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A wavelet based method for removal of highly non-stationary noises from single-channel hindi speech patterns of low input SNR

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Abstract

This paper presents a binary mask thresholding function in Doubachies10 wavelet transform for enhancement of highly non-stationary noise mixed single-channel Hindi speech patterns of low (negative) SNR. In the wavelet transform, a five level of decomposition is used and detailed coefficients of all five levels are given to binary mask thresholding function for removing noise and enhancing the speech patterns. The robustness of the proposed method is compared with the wildly popular methods such as log-mmse, test-psc, Wiener, IdBM, and spectral-subtraction on the basis of performance measure parameters viz SNR, PSNR, PESQ, and Cepstrum distance. The algorithms were implemented in MATLAB 7.1.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Sachin Singh, Manoj Tripathy & R. S. Anand

Authors
  1. Sachin Singh
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  2. Manoj Tripathy
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  3. R. S. Anand
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Correspondence to Sachin Singh.

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Singh, S., Tripathy, M. & Anand, R.S. A wavelet based method for removal of highly non-stationary noises from single-channel hindi speech patterns of low input SNR. Int J Speech Technol 18, 157–166 (2015). https://doi.org/10.1007/s10772-014-9255-3

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  • DOI: https://doi.org/10.1007/s10772-014-9255-3

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