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Pseudo Affine Projection Algorithm Based Noise Minimization from Speech Signals

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IJCA Proceedings on National Conference on Recent Trends in Electronics and Electrical Engineering
© 2018 by IJCA Journal
NCRTEEE 2017 - Number 1
Year of Publication: 2018
Authors:
Deepak Gupta
V. K. Gupta
A. N. Mishra

Deepak Gupta, V K Gupta and A N Mishra. Article: Pseudo Affine Projection Algorithm Based Noise Minimization from Speech Signals. IJCA Proceedings on National Conference on Recent Trends in Electronics and Electrical Engineering NCRTEEE 2017(1):1-5, August 2018. Full text available. BibTeX

@article{key:article,
	author = {Deepak Gupta and V. K. Gupta and A. N. Mishra},
	title = {Article: Pseudo Affine Projection Algorithm Based Noise Minimization from Speech Signals},
	journal = {IJCA Proceedings on National Conference on Recent Trends in Electronics and Electrical Engineering},
	year = {2018},
	volume = {NCRTEEE 2017},
	number = {1},
	pages = {1-5},
	month = {August},
	note = {Full text available}
}

Abstract

This paper presents the noise minimization from speech signal and how speech enhancement is done by using pseudo affine projection algorithm. Noise minimization is one of the major applications of the adaptive filters used in recent research areas. The Affine Projection algorithm and its variants are popular choice for noise minimization because of its fast convergence like recursive least square (RLS) and low complexity like least mean square (LMS) algorithm. The pseudo affine projection is a gradient type variant of affine projection algorithm with relaxed step-size conditions and less complexity which offers improved performance. The pseudo affine projection algorithm works successfully for local robustness properties of algorithms, as well as steady-state values of moderate to high accuracy specially when applied to long filter order. The maximum signal to noise ratio improvement (SNRI) achieved is 40. 22dB and the minimum mean square error (MSE) achieved is 0. 0031 at filter order 400 for input SNR of -20dB. The robustness of this algorithm is verified by evaluating it for various noises.

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