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Performance analysis of adaptive variational mode decomposition approach for speech enhancement

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Abstract

Speech enhancement is an important pre-processing task in the area of speech processing research. Many techniques have been applied in this area since four/five decades. With progressive research it occupies a special position in various fields like engineering, medicine, society and security. Adaptive algorithms found effective for such cases and are utilized in this problem. The work is based on decomposition method using variational mode decomposition (VMD) technique, where the decomposed components signify the frequency characteristics of the signal. Since Wiener filtering is used in VMD inherently, it is modified with the least mean squares (LMS) adaptive algorithm for good accuracy and adaptability in this work. Different noises like Babble noise, Street noise, and Exhibition noise are considered and the corresponding signals are decomposed into five intrinsic mode functions (IMFs). Basically, the lower modes are of high frequency and noisy; whereas the higher mode IMFs contain the low and medium frequency components and are considered as the enhanced signal. The results of the proposed algorithm are found excellent as compared to earlier techniques. The resultant wave forms are visually observed and the sound is verified for audible range. Also different measuring parameters are considered for its performance measure. It is measured in terms of signal-to-noise ratio (SNR), segmental signal to noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ) and log spectral distance (LSD). The technique is verified with standard database NOIZEUS for 0, 5, 10, 15 dB respectively and also in real world case.

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References

  • Bertsekas, D. P. (2014). Constrained optimization and Lagrange multiplier methods. New York: Academic Press.

    MATH  Google Scholar 

  • Chatlani, N., & Soraghan, J. J. (2012). EMD-based filtering (EMDF) of low-frequency noise for speech enhancement. IEEE Transactions on Audio, Speech, and Language Processing, 20(4), 1158–1166.

    Article  Google Scholar 

  • Chergui, L., & Bouguezel, S. (2017). A new pre-whitening transform domain LMS algorithm and its application to speech denoising. Signal Processing, 130, 118–128.

    Article  Google Scholar 

  • Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.

    Article  MathSciNet  Google Scholar 

  • El-Fattah, M. A. A., Dessouky, M. I., Abbas, A. M., Diab, S. M., El-Rabaie, E. S. M., Al-Nuaimy, W., et al. (2014). Speech enhancement with an adaptive Wiener filter. International Journal of Speech Technology, 17(1), 53–64.

    Article  Google Scholar 

  • Gowri, B. G., Kumar, S. S., & Mohan, N., & Soman, K. P. (2016). A VMD based approach for speech enhancement. In S. Thampi, S. Bandyopadhyay, S. Krishnan, K. C. Li, S. Mosin, & M. Ma (Eds.), Advances in signal processing and intelligent recognition systems (pp. 309–321). Cham: Springer.

    Chapter  Google Scholar 

  • Hadei, S. (2011). A family of adaptive filter algorithms in noise cancellation for speech enhancement. arXiv preprint arXiv:1106.0846.

  • Hahn, S. L. (1996). Hilbert transforms in signal processing. Boston: Artech House.

    MATH  Google Scholar 

  • Haykin, S. (1996). Adaptive filter theory, Prentice Hall information and system sciences series. Upper Saddle: Prentice Hall.

    Google Scholar 

  • Hu, Y., & Loizou, P. C. (2008). Evaluation of objective quality measures for speech enhancement. IEEE Transactions on Audio, Speech, and Language Processing, 16(1), 229–238.

    Article  Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995.

    Article  MathSciNet  MATH  Google Scholar 

  • Khaldi, K., Boudraa, A. O., & Komaty, A. (2014). Speech enhancement using empirical mode decomposition and the Teager–Kaiser energy operator. The Journal of the Acoustical Society of America, 135(1), 451–459.

    Article  Google Scholar 

  • Khaldi, K., Boudraa, A. O., & Turki, M. (2016). Voiced/unvoiced speech classification-based adaptive filtering of decomposed empirical modes for speech enhancement. IET Signal Processing, 10(1), 69–80.

    Article  Google Scholar 

  • Liu, Y., Yang, G., Li, M., & Yin, H. (2016). Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Processing, 125, 349–364.

    Article  Google Scholar 

  • Loizou, P. C. (2013). Speech enhancement: Theory and practice. Boca Raton: CRC Press.

    Google Scholar 

  • Malik, M. B. (2004). State-space recursive least-squares: Part I. Signal Processing, 84(9), 1709–1718.

    Article  MATH  Google Scholar 

  • Mavaddaty, S., Ahadi, S. M., & Seyedin, S. (2016). A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation. Speech Communication, 76, 42–60.

    Article  Google Scholar 

  • Quatieri, T. F. (2002). Discrete-time speech signal processing: Principle and practice. New York: Prentice Hall.

    Google Scholar 

  • Ram, R., & Mohanty, M. N. (2016). Performance analysis of adaptive algorithms for speech enhancement applications. Indian Journal of Science and Technology. https://doi.org/10.17485/ijst/2016/v9i44/102867.

    Google Scholar 

  • Ram, R., & Mohanty, M. N. (2017). Comparative analysis of EMD and VMD algorithm in speech enhancement. International Journal of Natural Computing Research (IJNCR), 6(1), 17–35.

    Article  Google Scholar 

  • Ram, R., Patra, S., & Mohanty, M. N. (2017). Application of variational mode decomposition on speech enhancement. Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering. https://doi.org/10.15439/2017R27.

    Google Scholar 

  • Upadhyay, A., & Pachori, R. B. (2017). Speech enhancement based on mEMD-VMD method. Electronics Letters, 53(7), 502–504.

    Article  Google Scholar 

  • Upadhyay, A., Sharma, M., & Pachori, R. B. (2017). Determination of instantaneous fundamental frequency of speech signals using variational mode decomposition. Computers and Electrical Engineering, 62, 630–647.

    Article  Google Scholar 

  • Upadhyay, N., & Jaiswal, R. K. (2016). Single channel speech enhancement: Using Wiener filtering with recursive noise estimation. Procedia Computer Science, 84, 22–30.

    Article  Google Scholar 

  • Vihari, S., Murthy, A. S., Soni, P., & Naik, D. C. (2016). Comparison of speech enhancement algorithms. Procedia Computer Science, 89, 666–676.

    Article  Google Scholar 

  • Wang, Y., & Markert, R. (2016). Filter bank property of variational mode decomposition and its applications. Signal Processing, 120, 509–521.

    Article  Google Scholar 

  • Widrow, B., Stearns, S. D., & Burgess, J. C. (1986). Adaptive signal processing edited by Bernard Widrow and Samuel D. Stearns. The Journal of the Acoustical Society of America, 80(3), 991–992.

    Article  Google Scholar 

  • Zao, L., Coelho, R., & Flandrin, P. (2014). Speech enhancement with EMD and Hurst-based mode selection. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(5), 899–911.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, (Deemed to be University), Bhubaneswar, Odisha, India

    Rashmirekha Ram & Mihir Narayan Mohanty

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  1. Rashmirekha Ram
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  2. Mihir Narayan Mohanty
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Correspondence to Mihir Narayan Mohanty.

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Ram, R., Mohanty, M.N. Performance analysis of adaptive variational mode decomposition approach for speech enhancement. Int J Speech Technol 21, 369–381 (2018). https://doi.org/10.1007/s10772-018-9515-8

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  • DOI: https://doi.org/10.1007/s10772-018-9515-8

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