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.

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.

ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

2012 ◽  
pp. 199-205
Author(s):  
Alka Gautam ◽  
Hoon-Jae Lee ◽  
Wan-Young Chung
Keyword(s):  
Signal To Noise Ratio ◽  
Random Noise ◽  
Noise Cancellation ◽  
Peak Detection ◽  
Lms Algorithm ◽  
Ecg Signal ◽  
Maximum Point ◽  
Signal To Noise ◽  
Filtering Algorithm ◽  
Overall Performance

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

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.

NOVEL ADAPTIVE FILTERING TECHNIQUE FOR THE PROCESSING OF ELECTROGASTROGRAM

2002 ◽  
Vol 02 (02) ◽  
pp. L71-L78
Author(s):  
S. SELVAN
Keyword(s):  
Real Time ◽  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Signal Enhancement ◽  
Adaptive Noise Cancellation ◽  
Real Time Processing ◽  
Time Processing ◽  
Filtering Technique ◽  
Adaptive Noise ◽  
Adaptive Signal

The electrogastrogram measured cutaneously by attaching electrodes to the abdominal skin contains considerable noise. An attempt is made to develop an efficient adaptive filtering technique suitable for real time processing of electrogastrogram. A novel technique combining both adaptive noise cancellation and adaptive signal enhancement in a single recurrent neural network is proposed. To compare its performance, adaptive noise cancellation and cascaded connection of adaptive noise cancellation and adaptive signal enhancement are employed. Recurrent neural networks using Real Time Recurrent Learning (RTRL) algorithm are employed for implementing the all the above systems. An attempt is made to alleviate the computational burden imposed by RTRL algorithm by employing pruning of weights.

Sign in / Sign up

Export Citation Format

Share Document