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Adaptive Weiner Filter for Speech Enhancement under Various Noisy Conditions

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Akash A. Salmuthe, Rajesh K. Agrawal

Akash A Salmuthe and Rajesh K Agrawal. Adaptive Weiner Filter for Speech Enhancement under Various Noisy Conditions. International Journal of Computer Applications 170(7):9-11, July 2017. BibTeX

	author = {Akash A. Salmuthe and Rajesh K. Agrawal},
	title = {Adaptive Weiner Filter for Speech Enhancement under Various Noisy Conditions},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {7},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {9-11},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2017914912},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The performance of a noisy speech enhancement algorithm depends on the estimation of the priori signal-to-noise ratio (SNR). The most commonly used approach to estimate the priori SNR parameter uses Decision-Directed (DD), Two Step Noise Reduction (TSNR) and Harmonic Regeneration Noise Reduction (HRNR) method. Two-step noise reduction (TSNR) method eliminate this problem decision-directed method. Common short-time noise reduction techniques introduce harmonic distortion in enhanced speech because of the non-reliability of estimators for small signal to-noise ratios. A simple but effective harmonic regeneration method called Harmonic Regeneration Noise Reduction (HRNR) is used to overcome this problem.


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Speech enhancement; harmonic regeneration SNR; noise reduction; TSNR; HRNR.