@article{(Open Science Index):https://publications.waset.org/pdf/10008257,
	  title     = {Speech Enhancement Using Wavelet Coefficients Masking with Local Binary Patterns},
	  author    = {Christian Arcos and  Marley Vellasco and  Abraham Alcaim},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, we present a wavelet coefficients masking
based on Local Binary Patterns (WLBP) approach to enhance the
temporal spectra of the wavelet coefficients for speech enhancement.
This technique exploits the wavelet denoising scheme, which splits
the degraded speech into pyramidal subband components and extracts
frequency information without losing temporal information. Speech
enhancement in each high-frequency subband is performed by binary
labels through the local binary pattern masking that encodes the ratio
between the original value of each coefficient and the values of the
neighbour coefficients. This approach enhances the high-frequency
spectra of the wavelet transform instead of eliminating them through
a threshold. A comparative analysis is carried out with conventional
speech enhancement algorithms, demonstrating that the proposed
technique achieves significant improvements in terms of PESQ, an
international recommendation of objective measure for estimating
subjective speech quality. Informal listening tests also show that
the proposed method in an acoustic context improves the quality
of speech, avoiding the annoying musical noise present in other
speech enhancement techniques. Experimental results obtained with a
DNN based speech recognizer in noisy environments corroborate the
superiority of the proposed scheme in the robust speech recognition
scenario.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {11},
	  number    = {12},
	  year      = {2017},
	  pages     = {1254 - 1259},
	  ee        = {https://publications.waset.org/pdf/10008257},
	  url   	= {https://publications.waset.org/vol/132},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 132, 2017},
	}