 in 179 seconds.




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