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17 March 2016 Speech signal denoising with wavelet-transforms and the mean opinion score characterizing the filtering quality
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Abstract
Speech signal processing is widely used to reduce noise impact in acquired data. During the last decades, wavelet-based filtering techniques are often applied in communication systems due to their advantages in signal denoising as compared with Fourier-based methods. In this study we consider applications of a 1-D double density complex wavelet transform (1D-DDCWT) and compare the results with the standard 1-D discrete wavelet-transform (1DDWT). The performances of the considered techniques are compared using the mean opinion score (MOS) being the primary metric for the quality of the processed signals. A two-dimensional extension of this approach can be used for effective image denoising.
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Alauldeen S. Yaseen, Alexey N. Pavlov, and Alexander E. Hramov "Speech signal denoising with wavelet-transforms and the mean opinion score characterizing the filtering quality", Proc. SPIE 9707, Dynamics and Fluctuations in Biomedical Photonics XIII, 970719 (17 March 2016); https://doi.org/10.1117/12.2211384
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