DISCRETE S-TRANSFORM BASED SPEECH ENHANCEMENT

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

0
Reader(s)
0
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 8 , ISSUE 4 (December 2015) > List of articles

DISCRETE S-TRANSFORM BASED SPEECH ENHANCEMENT

Guo-hua Hu / Rui Li / Liang Tao

Keywords : Discrete s-transform, time-frequency domain, speech enhancement.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 4, Pages 2,231-2,246, DOI: https://doi.org/10.21307/ijssis-2017-851

License : (CC BY-NC-ND 4.0)

Received Date : 30-August-2015 / Accepted: 15-November-2015 / Published Online: 01-December-2015

ARTICLE

ABSTRACT

S-transform (ST) is an effective time-frequency representation method with the advantages of short-time Fourier transform and wavelet transform. This paper utilizes the advantages of the power spectral subtraction method and the speech enhancement method to presents a novel speech enhancement algorithm based on discrete ST. Firstly, the speech in time domain is transformed by ST to joint time-frequency domain for spectral subtraction so that the clear speech spectrum can be obtained and then the inverse ST is performed to acquire the enhanced speech in time domain. Simulation experiment is done to verify the validity of the method. The experiment results show that the proposed method can effectively enhance the de-noising ability and improve the signal-to-noise ratio.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] J. Ying, G. Li, and Z. Wang. “A novel approach to speech signal synthesis”, In Proc.
International Conference on Audio, Language and Image Processing, 2008, pp. 680–685,
2008.
[2] C. Z. Wu, M. T. Guo, S. B. Xiong, and Q. Li. “Studies on a speech signal testing method
based on adaptive filtering feedback technology”, In Proc. International Conference on
Signal Processing, 2000, vol. 1, pp. 543–546, 2000.
[3] T. F. Quatieri. “Discrete-time speech signal processing: principles and practice”, Pearson
Education India, 2002.
[4] A. Kusumoto, T. Arai, K. Kinshita, and N. Hodoshima. “Modulation Enhancement of
speech by a preprocessing algorithm for improving intelligibility in reverberant
environments”, Speech Communication, vol. 45, no. 2, pp. 101–113, 2005.
[5] 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, 1979.
[6] M. Berouti, R. Schwartz, and J. Makhoul. “Enhancement of speech corrupted by acoustic
noise”, In Proc. IEEE Int. Conf. Acoust., Speech Signal Processing, 1979, pp. 208–211,
1979.
[7] Y. Ephriam, H. L. Van Trees. “A signal subspace approach for speech enhancement”, In
Proc. International Conference on Acoustic, Speech and Signal processing, 1993, Detroit,
MI, USA, vol. 2, pp. 355–358, 1993.
[8] F. Huang, T. Lee, W. B. Kleijn, et al., “A method of speech periodicity enhancement using
transform-domain signal decomposition”, Speech Communication, vol. 67, pp.102-112,
2015.
[9] MAA El-Fattah, MI Dessouky, AM Abbas, et al., “Speech enhancement with an adaptive
Wiener filter”, International Journal of Speech Technology, vol. 17, no.1, pp.53-64, 2014.
[10] C. Caraiscos, B. Liu. “A roundoff error analysis of the LMS adaptive algorithm”, IEEE
Transactions on Acoustics, Speech and Signal Processing, vol. 32, no. 1, pp. 34-41, 1984.
[11] V. Udayashankara. “Fast Recursive DCT-LMS Speech enhancement For Performance
Enhancement Of Digital Hearing Aid”, Academic Open Internet Journal, no.18, 2006.
[12] B. M. Soujanya, C. R. Rao, D. V. L. N. Sastry. “Speech Enhancement using Combinational
Adaptive LMS Algorithms”, International Journal of Advanced Computer Research, vol.
18, no. 5, 2015.
[13] J. Chen. “A study of speech enhancement based on RLS filter”, Computer Applications &
Software, (in Chinese), vol. 19, no. 10, pp. 40-42, 2002.
[14] Pogula Rakesh, T. Kishore Kumar. “A Novel RLS Based Adaptive Filtering Method for
Speech Enhancement”, World Academy of Science, Engineering and Technology,
International Journal of Electrical, Computer, Electronics and Communication Engineering,
vol. 9, no. 2, pp. 624-628, 2015.
[15] Zhang Qiu-yu,Liu Yang-wei,Huang Yi-bo,et al., “Perceptual Hashing Algorithm for
Speech Content Identification Based on Spectrum Entropy in Compressed Domain”,
International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 1, pp. 283–300,
2014.
[16] Y. Ghanbari, M. Karami. “Spectral subtraction in the wavelet domain for speech
enhancement”, International Journal of Software & Information Technology, no.1, pp. 26–
30, 2004.
[17] Y. Ghanbari, M. Karami, and B. Amelifard. “Improved multiband spectral subtraction
method for speech enhancement”, In Proc. 6th IASTED International Conf. on Signal and
Image Processing, USA, pp. 225–230, 2004.
[18] R. G. Stockwell, L. Mansinha, and R. P. Lowe. “Localization of the complex spectrum: the
S-transform”, IEEE Transactions on Signal Processing, vol. 44, no. 4, pp. 998–1001, 1996.
[19] I. Djurović, E. Sejdić and J. Jiang. “Frequency-based window width optimization for Stransform”,
AEU International Journal of Electronics and Communications, vol. 62, no. 4,
pp. 245–250, 2008.
[20] E. Sejdić, I. Djurović and J. Jiang. “A window width optimized S-transform”, EURASIP
Journal on Advances in Signal Processing, 2008:59, 2008.
[21] Wang Lin, Meng Xiaofeng, “An adaptive Generalized S-transform for instantaneous
frequency estimation”, Signal Processing, vol. 91, no. 8, pp.1876–1886, 2011.
[22] Zenton Goh, Kah-Chye Tan and B. T. G. Tan, “Postprocessing method for suppressing
musical noise generated by spectral subtraction”, IEEE Transactions on Speech and Audio
Processing, vol. 6, no. 3, pp.287-292, 1998.
[23] K. Paliwal, K. Wójcicki and B. Schwerin, “Single-channel speech enhancement using
spectral subtraction in the short-time modulation domain”, Speech Communication, vol. 52,
no. 5, pp. 450–475, 2010.
[24] Zhou Jian , Huang Cheng , Zhang Man , et al., “Whisper denoising in joint timefrequency
domain based on real valued discrete Gabor transform”, Applied Mechanics and
Materials,vol. 152, no. 8, pp.1091-1096, 2012.
[25] L. Debnath, F. A. Shah, “The Gabor Transform and Time–Frequency Signal Analysis”,
Wavelet Transforms and Their Applications, Birkhäuser Boston, pp.243-286, 2015.

EXTRA FILES

COMMENTS