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Journals / International Journal of Applied Mathematics Electronics and Computers / 2016 / Special Issue (2016)

Speech Denoising using Common Vector Analysis in Frequency Domain

Erol Seke
Mehmet Hakan Durak
Kemal Özkan
Pages
224–229
Publication Date
2016-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results are compared with that of the state of the art in well-known speech quality measures.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Signal denoising approaches on data of any dimension largely relies on the assumption that data and the noise components and the noise itself are somewhat uncorrelated.
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Key Findings & Data:Therefore, several proposed algorithms try to separate the data into two or more parts with varying noise levels so that denoising process can be applied on them with different parameters and constraints.
💡
Conclusion & Impact:Results are compared with that of the state of the art in well-known speech quality measures.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:speech · denoising · Common Vector Analysis

Abstract

Signal denoising approaches on data of any dimension largely relies on the assumption that data and the noise components and the noise itself are somewhat uncorrelated. However, any denoising process heavily depending on this assumption retreats when the signal component is adversely affected by the operation. Therefore, several proposed algorithms try to separate the data into two or more parts with varying noise levels so that denoising process can be applied on them with different parameters and constraints. In this paper, the proposed method separates the speech data into magnitude and phase where the magnitude part is further separated into common and difference parts using common vector analysis. It is assumed that the noise largely resides on difference part and therefore denoised by a known algorithm. The speech data is reconstructed by combining common, difference and phase parts. Using Linear Minimum Mean Square Error Estimation algorithm on the difference part, excellent denoising results are obtained. Results are compared with that of the state of the art in well-known speech quality measures.

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Cite This Article

Seke, E., Durak, M. H., Özkan, K. (2016). Speech Denoising using Common Vector Analysis in Frequency Domain. International Journal of Applied Mathematics Electronics and Computers, Special Issue (2016), 224–229. https://ajindex.com/articles/258188

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References (8)

  1. M. Dendrinos, S. Bakamidis and G. Carayannis, "Speech enhancement from noise: a regenerative approach," Speech Comm., vol. 10 (1), pp. 45-57, 1991.
  2. S. H. Jensen, P. C. Hansen, S. D. Hansen and J. A Sorensen, "Reduction of broadband noise in speech by truncated QSVD," IEEE Trans. Speech Audio Processing, vol. 3, pp. 439-448, 1995. Y. Ephraim and H. L. Van Trees, "A signal subspace approach for speech Enhancement," IEEE Trans. on Speech Audio Processing, 3, 251. 166, 1995. U. Mittal and N. Phamdo, "Signal/noise KLT based approach for enhancing speech degraded by colored noise," IEEE Trans. Speech and Audio Proc., vol. 8 (2), pp. 159-167, 2000. A. Rezayee and S. Gazor, "An Adaptive KLT Approach for Speech Enhancement," IEEE Trans. Speech and Audio Proc., vol. 9 (2), pp. 87-95, 2001. S. Ergin, "The Improvement and Recognition of the Noisy Speech Parameters," M. Eng. Thesis, Eskisehir Osmangazi University, Eskisehir, Turkey, 2004, [Online]. Available: http://ulusaltezmerkezi.com/gurultulu-ses-parametrelerinin-iyilestirilmesi-ve-taninmasi/. M. B. Gülmezoglu. and A. Barkana, "Text-dependent speaker recognition by using Gram-Schmidt orthogonalization method," Proc. of IASTED Int. Conf. on Sig. Proc. & App., 438-440, 1998.
  3. M. B. Gülmezoglu, V. Dzhafarov, M. Keskin and A. Barkana, "A novel approach to isolated word recognition," IEEE Trans. on Acoustic Speech and Signal Processing, vol. 7(6), pp. 620-628, 1999.
  4. M.B.Gülmezoglu, V. Dzhafarov and A. Barkana, "The Common Vector Approach and its relation to Principal Component Analysis," IEEE Trans. On Speech and Audio Processing, vol. 9( 6), pp. 655-662, 2001.
  5. M. B. Gülmezoglu, V. Dzhafarov and A. Barkana, "The Common Vector Approach and its Comparison with Other Subspace Methods in Case of Sufficient Data," Computer Speech and Language, vol. 21, pp. 266-281, 2007.
  6. H. Çevikalp, M. Neamtu, M. Wilkes and A. Barkana, "Discriminative Common Vectors for Face Recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27(1), pp. 4-13, 2005. M. B. Gülmezoglu and S. Ergin, "An Approach for Bearing Fault Detection in Electrical Motors," European Transactions on Electrical Power, vol. 17(6), pp. 628-641, 2007.
  7. S. Günal, S. Ergin, M. B. Gülmezoglu and Ö. N. Gerek, "On Feature Extraction for Spam E-Mail Detection," Lecture Notes in Computer Science, vol. 4105, pp. 635-642, 2006. K. Özkan and E. Seke, "Image Denoising Using Common Vector Approach, "IET Image Processing, vol. 9(8), pp. 709-715, 2015.
  8. L. Zhang, W. S.