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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31867–31891 | Cite as

Variance based time-frequency mask estimation for unsupervised speech enhancement

  • Nasir SaleemEmail author
  • Muhammad Irfan Khattak
  • Gunawan Witjaksono
  • Gulzar Ahmad
Article
  • 71 Downloads

Abstract

Variance based two dimensional time-frequency mask estimation for unsupervised speech enhancement is proposed to improve the speech quality and intelligibility by reducing the low-frequency residual noise distortion in the noisy speech signals. Unlike conventional speech enhancement methods, the proposed method is able to reduce the residual noise distortion by utilizing benefits of the less aggressive Wiener gain and variance based two dimensional time-frequency mask to establish a two-stage speech enhancement method. In the first stage, the less aggressive Wiener gain with modified a priori signal-to-noise (SNR) estimate is applied to the input noisy speech to obtain a reduced noise pre-processed speech signal. In the second stage, variance based features are extracted from the pre-processed speech and compared to a nonparametric adaptive threshold to construct a two dimensional time-frequency mask. The estimated mask is then applied to the pre-processed speech from the first stage to suppress the annoying residual noise distortion. A comparative performance study is included to demonstrate the effectiveness of the proposed method in various noisy conditions. The experimental results showed large improvements in terms of the perceptual evaluation of speech quality (PESQ), segmental SNR (SegSNR), residual noise distortion (BAK) and speech distortion (SIG) over that achieved with competing methods at different input SNRs. To measure the understanding of enhanced speech in different noisy conditions, short-time intelligibility prediction (STOI) is used which reinforced a better performance of the proposed method in terms of the speech intelligibility. The time-varying spectral analysis validated significant reduction of the residual noise components in the enhanced speech.

Keywords

A priori SNR estimation Speech enhancement Time-frequency masking Variance-based features Wiener gain Intelligibility Speech quality 

Notes

References

  1. 1.
    Abel A, Hussain A (2015). Cognitively inspired audiovisual speech filtering: towards an intelligent, fuzzy based, multimodal, two-stage speech enhancement system(Vol. 5). SpringerGoogle Scholar
  2. 2.
    Aicha AB (2017) Noise estimation for speech enhancement algorithms with post-smoothness processor incorporating global posterior SNR. Multimed Tools Appl 76(22):23661–23678CrossRefGoogle Scholar
  3. 3.
    Bao F, Abdulla WH (2018) Noise masking method based on an effective ratio mask estimation in Gammatone channels. APSIPA Transactions on Signal and Information Processing, 7Google Scholar
  4. 4.
    Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Signal Process 27(2):113–120CrossRefGoogle Scholar
  5. 5.
    Braun S, Kowalczyk K, Habets EA (2015) In Residual noise control using a parametric multichannel Wiener filter, Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, IEEE; pp 360–364Google Scholar
  6. 6.
    Chatlani N, Soraghan JJ (2012) EMD-based filtering (EMDF) of low-frequency noise for speech enhancement. IEEE Trans Audio Speech Lang Process 20(4):1158–1166CrossRefGoogle Scholar
  7. 7.
    Chehrehsa S, Moir TJ (2017) Speech and noise power estimation using gamma modeling. International Journal of Adaptive Control and Signal Processing 31(10):1491–1502MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Cohen I, Berdugo B (2002) Noise estimation by minima controlled recursive averaging for robust speech enhancement. IEEE Signal processing letters 9(1):12–15CrossRefGoogle Scholar
  9. 9.
    Ephraim Y, Malah D (1984) Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans Acoust Speech Signal Process 32(6):1109–1121CrossRefGoogle Scholar
  10. 10.
    Ephraim Y, Malah D (1985) Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Trans Acoust Speech Signal Process 33(2):443–445CrossRefGoogle Scholar
  11. 11.
    Ferreira LB, Duarte AB, da Cunha FF, Fernandes Filho EI (2019) Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data. Acta Scientiarum Agronomy 41:e39880CrossRefGoogle Scholar
  12. 12.
    Goehring T, Bolner F, Monaghan JJ, van Dijk B, Zarowski A, Bleeck S (2017) Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users. Hear Res 344:183–194CrossRefGoogle Scholar
  13. 13.
    Gogate M, Adeel A, Marxer R, Barker J, Hussain A (2018) Dnn driven speaker independent audio-visual mask estimation for speech separation. arXiv preprint arXiv:1808.00060Google Scholar
  14. 14.
    Guang-Yan W, Xiao-qun Z, Xia W (2009) Musical noise reduction based on spectral subtraction combined with Wiener filtering for speech communicationGoogle Scholar
  15. 15.
    Gustafsson H, Nordholm SE, Claesson I (2001) Spectral subtraction using reduced delay convolution and adaptive averaging. IEEE transactions on speech and audio processing 9(8):799–807CrossRefGoogle Scholar
  16. 16.
    Han T, Yao H, Sun X, Zhao S, Zhang Y (2016) Unsupervised discovery of crowd activities by saliency-based clustering. Neurocomputing 171:347–361CrossRefGoogle Scholar
  17. 17.
    Hermus K, Wambacq P (2006) A review of signal subspace speech enhancement and its application to noise robust speech recognition. EURASIP journal on advances in signal processing 2007(1):045821MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Hirsch H-G, Pearce D (2000) In The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions, ASR2000-Automatic Speech Recognition: Challenges for the new Millenium ISCA Tutorial and Research Workshop (ITRW)Google Scholar
  19. 19.
    Hu Y, Loizou PC (2003) A generalized subspace approach for enhancing speech corrupted by colored noise. IEEE transactions on speech and audio processing 11(4):334–341CrossRefGoogle Scholar
  20. 20.
    Hu Y, Loizou PC (2008) Evaluation of objective quality measures for speech enhancement. IEEE Trans Audio Speech Lang Process 16(1):229–238CrossRefGoogle Scholar
  21. 21.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) In 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, The Royal Society; pp 903–995Google Scholar
  22. 22.
    Kamath S, Loizou, P. (2002) In A multi-band spectral subtraction method for enhancing speech corrupted by colored noise, ICASSP, pp 44164–44164Google Scholar
  23. 23.
    Li H, Wang Y, Zhao R, Zhang X (2018) An unsupervised two-talker speech separation system based on CASA. Int J Pattern Recognit Artif Intell 32(07):1858002CrossRefGoogle Scholar
  24. 24.
    Lim J, Oppenheim A (1978) All-pole modeling of degraded speech. IEEE Trans Acoust Speech Signal Process 26(3):197–210zbMATHCrossRefGoogle Scholar
  25. 25.
    Liu Z, Wang T. (2016) An Adaptive Image Denoising Algorithm Based on Wavelet Transform and Independent Component Analysis, Sixth International Conference on Intelligent Systems Design and Engineering Applications. IEEE:104–107Google Scholar
  26. 26.
    Loizou P (2007) Subjective evaluation and comparison of speech enhancement methods. Speech Commun 49:588–601CrossRefGoogle Scholar
  27. 27.
    Lu C-T (2007) Reduction of musical residual noise for speech enhancement using masking properties and optimal smoothing. Pattern Recogn Lett 28(11):1300–1306CrossRefGoogle Scholar
  28. 28.
    Lu C-T (2014) Noise reduction using three-step gain factor and iterative-directional-median filter. Appl Acoust 76:249–261CrossRefGoogle Scholar
  29. 29.
    Lu Y, Loizou PC (2011) Estimators of the magnitude-squared spectrum and methods for incorporating SNR uncertainty. IEEE Trans Audio Speech Lang Process 19(5):1123CrossRefGoogle Scholar
  30. 30.
    Luo Y, Mesgarani N (2018) TasNet: Surpassing ideal time-frequency masking for speech separation. arXiv preprint arXiv:1809.07454Google Scholar
  31. 31.
    Martin R (2001) Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE transactions on speech and audio processing 9(5):504–512CrossRefGoogle Scholar
  32. 32.
    Marxer R, Barker J (2017) Binary Mask Estimation Strategies for Constrained Imputation-Based Speech Enhancement. In INTERSPEECH, pp. 1988–1992Google Scholar
  33. 33.
    Min G, Zhang X, Zou X, Sun M (2016) In Mask estimate through Itakura-Saito nonnegative RPCA for speech enhancement, Acoustic Signal Enhancement (IWAENC), 2016 IEEE International Workshop on, IEEE; pp 1–5Google Scholar
  34. 34.
    Nasir S, Sher A, Usman K, Farman U (2013) Speech enhancement with geometric advent of spectral subtraction using connected time-frequency regions noise estimation. Res J Appl Sci Eng Technol 6(6):1081–1087CrossRefGoogle Scholar
  35. 35.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9(1):62–66CrossRefGoogle Scholar
  36. 36.
    Rahali H, Hajaiej Z (2017) Enhancement of noise-suppressed speech by spectral processing implemented in a digital signal processor. Analog Integr Circ Sig Process 93(2):341–350CrossRefGoogle Scholar
  37. 37.
    Rangachari S, Loizou PC (2006) A noise-estimation method for highly non-stationary environments. Speech Comm 48(2):220–231CrossRefGoogle Scholar
  38. 38.
    Renson L, Sieber J, Barton DAW, Shaw AD, Neild SA (2019) Numerical Continuation in Nonlinear Experiments using Local Gaussian Process Regression. arXiv preprint arXiv:1901.06970Google Scholar
  39. 39.
    Rix AW, Beerends JG, Hollier MP, Hekstra AP (2001) In Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs, Acoustics, Speech, and Signal Processing, 2001. Proceedings.(ICASSP'01). 2001 IEEE International Conference on, IEEE: pp 749–752Google Scholar
  40. 40.
    Rothauser E (1969) IEEE recommended practice for speech quality measurements. IEEE Trans on Audio and Electroacoustics 17:225–246CrossRefGoogle Scholar
  41. 41.
    Saleem N (2017) Single channel noise reduction system in low SNR. International Journal of Speech Technology 20(1):89–98MathSciNetCrossRefGoogle Scholar
  42. 42.
    Saleem N, Ijaz G (2018) Low rank sparse decomposition model based speech enhancement using gammatone filterbank and Kullback–Leibler divergence. International Journal of Speech Technology 21(2):217–231CrossRefGoogle Scholar
  43. 43.
    Saleem N, Irfan M (2018) Noise reduction based on soft masks by incorporating SNR uncertainty in frequency domain. Circuits, Systems, and Signal Processing 37(6):2591–2612CrossRefGoogle Scholar
  44. 44.
    Saleem N, Shafi M, Mustafa E, Nawaz A (2015) A novel binary mask estimation based on spectral subtraction gain-induced distortions for improved speech intelligibility and quality. University of Engineering and technology Taxila. Technical Journal 20(4):36Google Scholar
  45. 45.
    Saleem N, Khattak MI, Shafi M (2018) Unsupervised speech enhancement in low SNR environments via sparseness and temporal gradient regularization. Appl Acoust 141:333–347CrossRefGoogle Scholar
  46. 46.
    Scalart P (1996) In Speech enhancement based on a priori signal to noise estimation, Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings, 1996 IEEE International Conference on, IEEE; pp 629-63e2Google Scholar
  47. 47.
    Singh S, Tripathy M, Anand R (2015) Binary mask based method for enhancement of mixed noise speech of low SNR input. International Journal of Speech Technology 18(4):609–617CrossRefGoogle Scholar
  48. 48.
    Sorensen KV, Andersen SV (2005) Speech enhancement with natural sounding residual noise based on connected time-frequency speech presence regions. EURASIP Journal on Applied Signal Processing 2005:2954–2964zbMATHGoogle Scholar
  49. 49.
    Srinivasan S, Roman N, Wang D (2006) Binary and ratio time-frequency masks for robust speech recognition. Speech Comm 48(11):1486–1501CrossRefGoogle Scholar
  50. 50.
    Taal CH, Hendriks RC, Heusdens R, Jensen J (2011) An method for intelligibility prediction of time–frequency weighted noisy speech. IEEE Trans Audio Speech Lang Process 19(7):2125–2136CrossRefGoogle Scholar
  51. 51.
    Tavares R, Coelho R (2016) Speech enhancement with nonstationary acoustic noise detection in time domain. IEEE Signal Processing Letters 23(1):6–10CrossRefGoogle Scholar
  52. 52.
    Wang D (2005) On ideal binary mask as the computational goal of auditory scene analysis. In Speech separation by humans and machines, Springer: pp 181–197Google Scholar
  53. 53.
    Wang D (2008) Time-frequency masking for speech separation and its potential for hearing aid design. Trends in Amplification 12(4):332–353CrossRefGoogle Scholar
  54. 54.
    Wang D, Brown GJ (2006) Computational auditory scene analysis: Principles, methods, and applications. Wiley-IEEE pressGoogle Scholar
  55. 55.
    Yan C, Xie H, Chen J, Zha Z, Hao X, Zhang Y, Dai Q (2018) A fast uyghur text detector for complex background images. IEEE Transactions on Multimedia 20(12):3389–3398CrossRefGoogle Scholar
  56. 56.
    Yan C, Li L, Zhang C, Liu B, Zhang Y, Dai Q (2019) Cross-modality bridging and knowledge transferring for image understanding. IEEE Transactions on MultimediaGoogle Scholar
  57. 57.
    Yan C, Li Z, Zhang Y, Qin P, Ji X and Dai Q. (2019) Depth image denoising using nuclear norm and learning graph model. IEEE Transactions on MultimediaGoogle Scholar
  58. 58.
    Yan C, Tu Y, Wang X, Zhang Y, Hao X, Zhang Y and Dai Q (2019) STAT: Spatial-Temporal Attention Mechanism for Video Captioning. IEEE Transactions on MultimediaGoogle Scholar
  59. 59.
    You X, Du L, Cheung Y-m, Chen Q (2010) A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans Image Process 19(12):3271–3284MathSciNetzbMATHCrossRefGoogle Scholar
  60. 60.
    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):899–911CrossRefGoogle Scholar
  61. 61.
    Zhao S, Yao H, Wang F, Jiang X, Zhang W (2014) Emotion based image musicalization. IEEE International conference on multimedia and expo workshops (ICMEW) pp. 1–6Google Scholar
  62. 62.
    Zou X, Jancovic P, Liu J, Kokuer M (2008) Speech signal enhancement based on MAP method in the ICA space. IEEE Trans Signal Process 56(5):1812–1820MathSciNetzbMATHCrossRefGoogle Scholar
  63. 63.
    Zou Y, Liu Z, Ritz C (2018) Enhancing target speech based on nonlinear soft masking using a single acoustic vector sensor. Appl Sci 8(9):1436CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of Engineering & TechnologyGomal UniversityD.I.KhanPakistan
  2. 2.Department of Electrical EngineeringUniversity of Engineering & TechnologyPeshawarPakistan
  3. 3.Department of Electrical and Electronics EngineeringUniversity Technology PETRONASSeri IskandarMalaysia

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