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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Analysis of Least Mean Square and Recursive Least Squared Adaptive Filter Algorithm for Speech Enhancement Application

verfasst von : Mrinal Bachute, R. D. Kharadkar

Erschienen in: Smart and Innovative Trends in Next Generation Computing Technologies

Verlag: Springer Singapore

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Abstract

Speech enhancement is a vital area of research, the performance of speech based human machine applications such as automatic speech recognition system, in car communication depends on the quality of speech communicated. Different methodologies have been used by various researchers to improve the quality of speech signal. In this paper an attempt is made to analyze the performance of Least Mean Square (LMS) and Recursive Least Squared (RLS) adaptive filter algorithm for speech enhancement application. The performance indices used for the evaluations is Mean Square Error (MSE), Signal to Noise Ration (SNR) and execution time. The detail analysis is done and experimentally the results are validated and certain modifications are suggested in the algorithm. The experimentation revels that LMS have fast convergence than RLS. The computational complexity of RLS is very high as compared to LMS.

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Vorheriges Kapitel Effective Heuristics for the Bi-objective Euclidean Bounded Diameter Minimum Spanning Tree Problem
Nächstes Kapitel A Novel Statistical Pre-processing Based Spatial Anomaly Detection Model on Cyclone Dataset
Literatur
1.
Jie, Y., Zhenli, W.: On the application of variable-step adaptive noise cancelling for improving the robustness of speech recognition. In: ISECS International Colloquium on Computing, Communication, Control, and Management 2009 CCCM 2009, vol. 2, pp. 419–422. IEEE (2009)
2.
Pandey, A., Malviya, L.D., Sharma, V.: Comparative study of LMS and NLMS algorithms in adaptive equalizer. Int. J. Eng. Res. Appl. (IJERA) 2(3), 1584–1587 (2012)
3.
Douglas, S.C., Pan, W.: Exact expectation analysis of the LMS adaptive filter. IEEE Trans. Signal Process. 43(12), 2863–2871 (1995)CrossRef
4.
Chu, H.S., An, C.K.: Design of the adaptive noise canceler using neural network with backpropagation algorithm. In: Proceedings the Third Russian-Korean International Symposium on Science and Technology, 1999, KORUS 1999, vol. 2, pp. 762–764. IEEE (1999)
5.
Hu, Y., Loizou, P.C.: Subjective comparison and evaluation of speech enhancement algorithms. Speech Commun. J. SPCOM Sci. Direct 49, 588–601 (2006)CrossRef
6.
Caraiscos, C., Liu, B.: A round of error analysis of LMS adaptive algorithm. IEEE Trans. Acoust. Speech Signal Process. 32, 34–41 (2000)CrossRef
7.
Gardner, W.: Learning characteristics of scholastic gradient descent algorithms: a general study analysis & critique. IEEE Sig. Process. 6, 113–133 (2010)CrossRef
8.
9.
Douglas, S.C., Pan, W.: Exact expectation analysis of the LMS adaptive filter’. IEEE Trans. Sig. Process. 43(12), 2863–2871 (1995)CrossRef
10.
11.
Douglas, S.C.: Exact expectation analysis without the independence assumption of the LMS adaptive filter. IEEE Trans. Sig. Process. 43(12), 2863–2871 (2000)CrossRef
12.
Yang, F., Wu, M., Ji, P., Yang, J.: An improved multiband-structured subband adaptive filter algorithm. 19(10) 647–650 (2012)
13.
Sami, S., Padmaja, P.: Speech enhancement using fast adaptive Kalman filtering algorithm along with weighting filter. 2(5) 387–390 (2013)
14.
Ravi, B., Kumar, T.K.: Speech enhancement using kernel and normalized kernel affine projection. 4(4), 129–138 (2013)
15.
Widrow, B., Stearns, S.D.: Adaptive Signal Processing, 4th edn. Cliff, Prentice Hall, Upper Saddle River (2009)MATH
16.
Haykin, S.: Adaptive Filter Theory. Pearson Education, London (2011)MATH
17.
Farhang Boroujeny, B.: Adaptive Filters: Theory and Applications. Wiley Publications, Hoboken (2006)MATH
18.
Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, Hoboken, pp. 493–552 (1996)
19.
Haykin, S.: Adaptive Filter Theory. Prentice-Hall Inc, Upper Saddle River (1996)MATH
20.
Bachute, M.: Performance Evaluation of PSO based Speech Enhancement Technique for Speech Communication System Technical Journal of Institution of Engineers (India) Pune Local Center, India, October 2013. ISBN 978-81-924990-1-7
21.
Bachute, M.R., Kharadkar, R.D.: Performance analysis and comparison of complex LMS, sign LMS and RLS algorithms for speech enhancement application. IJCSN Int. J. Comput. Sci. Netw. 4(5) (2002)
22.
Bachute, M.R., Kharadkar, R.D.: Performance analysis and comparison of complex LMS, sign LMS and RLS algorithms for speech enhancement application
23.
Bachute, M., Kharadkar, R.D.: Analysis and implementation of time-varying least mean square algorithm and modified Time-Varying LMS for speech enhancement. Int. J. Sci. Res. (IJSR). ISSN (Online) 2319-7064, Index Copernicus Value (2013): 6.14, Impact Factor (2013)
Metadaten
Titel
Analysis of Least Mean Square and Recursive Least Squared Adaptive Filter Algorithm for Speech Enhancement Application
verfasst von
Mrinal Bachute
R. D. Kharadkar
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Smart and Innovative Trends in Next Generation Computing Technologies

Print ISBN: 978-981-10-8656-4

Electronic ISBN: 978-981-10-8657-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-8657-1_45