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Stefan cel Mare
University of Suceava
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ROMANIA

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


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  4/2011 - 1
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 HIGHLY CITED PAPER 

Line Spectral Frequency-based Noise Suppression for Speech-Centric Interface of Smart Devices

JANG, G. J. See more information about JANG, G. J. on SCOPUS See more information about JANG, G. J. on IEEExplore 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, SEO, Y. H. See more information about SEO, Y. H. on SCOPUS See more information about SEO, Y. H. on SCOPUS See more information about SEO, Y. H. on Web of Science
 
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Download PDF pdficon (708 KB) | Citation | Downloads: 1,877 | Views: 4,300

Author keywords
noise measurement, noise reduction, speech enhancement, speech recognition, linear predictive coding

References keywords
speech(15), spectral(6), processing(5), signal(4), recognition(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04001
Web of Science Accession Number: 000297764500001
SCOPUS ID: 84863083144

Abstract
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This paper proposes a noise suppression technique for speech-centric interface of various smart devices. The proposed method estimates noise spectral magnitudes from line spectral frequencies (LSFs), using the observation that adjacent LSFs correspond to peak frequencies of spectrum, whereas isolated LSFs are close to flattened valley frequencies retaining noise components. Over a course of segmented time frames, the logarithms of spectral magnitudes at respective LSFs are computed, and their distribution is then modeled by the Rayleigh probability density function. The standard deviation from the Rayleigh function approximates the noise spectral magnitude. The model is updated at every frame in an online manner so that it can deal with real-time inputs. Once the noise spectral magnitude is estimated, a time-domain Wiener filter is derived for the suppression of the estimated noise spectral magnitude, and this is then applied to the input noisy speech signals. Our proposed approach operates well on most smart devices owing to its low computational complexity and real-time implementation. Speech recognition experiments, conducted to evaluate the proposed technique, show that our method exhibits superior performance, with less distortion of original speech, when compared to conventional noise suppression techniques.


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

[1] M. Schuricht, Z. Davis, M. Hu, S. Prasad, P. Melliar-Smith, and L. Moser, "Managing multiple speech-enabled applications in a mobile handheld device," International Journal of Pervasive Computing and Communications, vol. 5, no. 3, pp. 332-359, Sep. 2009.
[CrossRef] [SCOPUS Times Cited 3]


[2] L. Deng, A. Acero, Y. Wang, K. Wang, H. Hon, et al., "A speech-centric perspective for human-computer interface," IEEE Workshop on Multimedia Signal Processing, pp. 263-267, Dec. 2002.
[CrossRef] [SCOPUS Times Cited 3]


[3] K. Kim and M. Kim, "Robust speaker recognition against background noise in an enhanced multi-condition domain," IEEE Transactions on Consumer Electronics, vol. 56, no. 3, pp. 1684-1688, Aug. 2010.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 12]


[4] S. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, no. 2, pp. 113-120, Apr. 1979.
[CrossRef] [Web of Science Times Cited 2581] [SCOPUS Times Cited 3513]


[5] K. Wu and P. Chen, "Efficient speech enhancement using spectral subtraction for car hands-free applications," Proc. of International Conference on Consumer Electronics, pp. 220-221, Jun. 2001.
[CrossRef] [Web of Science Times Cited 3]


[6] V. Stahl, A. Fischer, and R. Bippus, "Quantile based noise estimation for spectral subtraction and wiener filtering," Proc. of ICASSP, vol. 3, pp. 1875-1878, Jun. 2000.
[CrossRef] [SCOPUS Times Cited 147]


[7] A. Kindoz and A. Kondoz, Digital speech; coding for low bit rate communication systems, John Wiley & Sons, Inc., New York, NY, USA, Jan. 1994.

[8] P. Kabal and R. Ramachandran, "The computation of line spectral frequencies using chebyshev polynomials," IEEE Transactions on Acoustics, Speech, Signal Processing, vol. 34, no. 6, pp. 1419-1426, Dec. 1986.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 130]


[9] M. Lee, H. Kim, S. Choi, and H. Lee, "On the use of LSF intermodel interlacing property for spectral quantization," Proc. of IEEE Workshop on Speech Coding, pp. 43-45, Jun. 1999.
[CrossRef] [SCOPUS Times Cited 2]


[10] M. Lee, H. Kim, and H. Lee, "A new distortion measure for spectral quantization based on the LSF intermodel interlacing property," Speech Communication, vol. 35, no. 3-4, pp. 191-202, Oct. 2001.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 7]


[11] T. Backstrom and C. Magi, "Properties of line spectrum pair polynomials - a review," Signal Processing, vol. 86, pp. 3286-3298, Nov. 2006.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 35]


[12] Telecommunications Industry Association (TIA), "Enhanced variable rate codec, speech service option 3 for wideband spread spectrum digital systems," Technical Report, TIA/EIA/IS-127-2, Dec. 1999.

[13] European Telecommunications Standards Institute, "Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithm," Technical Report, ES 202 050 v1.1.5, Jan. 2007.

[14] M. Cooke, J. Hershey, and S. Rennie, "Monaural speech separation and recognition challenge," Computer Speech & Language, vol. 24, no. 1, pp. 1-15, 2010.
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 151]


[15] S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X. Liu, et al., Hidden Markov model toolkit (HTK), ver. 3.4, Dec. 2006. [Online] Available: Temporary on-line reference link removed - see the PDF document

[16] D. Pearce and H. Hirsch, "The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy condition," Proc. of ICSLP, Oct. 2000. [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 2,852 TCR
SCOPUS® Citations for all references: 4,003 TCR

Web of Science® Average Citations per reference: 178 ACR
SCOPUS® Average Citations per reference: 250 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 2022-06-29 00:51 in 71 seconds.




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