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3 articles for “Least Mean Square (LMS) algorithm.”
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Implementation of LMS Calibration Algorithm for 12-Bit Pipelined ADC
Abstract: Normal 0 false false false EN-IN X-NONE X-NONE As the technology is scaling, digital systems perform well with that but analog systems undergo degradation in their performance due to reduced gain. Thus, to improve their performance various calibration techniques are used. This paper implements one of such a digital calibration technique using digital signal processing, to compensate for the errors in 12-bit pipelined ADC with 1.5 bit/stage architecture at lower …
Published in Journal of VLSI Design Tools and Technology · Vol. 5, Issue 3, 2015 · pp. 104–113 Read article
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Development of System for Speech Enhancement using Combinational Adaptive LMS on Reconfigurable Platform
Abstract: Low cost and robustness makes the least mean square (LMS) algorithm most popular. Improved adaptive filter performance can be obtained by combinational approach. In this paper, for speech enhancement, we used one of such approaches known as ‘filter combination’. As our simulation results show, Fixed-Sign LMS algorithm overcome other algorithms in terms of SNR and mean square error. Keywords: Adaptive filter, speech enhancement, windows, convex combination
Published in Journal of VLSI Design Tools and Technology · Vol. 6, Issue 3, 2016 · pp. 28–34 Read article
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The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments
Abstract: The novel approach that this work discusses is a DCD-based iterative learning filter approach improved with deep learning methodologies, designed to improve the efficiency of acoustic echo cancellation. The proposed system can really manage both linear and nonlinear echo scenarios, dynamically adapting to fluctuating acoustic environments. The above comparative evaluations with standard filter, the standard RLS filter, indicate that the mean square error, and the standard deviation of the correlation …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 2, 2024 · pp. 9–24 Read article