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.

scholarly journals Adaptive Noise Cancellation of an Analog Fiber Optic Link

2021 ◽  
Author(s):  
Mohammad Nazrul Islam
Keyword(s):  
Optical Fiber ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Optical Power ◽  
Noise Cancellation ◽  
Adaptive Noise Cancellation ◽  
Fiber Link ◽  
The Mean ◽  
Simulation Results ◽  
Adaptive Noise

There are three dominant noise mechanisms in an analog optical fiber link. These are shot noise that is proportional to the mean optical power, relative intensity noise (RIN) that is proportional to the square of the instanteaneous optical power. This report describes an adaptive noise cancellation of these dominant noise processes that persist an analog optical fiber link. The performance of an analog optical fiber link is analyzed by taking the effects of these noise processes. Analytical and simulation results show that some improvement in signal to noise ratio (SNR) and this filter is effective to remove noise adaptively from the optical fiber link.

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

2014 ◽  
Vol 886 ◽  
pp. 390-393
Author(s):  
Jing Mo ◽  
Wei He ◽  
Dan Su ◽  
Jing Wei Wu
Keyword(s):  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Filter Performance ◽  
Multi Level ◽  
Adaptive Noise ◽  
Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

The Adaptive Filtering Algorithms for Engine’s Noise Cancellation in Speech Signals

2011 ◽  
Vol 130-134 ◽  
pp. 1323-1326
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
De You Fu ◽  
Hai Shen Zhou
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Speech Signals ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Signal Noise ◽  
Filtering Algorithm ◽  
Adaptive Noise

The presence of noise superimposed on a signal limits the receiver’s ability to correctly identify the intended signal. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper describes the Least Mean Squares (LMS) adaptive filtering algorithm. The algorithm was implemented in Matlab and was tested for noise cancellation in speech signals.

The Design of Adaptive Noise Cancellation Filter Based on DSP Chip

2014 ◽  
Vol 971-973 ◽  
pp. 1786-1790
Author(s):  
Xiu Min Wang ◽  
Ting Ting Li ◽  
Liang Shan
Keyword(s):  
Digital Filters ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Speech Communication ◽  
Adaptive Noise Cancellation ◽  
Change Characteristics ◽  
The Common ◽  
Adaptive Noise

The speech signal usually could not be extracted correctly from the digital speech communication system with strong interference. As for this kind of system, the common fixed coefficient digital filters (FIR, IIR) are unable to achieve the best effect of filtering. Whereas the adaptive filter could extract the available signals properly by adjusting the filter coefficient automatically without knowing the change characteristics of the noise signal. In this paper, we designed an adaptive noise cancellation filter based on LMS algorithm on the DSP chip and verification of the filter was done on the TMS320C5509 platform. The results show that the adaptive noise cancellation designed in this paper could extract the available signals properly and improve the quality of the speech communication.

scholarly journals Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Noman Q. Al-Naggar ◽  
Mohammed H. Al-Udyni
Keyword(s):  
Heart Sound ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Mean Square ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Step Size ◽  
Nlms Algorithm ◽  
Noise Ratio ◽  
Adaptive Noise

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length Lj parameter, which is determined in 2n sequence of 2, 4, 8, 16, … , 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer.

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.

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