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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
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Next issue: Feb 2017
Avg review time: 97 days


PUBLISHER

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: 644266260
doi: 10.4316/AECE


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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  2/2012 - 3

Speech Segregation based on Pitch Track Correction and Music-Speech Classification

KIM, H.-G. See more information about KIM, H.-G. on SCOPUS See more information about KIM, H.-G. on IEEExplore See more information about KIM, H.-G. on Web of Science, JANG, G.-J. See more information about  JANG, G.-J. on SCOPUS See more information about  JANG, G.-J. on SCOPUS See more information about JANG, G.-J. on Web of Science, PARK, J.-S. See more information about  PARK, J.-S. on SCOPUS See more information about  PARK, J.-S. on SCOPUS See more information about PARK, J.-S. on Web of Science, KIM, J.-H. See more information about  KIM, J.-H. on SCOPUS See more information about  KIM, J.-H. on SCOPUS See more information about KIM, J.-H. on Web of Science, OH, Y.-H. See more information about OH, Y.-H. on SCOPUS See more information about OH, Y.-H. on SCOPUS See more information about OH, Y.-H. on Web of Science
 
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Download PDF pdficon (872 KB) | Citation | Downloads: 496 | Views: 1,892

Author keywords
source separation, speech processing, speech analysis, signal denoising, noise cancellation

References keywords
processing(7), neural(6), signal(5), separation(5), music(5), auditory(5), speech(4), negative(4), factorization(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): 15 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.02003
Web of Science Accession Number: 000305608000003
SCOPUS ID: 84865301789

Abstract
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Full text preview
A novel approach for pitch track correction and music-speech classification is proposed in order to improve the performance of the speech segregation system. The proposed pitch track correction method adjusts unreliable pitch estimates from adjacent reliable pitch streaks, in contrast to the previous approach using a single pitch streak which is the longest among the reliable pitch streaks in a sentence. The proposed music and speech classification method finds continuous pitch streaks of the mixture, and labels each streak as music-dominant or speech-dominant based on the observation that music pitch seldom changes in a short-time period whereas speech pitch fluctuates a lot. The speech segregation results for mixtures of speech and various competing sound sources demonstrated that the proposed methods are superior to the conventional method, especially for mixtures of speech and music signals.


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

[1] A. S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound. MIT Press, Cambridge MA, 1990.

[2] P. Smaragdis and J. C. Brown, "Non-negative matrix factorization for polyphonic music transcription," IEEE workshop on applications of signal processing to audio and acoustics, pp. 177-180, 2003
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 359]


[3] S. Choi, A. Cichocki, H.-M. Park, and S.-Y. Lee, "Blind source separation and independent component analysis: A review," Neural Information Processing - Letters and Reviews, vol. 6, 1 2005

[4] B. Raj, T. Virtanen, S. Chaudhuri, and R. Singh, "Non-negative matrix factorization based compensation of music for automatic speech recognition," in Proc. INTERSPEECH, pp. 717-720, 2010

[5] A. Nehorai and B. Porat, "Adaptive comb filtering for harmonic signal enhancement," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 5, pp. 1124-1138, 1986
[CrossRef] [SCOPUS Times Cited 167]


[6] S. T. Roweis, "One microphone source separation," Advances in Neural Information Processing Systems, vol. 13, pp. 793-799, 2001

[7] G.-J. Jang, T.-W. Lee, and Y.-H. Oh, "Single channel signal separation using time-domain basis functions," IEEE Signal Processing Letters, vol. 10, pp. 168-171, 6 2003
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 51]


[8] G. Hu and D. Wang, "Monaural speech segregation based on pitch tracking and amplitude modulation," IEEE Transactions on Neural Networks, vol. 15, no. 5, pp. 1135-1150, 2004
[CrossRef] [PubMed] [Web of Science Times Cited 184] [SCOPUS Times Cited 240]


[9] D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," Advances in Neural Information Processing Systems, vol. 13, pp. 556-562, 2001

[10] T. Virtanen, A. Mesaros, and M. Ryynänen, "Combining pitch-based inference and non-negative spectrogram factorization in separating vocals from polyphonic music," in Proc. ITRW on Statistical and Perceptual Audio Processing, pp. 17-22, 2008

[11] R. D. Patterson, I. Nimmo-Smith, J. Holdsworth, and P. Rice, "An efficient auditory filterbank based on the gammatone function," tech. rep., Annex B of the SVos Final Report: The auditory filterbank, APU Report 2341, 1988

[12] M. Weintraub, "A theory and computational model of auditory monaural sounds separation," ph.d. thesis, Stanford University, 1985

[13] M. P. Ryynänen and A. P. Klapuri, "Automatic transcription of melody, bass line, and chords in polyphonic music," Computer Music Journal, vol. 32, no. 3, pp. 72-86, 2008
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 78]


[14] D. L. Wang and G. J. Brown, "Separation of speech from interfering sounds based on oscillatory correlation," IEEE transactions on neural networks, vol. 10, no. 3, pp. 684-697, 1999
[CrossRef] [PubMed] [SCOPUS Times Cited 187]


[15] Ray Meddis, "Simulation of auditory-neural transduction: further studies," Acoustical Society of America, vol. 83, pp. 1056-1063, 1988



References Weight

Web of Science® Citations for all references: 263 TCR
SCOPUS® Citations for all references: 1,082 TCR

Web of Science® Average Citations per reference: 16 ACR
SCOPUS® Average Citations per reference: 68 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 2016-12-01 01:31 in 35 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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