On the performance of an optimized NLMS algorithm

Paleologu, Constantin; Ciochină, Silviu; Caramalau, Razvan; Benesty, Jacob

doi:10.1109/ecai.2015.7301212

Cited by 1 publication

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NLMS algorithm is an improved algorithm derived from the LMS algorithm by introducing the concept of normalization. By normalizing the input signal, the algorithm's robustness is improved, allowing NLMS algorithm to adapt to different signal statistical characteristics and signal-to-noise ratios [5]. The core idea of the NLMS algorithm is to gradually adjust the filter's weights to minimize the mean square error between the input signal and the desired output.…”

Section: Nlms Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

The improved variable step size least mean squares algorithm for speech enhancement

Kecheng,

Hao,

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

View full text Add to dashboard Cite

This paper proposes a new variable step-size adaptive filtering algorithm based on the LMS algorithm and compares its performance with Least Mean Squares(LMS), Normalized Least Mean Squares(NLMS), Modified Sigmoid-LMS(MLMS) and Regularized NLMS(RNLMS) algorithms. The results indicate that compared to the other comparative algorithms, this algorithm can more effectively improve the signal-to-noise ratio of the filter output. Furthermore, within a wide input signal-to-noise ratio range, this algorithm can consistently enhance the filter signal-to-noise ratio output, effectively addressing the problem of inconsistent filtering effects caused by the input signal-to-noise ratio sensitivity in algorithms such as NLMS algorithms.

show abstract

Section: Nlms Algorithmmentioning

confidence: 99%

“…It is a manifestation of the principle of minimum disturbance. Its principle can be described as follows: from one adaptive cycle to the next, the weight vector of the adaptive filter should change in the smallest possible way, while being constrained by the updated filter's output [6] [7].…”

Section: Nlms Algorithmmentioning

confidence: 99%

The improved variable step size least mean squares algorithm for speech enhancement

Kecheng,

Hao,

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

View full text Add to dashboard Cite

show abstract

On the performance of an optimized NLMS algorithm

Cited by 1 publication

References 14 publications

The improved variable step size least mean squares algorithm for speech enhancement

The improved variable step size least mean squares algorithm for speech enhancement

Contact Info

Product

Resources

About