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Analysis of Optimized Spectral Subtraction Method for Single Channel Speech Enhancement

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Abstract

Speech is the primary entity for personal communication however ambient quality generally impairs speech signal quality and understanding of communication. Therefore, it is required that the distorted speech signal be improved in its quality and comprehension. In the field of speech processing, great efforts have been made to develop speech enhancement techniques that restore speech signals by reducing the amount of interfering noise. This work focuses on a critical analysis of single channel speech enhancement technique that performs noise reduction through spectral subtraction based on minimal statistics. Minimal statistics implies estimating the power spectrum of a non-standard noise signal by avoiding the problem of detecting speech activity by finding the smallest value for a smooth power spectrum of a noisy speech signal. The performance of the spectral subtraction method is evaluated over a wide range of noise types with varying sound levels using single channel speech data. This estimator is used to find the optimal value for the method parameter and improve this algorithm to make it more suitable for voice communication purposes. The system can be implemented in MATLAB and also validated against a variety of performance measures and various improvements in signal-to-noise ratio (SNRI) and spectral distortion (SD). This approach provides effective speech enhancement in SNRI and SD performance metrics. A comparatively new method has been proposed in this paper named Spectral Statistics Based on Minimum Statistics (SSBMS) which customarily follows the transient noise and provides a better response in the process of speech enhancement.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the NOIZEUS repository and IEEE DataPort which is a Dataset Storage and Dataset Search Platform. [1. https://ecs.utdallas.edu/loizou/speech/noizeus/ and 2. https://ieee-dataport.org/keywords/speech-dataset]. For the experimental purpose, the clean speech samples have been taken from NOIZEUS corpus, which is a publicly available speech database and is usually used for benchmark experiments. Data openly available in a public repository that issues datasets with DOIs and Data derived from public domain resources.

Code Availability

NA.

References

  1. Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(2), 113–120.

    Article  Google Scholar 

  2. McAulay, R., & Malpass, M. (1980). Speech enhancement using a soft-decision noise suppression filter. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(2), 137–145.

    Article  Google Scholar 

  3. Ghorpade, K., & Khaparde, A. (2022). Single channel speech enhancement using evolutionary algorithm with Log-MMSE. ASEAN Engineering Journal, 12(1), 83–91.

    Article  Google Scholar 

  4. Yang, Y., Zhang, H., Zhang, X., & Zhang, H (2022) Alleviating the Loss-Metric mismatch in supervised single-channel speech enhancement, In: ICASSP 2022–2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 6952–6956.

  5. Ephraim, Y., & Malah, D. (1984). Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(6), 1109–1121.

    Article  Google Scholar 

  6. Cappe, O. (1994). Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor. IEEE Transactions on Speech and Audio Processing, 2(2), 345–349.

    Article  Google Scholar 

  7. Martin, R. (2005). Speech enhancement based on minimum mean-square error estimation and super-gaussian priors. IEEE Transactions on Speech and Audio Processing, 13(5), 845–856.

    Article  Google Scholar 

  8. Gerkmann, T., & Hendriks, R. C. (2012). Unbiased MMSE-based noise power estimation with low complexity and low tracking delay. IEEE Transactions on Audio, Speech, and Language Processing, 20(4), 1383–1393.

    Article  Google Scholar 

  9. Wang, D., & Lim, J. (1982). The unimportance of phase in speech enhancement. IEEE Transactions on Acoustics, Speech, and Signal Processing, 30(4), 679–681.

    Article  Google Scholar 

  10. M. R. Weiss, A. E. Aschkenasy, and T. W. (1974) Parsons, Study and development of the intel technique for improving speech intelligibility, Nicolet Scientific Corp., Tech. Rep.

  11. Paliwal, K., Wojcicki, K., & Shannon, B. (2011). The importance of phase in speech enhancement. Speech Communication, 53(4), 465–494.

    Article  Google Scholar 

  12. P. Mowlaee and R. Martin (2012) On phase importance in parameter estimation for single-channel source separation. In: Proceedings International Workshop on Acoustic Signal Enhancement, pp. 1–4.

  13. Mowlaee, P., & Saeidi, R. (2013). Iterative closed-loop phase-aware single-channel speech enhancement. IEEE Signal Processing Letter, 20(12), 1235–1239.

    Article  Google Scholar 

  14. Mowlaee, P., & Kulmer, J. (2015). Phase estimation in single-channel speech enhancement: Limits-potential. IEEE Transactions on Audio, Speech, and Language Processing, 23(8), 1283–1294.

    Article  Google Scholar 

  15. Mowlaee, P., & Kulmer, J. (2015). Harmonic phase estimation in single-channel speech enhancement using phase decomposition and SNR information. IEEE Transactions on Audio, Speech, and Language Processing, 23(9), 1521–1532.

    Article  Google Scholar 

  16. Gerkmann, T., & Krawczyk, M. (2013). MMSE-optimal spectral amplitude estimation given the STFT-phase. IEEE Signal Processing Letter, 20(2), 129–132.

    Article  Google Scholar 

  17. Gerkmann, T. (2014). Bayesian estimation of clean speech spectral coefficients given a priori knowledge of the phase. IEEE Transactions on Signal Processing, 62(16), 4199–4208.

    Article  MATH  Google Scholar 

  18. Krawczyk, M., & Gerkmann, T. (2014). STFT phase reconstruction in voiced speech for an improved single-channel speech enhancement. IEEE Transactions on Audio, Speech, and Language Processing, 22(12), 1931–1940.

    Article  Google Scholar 

  19. Krawczyk-Becker, M., & Gerkmann, T. (2016). On MMSE-based estimation of amplitude and complex speech spectral coefficients under phase-uncertainty. IEEE Transactions on Audio, Speech, and Language Processing, 24(12), 2251–2262.

    Article  Google Scholar 

  20. Krawczyk-Becker, M., & Gerkmann, T. (2016). An evaluation of the perceptual quality of phase-aware single-channel speech enhancement. The Journal of the Acoustical Society of America, 140, 364–369.

    Article  Google Scholar 

  21. Deville, Y., Gannot, S., Mason, R., Plumbley, M. D., & Ward, D. (2018). A study on the benefits of phase-aware speech enhancement in challenging noise scenarios. In Y. Deville, S. Gannot, R. Mason, & M. D. Plumbley (Eds.), Latent Variable Analysis and Signal Separation (pp. 407–416). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  22. Mowlaee, P., Saeidi, R., & Stylianou, Y. (2016). Advances in phase-aware signal processing in speech communication. Speech Communication, 81, 1–29.

    Article  Google Scholar 

  23. Gerkmann, T., Krawczyk-Becker, M., & Roux, J. L. (2015). Phase processing for single channel speech enhancement: History and recent advances. IEEE Signal Processing Magazine, 32(2), 55–66.

    Article  Google Scholar 

  24. Krawczyk-Becker, M., & Gerkmann, T. (2018). On speech enhancement under PSD uncertainty. IEEE Transactions on Audio, Speech, and Language Processing, 26(6), 1144–1153.

    Article  Google Scholar 

  25. Xu, Y., Du, J., Dai, L., & Lee, C. (2015). A regression approach to speech enhancement based on deep neural networks. IEEE Transactions on Audio, Speech, and Language Processing, 23(1), 7–19.

    Article  Google Scholar 

  26. M. Kolbaek, Z. Tan, and J. Jensen, (2018) Monaural speech enhancement using deep neural networks by maximizing a short-time objective intelligibility measure.

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Authors and Affiliations

  1. Uttarakhand Technical University, Dehradun, India

    Monika Gupta

  2. Kumaon Engineering College, Dwarahat, India

    R. K. Singh

  3. NIT New Delhi, Delhi, India

    Sachin Singh

Authors
  1. Monika Gupta
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  2. R. K. Singh
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  3. Sachin Singh
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Ms. Monika Gupta has prepared this manuscript under the guidance of Dr. R.K. Singh and Dr. Sachin Singh.

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Correspondence to Monika Gupta.

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Gupta, M., Singh, R.K. & Singh, S. Analysis of Optimized Spectral Subtraction Method for Single Channel Speech Enhancement. Wireless Pers Commun 128, 2203–2215 (2023). https://doi.org/10.1007/s11277-022-10039-y

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