Performance Of Adaptive Noise Canceller Research Articles

AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALE OPTIMIZATION ALGORITHM

In this paper, the Oppositional Whale Optimization Algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter’s noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels ([Formula: see text]10[Formula: see text]dB, [Formula: see text]15[Formula: see text]dB and [Formula: see text]20[Formula: see text]dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

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

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.

Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithms

In remote health care monitoring, the extraction of high resolution cardiac signals is an important task. For this the cardiac signal (CS) needed to be enhanced. Among various filtering techniques, the adaptive noise cancellation (ANC) is a promising methodology. In adaptive filtering least mean square (LMS) algorithm is the fundamental enhancement algorithm. However, it suffers with slow convergence and weight drift problem in non-stationery environment. In order to improve the performance of ANC this paper proposes to implement an ANC methodology based on variable step size on the normalization of fundamental LMS algorithm for CS enhancement. Based on such strategy this research implements ANC using hybrid algorithm called variable normalized LMS (VNLMS) algorithm. Further, to improve the convergence characteristics, to filter the ability and to minimize computational complexity some versions of VNLMS algorithms are implemented too. Finally, the proposed ANCs are tested using real CS obtained from MIT-BIH data base. The performance evaluation is carried based on signal to noise ratio improvement (SNRI) and excess mean square error (EMSE).

Performance Analysis of Adaptive Noise Canceller Employing NLMS Algorithm

In voice communication systems, noise cancellation using adaptive digital filter is a renowned technique for extracting desired speech signal through eliminating noise from the speech signal corrupted by noise. In this paper, the performance of adaptive noise canceller of Finite Impulse Response (FIR) type has been analysed employing NLMS (Normalized Least Mean Square) algorithm. An extensive study has been made to investigate the effects of different parameters, such as number of filter coefficients, number of samples, step size, and input noise level, on the performance of the adaptive noise cancelling system. All the results have been obtained using computer simulations built on MATLAB platform.

Application of adaptive noise canceller based on fixed-point algorithm for real-time somatosensory evoked potential monitoring

Objective To efficiently detect somsatosensory evoked potential (SEP) using field programmable gate array (FPGA) real-time system, fixed-point algorithm based adaptive noise canceller (ANC) was designed to improve signal to noise ratio (SNR). Methods With the optimization of important parameters that influence the performance of fixed-point algorithm ANC, the performance was compared to that of floating-point algorithm ANC which was isolated from the effect of quantization error. Results In the simulation study, the outputs of fixed-point-based ANC showed a little higher distortion from real SEP signals than that of floating-point algorithm ANC. In the optimal selection of μ value, fixed-point algorithm ANC could get as good results as floating-point algorithm. Conclusion With appropriate parameter values, fixed-point algorithm ANC is able to improve SNR of SEP as well as that of fixed-point algorithm ANC. Key words: Somsatosensory evoked potential; Fixed-point algorithm; Adaptive noise canceller; Step-size parameter, Distortion index

A Hybrid Subband Adaptive System for Speech Enhancement in Diffuse Noise Fields

Performance of adaptive noise cancellation (ANC) degrades severely when uncorrelated noise components are present at the two inputs. Thus, practical background diffuse noises pose a serious problem for ANC systems. In this letter, we propose a new hybrid system that integrates subband adaptive filters (SAFs) and a Wiener filter. The hybrid system is implemented on an oversampled DFT filterbank that efficiently integrates the SAF and the Wiener filter components in the frequency-domain. Performance evaluation of the hybrid system in presence of diffuse noise interference shows that the proposed system is superior to both the Wiener filter and the SAF subsystems.

The performance of adaptive noise cancellation systems in reverberant rooms

The performance of adaptive noise cancellation (ANC) systems in a reverberant room is investigated. Room reverberation effects are modeled through the method of images, incorporating both direct signal and reverberation effects into the room impulse response. The performance of ANC for broadband inputs is examined as a function of reverberation time, noise source location, and the geometry of the room and measurement equipment. Expressions are obtained relating the steady-state minimum mean-squared error and excess mean-squared error to the reverberation time and filter parameters. Nonuniform convergence effects are also investigated and bounds on the eigenvalue disparity are obtained for a given configuration. It is shown that the eigenvalue disparity increases as both the filter length and the distance between noise source and reference receiver increase, approaching a limiting value as the distance from source to reference receiver surpasses the acoustic critical distance.

Crosstalk resistant adaptive noise cancellation

Presence of signal components in the reference input is detrimental to the performance of practical adaptive noise cancellation systems. Using a modeling approach, we analyse the performance of adaptive noise cancellation in the presence of signal cross talk. We demonstrate a crosstalk resistant adaptive noise cancellation method. After showing that the original signal cannot be recovered if the ability to prevent adaptation does not exist, we discuss the use of a weighted exact least squares lattice algorithm in the joint estimation form, where adaptation can be controlled. Using stimulated data, it is shown that signal can be estimated with good accuracy, even when there is significant signal crosstalk in the reference input.

Speech enhancement for mobile telephony

The properties of noise fields in automobile interiors are discussed with a view toward speech enhancement for voice-activated mobile telephony. The limitations on performance of adaptive noise cancellation are explained in the context of the spatial correlation properties of the noise field. A simple delay-equalized near-field array of directional microphones is analyzed and found to be effective for increasing the signal-to-noise density ratio (SNR) and reducing the reverberant distortion of the speech, without introducing any further distortion. An array of N microphones, each with a delay and weight chosen according to its distance from the speech source, is a viable solution. Such an array gives gains on the order of N over the speech band, reduces reverberation, and does not introduce waveform distortion. Experimental results confirming the predicted performance are presented. >