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Quantization Effects in the LMS Algorithm

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Adaptive Filtering
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Abstract

In this chapter, several aspects of the finite-wordlength effects in the LMS algorithm are discussed for the cases of implementations in fixed- and floating-point arithmetics [1, 2, 4].

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References

  1. S.T. Alexander, Transient weight misadjustment properties for the finite precision LMS algorithm. IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 1250–1258 (1987)

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  2. M. Andrews, R. Fitch, Finite wordlength arithmetic computational error effects on the LMS adaptive weights, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Process, 1977, pp. 628–631

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  3. A. Antoniou, Digital Signal Processing: Signals, Systems, and Filters (McGraw Hill, New York, 2005)

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  4. C. Caraiscos, B. Liu, A roundoff error analysis of the LMS adaptive algorithm. IEEE Trans. Acoust. Speech Signal Process. ASSP-32, 34–41 (1984)

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  5. P.S.R. Diniz, E.A.B. da Silva, S.L. Netto, Digital Signal Processing: System Analysis and Design, 2nd edn. (Cambridge University Press, Cambridge, 2010)

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  6. A.V. Oppenheim, R.W. Schaffer, Discrete-Time Signal Processing (Prentice Hall, Englewood Cliffs, 1989)

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  7. A.B. Spirad, D.L. Snyder, Quantization errors in floating-point arithmetic. IEEE Trans. Acoust. Speech Signal Process. ASSP-26, 456–464 (1983)

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Correspondence to Paulo S. R. Diniz .

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Diniz, P.S.R. (2013). Quantization Effects in the LMS Algorithm. In: Adaptive Filtering. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-4106-9_15

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  • DOI: https://doi.org/10.1007/978-1-4614-4106-9_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-4105-2

  • Online ISBN: 978-1-4614-4106-9

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