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A new double backward distributive weighted adaptive filtering approach for speech quality improvement

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Abstract

In modern telecommunication systems the presence of noise in background degrades the overall intelligibility and quality of the speech signal. The problem of enhancing speech signals and reducing acoustic noise from the noisy environment using adaptive filtering algorithms with incorporation of blind source separation approach has drawn a particular attention in the recent past. In this paper a dual channel double backward distributive weighted adaptive filtering algorithm is proposed for speech quality enhancement. The proposed method has been evaluated using the objective measures such as Perceptual Evaluation of Speech Quality (PESQ) and Short Time Objective Intelligibility (STOI) in different noise setup and the results achieved indicate that this is a better method for speech quality improvement.

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References

  • Adam, E. E. B. (2020). Deep learning based NLP techniques in text to speech synthesis for communication recognition. Journal of Soft Computing Paradigm (JSCP), 2(04), 209–215.

    Article  Google Scholar 

  • Al-Kindi, M. J., & Dunlop, J. (1989). Improved adaptive noise cancellation in the presence of signal leakage on the noise reference channel. Signal Processing, 17(3), 241–250.

    Article  MathSciNet  Google Scholar 

  • Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73–82.

    Google Scholar 

  • Benallal, A., & Arezki, M. (2014). A fast convergence normalized least-mean-square type algorithm for adaptive filtering. International Journal of Adaptive Control and Signal Processing, 28(10), 1073–1080.

    Article  MathSciNet  MATH  Google Scholar 

  • Bendoumia, R., & Djendi, M. (2015). Two-channel variable-step-size forward and backward adaptive algorithms for acoustic noise reduction and speech enhancement. Signal Processing, 108, 226–244.

    Article  Google Scholar 

  • Cioffi, J., & Kailath, T. (1984). Fast recursive least squares transversal filters for adaptive filtering. IEEE Transactions on Acoustic Speech Signal Processing ASSP, 32, 304–337.

    Article  MATH  Google Scholar 

  • Djendi, M., Henni, R., & Sayoud, A. (2016). A new dual forward BSS based RLS algorithm for speech enhancement. In International Conference on Engineering and MIS, ICEMIS 2016, Agadir, Morooco.

  • Djendi, M., & Bendoumia, R. (2013). A new adaptive filtering subband algorithm for two-channel acoustic noise reduction and speech enhancement. Computers & Electrical Engineering, 39(8), 2531–2550.

    Article  Google Scholar 

  • Djendi, M., & Bendoumia, R. (2014). A new efficient two-channel backward algorithm for speech intelligibility enhancement: A subband approach. Applied Acoustics, 76, 209–222.

    Article  Google Scholar 

  • Djendi, M., Gilloire, A., & Scalart, P. (2007). New frequency domain post-filters for noise cancellation using two closely spaced microphones. Proc EUSIPCO, Poznan, 1, 218–221.

    Google Scholar 

  • Gerven, S. V., & Compernolle, D. V. (1995). Signal separation by symmetric adaptive decorrelation: Stability, convergence, and uniqueness. IEEE Transactions on Signal Processing, 43(7), 1602–1612.

    Article  Google Scholar 

  • Ghribi, K., Djendi, M., & Berkani, D. (2016). A New wavelet-based forward BSS algorithm for acoustic noise reduction and speech quality enhancement. Applied Acoustics, 105, 55–66.

    Article  Google Scholar 

  • Kajla, P., & George, N. V. (2020). Speech quality enhancement using a two channel sparse adaptive filtering approach. Applied Acoustics, 158, 107035.

    Article  Google Scholar 

  • Manoharan, S. (2019). A smart image processing algorithm for text recognition information extraction and vocalization for the visually challenged. Journal of Innovative Image Processing (JIIP), 1(01), 31–38.

    Article  Google Scholar 

  • Mirchandani, G., Zinser, R. L., & Evans, J. B. (1992). A new adaptive noise cancellation scheme in the presence of crosstalk. IEEE Transactions on Circuits and Systems, 39(10), 681–694.

    Article  MATH  Google Scholar 

  • Mitra, A. (2020). Sentiment analysis using machine learning approaches (Lexicon based on movie review dataset). Journal of Ubiquitous Computing and Communication Technologies (UCCT), 2(03), 145–152.

    Article  Google Scholar 

  • Nicolas, D., & Mike, B. (2019). Modulation domain kalman filtering for monaural blind speech denoising and dereverberation. IEEE/ACM Transactions on Audio, Speech and Language Processing, 27(4), 799–814.

    Article  Google Scholar 

  • Ozeki, K., & Umeda, T. (1984). An adaptive filtering algorithm using an orthogonal projection to an affine subspace and its properties. Electronics and Communications in Japan, 67(5), 19–27.

    Article  MathSciNet  Google Scholar 

  • Rahima, H., Mohamed, D., & Djebari, M. (2018). A dual backward adaptive algorithm for speech enhancement and acoustic noise reduction. In Proceedings of the Fourth International Conference on Engineering & MIS 2018 (pp. 1–4).

  • Raj, J. S. (2019). A comprehensive survey on the computational intelligence techniques and its applications. Journal of ISMAC, 1(03), 147–159.

    Article  Google Scholar 

  • Rakesh, P., & Kumar, T. K. (2015). A novel RLS adaptive filtering method for speech enhancement. Electrical, Computer, Energetic, 9(2), 225–229.

    Google Scholar 

  • Rix, A. W., Beerends, J. G., Hollier, M. P., & Hekstra, A. P. (2001). Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs. In 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221) (Vol. 2, pp. 749-752). IEEE

  • Sayoud, A., Djendi, M., Medahi, S., & Guessoum, A. (2018). A dual fast nlms adaptive filtering algorithm for blind speech quality enhancement. Applied Acoustics, 135, 101–110.

    Article  Google Scholar 

  • Shrawankar, U., & Thakare, V. (2010). Noise estimation and noise removal techniques for speech recognition in adverse environment. In International Conference on Intelligent Information Processing (pp. 336–342). Springer, Berlin,

  • Taal, C. H., Hendriks, R. C., Heusdens, R., & Jensen, J. (2010). A short-time objective intelligibility measure for time-frequency weighted noisy speech. In 2010 IEEE international conference on acoustics, speech and signal processing (pp. 4214–4217).

  • Widrow, B., & Hoff, M. (1960). Adaptive switching circuits. In Proceedings of IRE Western Electronic Show and Convention (Part 4, pp. 96–104).

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Authors and Affiliations

  1. ECE Department, National Institute of Technology, Kurukshetra, Haryana, India

    V. Srinivasarao & Umesh Ghanekar

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  1. V. Srinivasarao
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  2. Umesh Ghanekar
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Correspondence to V. Srinivasarao.

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Srinivasarao, V., Ghanekar, U. A new double backward distributive weighted adaptive filtering approach for speech quality improvement. Int J Speech Technol 25, 831–836 (2022). https://doi.org/10.1007/s10772-021-09894-0

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  • DOI: https://doi.org/10.1007/s10772-021-09894-0

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