scholarly journals Adaptive Noise Cancellation System Using a Recursive Least Squares Filter

Respuestas ◽  
2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yesica Beltrán-Gómez ◽  
Jorge Gómez-Rojas ◽  
Rafael Linero-Ramos
Keyword(s):  
Least Squares ◽  
Initial Conditions ◽  
Low Cost ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Adaptive Noise Cancellation ◽  
Forgetting Factor ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Recursive Least Squares Filter

In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6  is reached, and with this it makes possible a low-cost implementation.

scholarly journals Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

2018 ◽  
Vol 160 ◽  
pp. 01001
Author(s):  
Chen Chen ◽  
Run Min ◽  
Qiaoling Tong ◽  
Shifei Tao ◽  
Dian Lyu ◽  
...  
Keyword(s):  
Least Squares ◽  
Low Cost ◽  
Sampling Rate ◽  
Boost Converter ◽  
Identification Accuracy ◽  
Recursive Least Squares ◽  
Control Technique ◽  
Forgetting Factor ◽  
Rls Algorithm ◽  
Variable Forgetting Factor

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations.

RLS Adaptive Noise Cancellation via QR Decomposition for Noisy ICA

2011 ◽  
Vol 403-408 ◽  
pp. 1291-1296
Author(s):  
Ming Liang Zhang ◽  
Shu Zhao Wang ◽  
Xin Yan Jia
Keyword(s):  
Least Squares ◽  
Test Performance ◽  
Least Squares Method ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Qr Decomposition ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Adaptive Noise Cancellation ◽  
Adaptive Noise

This study addresses the independent component analysis (ICA) in the presence of additive noise via an approach of adaptive filtering. Recursive least squares (RLS) adaptive noise cancellation via QR decomposition (QRRLS) is introduced to reduce the bias in the mixing matrix caused by noise. To test performance of this approach, two kinds of experiments for speech signals are conducted by combining Fast-ICA algorithm with it, on the conditions of identical noise and correlational noises respectively. Moreover, in order to measure the performance availably, the least-squares method is adopted to calculate the signal to noise ratio (SNR) of recovery signals. By comparison, it shows that this approach outperforms the adaptive noise cancellation via least-mean-squares (LMS) algorithm.

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

2013 ◽  
Vol 3 (4) ◽  
pp. 51-67
Author(s):  
Swati S. Godbole ◽  
Sanjay B. Pokle
Keyword(s):  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Noise Component ◽  
Wide Range ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Filtering Techniques

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

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