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.

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.

scholarly journals Objective and Subjective Evaluations of Adaptive Noise Cancellation Systems with Selectable Algorithms for Speech Intelligibility

2018 ◽  
Vol 7 (4) ◽  
pp. 570-579
Author(s):  
Roshahliza M. Ramil ◽  
Salina Abdul Samad ◽  
Ali O. Abid Noor
Keyword(s):  
Speech Intelligibility ◽  
Signal To Noise Ratio ◽  
Subjective Evaluation ◽  
Noise Cancellation ◽  
Adaptive Noise Cancellation ◽  
Adaptation Algorithm ◽  
Listening Tests ◽  
Single Input ◽  
The Mean ◽  
Adaptive Noise

Adaptive Noise Cancellation (ANC) systems with selectable algorithms refer to ANC systems that are able to change the adaptation algorithm based on the eigenvalue spread of the noise. These systems can have dual inputs based on the conventional ANC structure or a single input based on the Adaptive Line Enhancer (ALE) structure. This paper presents a comparison of the performance of these two systems using objective and subjective measurements for speech intelligibility. The parameters used to objectively compare the systems are the Mean Square Error (MSE) and the output Signal to Noise Ratio (SNR). For subjective evaluation, listening tests were evaluated using the Mean Opinion Score (MOS) technique. The outcomes demonstrate that for both objective and subjection evaluations, the single input ALE with selectable algorithms (S-ALE) system outperforms that of the dual input ANC with selectable algorithm (S-ANC) in terms of better steady-state MSE by 10%, higher SNR values for most types of noise, higher scores in most of the questions in the MOS questionnaire and a higher acceptance rate for speech quality.

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.

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.

Speech Signal Denoise Based on LMS Algorithm in Simulink

2011 ◽  
Vol 225-226 ◽  
pp. 453-456 ◽  
Author(s):  
Yan Ping He
Keyword(s):  
Prior Knowledge ◽  
Adaptive Filter ◽  
Speech Signal ◽  
Noise Cancellation ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Signal Simulation ◽  
Simulation Results ◽  
Adaptive Noise

The adaptive noise cancellation system by LMS algorithm need not to know the prior knowledge of input speech signal and noise, and can carry out denoise. In this paper, we present a general approach to using Simulink to build adaptive filter which may denoise for noise added speech signal. Simulation results show that this method has the good suppression ability for the noise of collection speech signal.

The Design and Implementation of Adaptive Noise Cancellation for Magnetic Flux Leakage Test Signal

2013 ◽  
Vol 441 ◽  
pp. 393-396
Author(s):  
Xiao Gang Han ◽  
Mei Quan Liu ◽  
Qin Lei Sun
Keyword(s):  
Magnetic Flux ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Magnetic Flux Leakage ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Leakage Test ◽  
Adaptive Noise ◽  
Flux Leakage

In the application of magnetic flux leakage (MFL) nondestructive testing, the signal will be disturbed by varying noises. It seriously affects the accuracy of detection judgment result. This paper describes the design of least mean square (LMS) noise cancellation for MFL signal and the implementation based on ARM platform. Two giant magnetoresistive sensors are used to measure the signal, one sensor for MFL signal (with noise) and the other one for the noise signal. They are inputted to the noise cancellation to obtain pure MFL signals. Experimental results show that the LMS noise cancellation significantly improves the signal to noise ratio.

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.

Research and Simulation on Adaptive Noise Cancellation System Based on BP Neural Network

2012 ◽  
Vol 580 ◽  
pp. 110-113
Author(s):  
Yan Ping He ◽  
Hai Dong Zhang ◽  
Ke Yan Deng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Nonlinear Mapping ◽  
Adaptive Noise Cancellation ◽  
Source Characteristics ◽  
Simulation Results ◽  
Adaptive Noise

Adaptive noise cancellation system can effectively eliminate interference, and has the strong nonlinear mapping capability and less calculation on the conditions of unknown outside interference source characteristics. Firstly, we research adaptive noise cancellation system principle and structure, and construct an adaptive noise cancellation system based on BP neural network combining with characteristics of BP neural network. Then we dynamically simulate the system using Simulink. The simulation results show that the model can effectively offset the noise signal of noise added signal.

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