access icon free Real-time speech enhancement using optimised empirical mode decomposition and non-local means estimation

© The Institution of Engineering and Technology
Received 02/03/2020, Accepted 08/07/2020, Revised 29/05/2020, Published 12/08/2020

In this study, the authors present a novel speech enhancement method by exploring the benefits of non-local means (NLM) estimation and optimised empirical mode decomposition (OEMD) adopting cubic-spline interpolation. The optimal parameters responsible for improving the performance are estimated using the path-finder algorithm. At first, the noisy speech signal is decomposed into many scaled signals called intrinsic-mode functions (IMFs) through the use of a temporary decomposition method is called sifting process in OEMD approach. The obtained IMFs are processed by NLM estimation technique in terms of non-local similarities present in each IMF, to reduce the ill-effects caused by interfering noise. The proposed NLM-based method is effective to eliminate the noise of less-frequency. Each IMF contains essential information about the signals, on some scale or frequency band. Field programmable gate array architecture is implemented on a Xilinx ISE 14.5 and the result of the proposed method offers good performance with a high signal-to-noise ratio (SNR) and low mean-square error compared to other approaches. The performance evolution is carried out for different speech signals taken from the TIMIT database and noises taken from the NOISEX-92 database in different SNR stages of 0, 5 and 10 dB, respectively.

Inspec keywords: mean square error methods; signal denoising; performance evaluation; speech enhancement; Hilbert transforms; field programmable gate arrays; splines (mathematics); interpolation; parameter estimation

Other keywords: IMF; scaled signals; nonlocal similarities; NLM estimation technique; optimal parameter estimation; NLM-based method; nonlocal means estimation; noisy speech signal; mean-square error; intrinsic-mode functions; temporary decomposition method; high signal-to-noise ratio; TIMIT database; OEMD approach; NOISEX-92 database; Xilinx ISE 14.5; field programmable gate array architecture; cubic-spline interpolation; real-time speech enhancement; path-finder algorithm; performance improvement; optimised empirical mode decomposition

Subjects: Integral transforms in numerical analysis; Integral transforms in numerical analysis; Speech and audio signal processing; Speech processing techniques; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis)

References

    1. 1)
      • 1. Siddique, A., Yadava, G., Singh, B.: ‘A review of star fault monitoring techniques of induction motors’, IEEE Trans. Energy Convers., 2005, 20, (1), pp. 106–114.
    2. 2)
      • 16. Varga, A., Steeneken, H.: ‘Assessment for automatic speech recognition: iI. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems’, Speech Commun., 1993, 12, (3), pp. 247–251.
    3. 3)
      • 19. Camarena-Martinez, D., Valtierra-Rodriguez, M., Garcia-Perez, A., et al: ‘Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors’, Scientific World J., 2014, 2014, (1), pp. 1–17.
    4. 4)
      • 23. Tarpara, E., Patankar, V.: ‘Real time implementation of empirical mode decomposition algorithm for ultrasonic nondestructive testing applications’, Rev. Sci. Instrum., 2018, 89, (12), p. 125118.
    5. 5)
      • 27. Gupta, D., Gupta, V., Chandra, M., et al: ‘Real-time implementation of parallel architecture based noise minimization from speech signals on FPGA’, Wirel. Pers. Commun., 2018, 103, (3), pp. 1941–1963.
    6. 6)
      • 13. Goel, P., Chandra, M.: ‘VLSI implementations of retimed high speed adaptive filter structures for speech enhancement’, Microsyst. Technol., 2018, 24, (12), pp. 4799–4806.
    7. 7)
      • 24. Singh, P., Shahnawazuddin, S., Pradhan, G.: ‘An efficient ECG denoising technique based on non-local means estimation and modified empirical mode decomposition’, Circuits Syst. Signal Process., 2018, 37, (10), pp. 4527–4547.
    8. 8)
      • 2. Biswas, T., Mandal, S., Saha, D., et al: ‘FPGA based dual microphone speech enhancement’, Microsyst. Technol., 2019, 25, (3), pp. 765–775.
    9. 9)
      • 12. Giurcanu, M.: ‘Oracle M-estimation for time series models’, J. Time Ser. Anal., 2016, 38, (3), pp. 479–504.
    10. 10)
      • 17. Sharma, R., Vignolo, L., Schlotthauer, G., et al: ‘Empirical mode decomposition for adaptive AM-FM analysis of speech, a review’, Speech Commun., 2017, 88, (1), pp. 39–64.
    11. 11)
      • 7. Antonino-Daviu, J., Riera-Guasp, M., Pineda-Sanchez, M., et al: ‘A critical comparison between DWT and Hilbert–Huang-based methods for the diagnosis of rotor bar failures in induction machines’, IEEE Trans. Ind. Appl., 2009, 45, (5), pp. 1794–1803.
    12. 12)
      • 10. Stankus, A., Stankus, V., Kyselova, O., et al: ‘Frequency bands selection based on 24-hour heart rate using Hilbert–Huang transform’, Cybern. Syst. Anal., 2014, 50, (2), pp. 297–303.
    13. 13)
      • 22. Guo, T., Deng, Z.: ‘An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing’, Appl. Acoust., 2017, 127, (1), pp. 46–62.
    14. 14)
      • 5. Huang, N.E., Wu, Z.: ‘A review on Hilbert-Huang transform: method and its applications to geophysical studies’, Rev. Geophys., 2008, 46, (2), pp. 1–23.
    15. 15)
      • 20. Upadhyay, N., Karmakar, A.: ‘Speech enhancement using spectral subtraction-type algorithms: a comparison and simulation study’, Procedia Comput. Sci., 2015, 54, (1), pp. 574–584.
    16. 16)
      • 26. Chiea, R.A., Costa, M.H., Barrault, G.: ‘New insights on the optimality of parameterized Wiener filters for speech enhancement applications’, Speech Commun., 2019, 109, pp. 46–54.
    17. 17)
      • 21. Xu, J., Wang, Z., Tan, C., et al: ‘A novel denoising method for an acoustic-based system through empirical mode decomposition and an improved fruit fly optimization algorithm’, Appl. Sci., 2017, 7, (3), p. 215.
    18. 18)
      • 3. El Hachemi Benbouzid, M.: ‘A review of induction motors signature analysis as a medium for faults detection’, IEEE Trans. Ind. Electron., 2000, 47, (5), pp. 984–993.
    19. 19)
      • 9. Afonso, V., Tompkins, W., Nguyen, T., et al: ‘Comparing stress ECG enhancement algorithms’, IEEE Eng. Med. Biol. Mag., 1996, 15, (3), pp. 37–44.
    20. 20)
      • 28. Chaudhari, H., Nalbalwar, S.L., Sheth, R.: ‘A review on intrensic mode function of EMD’. Int. Conf. on Electrical, Electronics, and Optimization Technique, Chennai, India, March 2016, pp. 2349–2352.
    21. 21)
      • 14. Yapici, H., Cetinkaya, N.: ‘A new meta-heuristic optimizer: Pathfinder Algorithm’, Appl. Soft Comput., 2019, 78, (1), pp. 545–568.
    22. 22)
      • 11. Hong, Y.-Y., Bao, Y.-Q.: ‘FPGA implementation for real-time empirical mode decomposition’, IEEE Trans. Instrum. Meas., 2012, 61, (12), pp. 3175–3184.
    23. 23)
      • 4. Janatian, N., Sun, S., Modarres-Hashemi, M.: ‘Joint optimal spectrum sensing and power allocation in CDMA-based cognitive radio networks’, IEEE Trans. Veh. Technol., 2015, 64, (9), pp. 3990–3998.
    24. 24)
    25. 25)
      • 18. Srinivas, N., Pradhan, G., Kumar, P.: ‘A classification-based non-local means adaptive filtering for speech enhancement and its FPGA prototype’, Circuits Syst. Signal Process., 2019, 39, (1), pp. 2489–2506.
    26. 26)
      • 15. Vumanthala, S., Kalagadda, B.: ‘Nonlocal means estimation of intrinsic mode functions for speech enhancement’, Turkish J. Electr. Eng. Comput. Sci., 2020, 28, (1), pp. 318–330.
    27. 27)
      • 8. Yan, R., Gao, R.: ‘Hilbert–Huang transform-based vibration signal analysis for machine health monitoring’, IEEE Trans. Instrum. Meas., 2006, 55, (6), pp. 2320–2329.
    28. 28)
      • 6. Huag, N., Shen, Z., Long, S., et al: ‘The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis’, Proc. R. Soc. Lond. Ser. A, Math. Phys. Eng. Sci., 1998, 454, (1971), pp. 903–995.
    29. 29)
      • 29. Pallett, D., Lamel, L.: ‘Corrigendum to ‘Editorial’’, Speech Commun., 2002, 38, (1), pp. 211–211.
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