Performance analysis and enhancements of adaptive algorithms and their applications

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.

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“…NLMS algorithm is an indication of the minimal disturbance among iterations [ 13 , 14 ]. Table 2 summarizes the NLMS algorithm.…”

Section: Methodsmentioning

confidence: 99%

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

Al-Naggar

Al-Udyni

2018

Journal of Healthcare Engineering

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“…Hence, convergence can be slow and require high computational complexity. Adaptive filters are not guaranteed to be numerically stable, which limits their use in practice [21]. Adaptive filtering operates on a single signal stream and does not guarantee convergence to a local minimum that would maximize the SNR.…”

Section: Literature Reviewmentioning

confidence: 99%

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

et al. 2020

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A continuous condition monitoring system to detect and localize railroad track irregularities is achievable with inertial sensors onboard revenue service trains. However, the inaccurate geospatial position estimates of GPS receivers and the non-uniform sampling of inertial sensors adds noise and reduces signal strength. Consequently, the signal-to-noise ratio decreases, which leads to higher rates of false positives and false negatives. Appropriate signal filtering, alignment, and combination from multiple traversals can enhance the signal-to-noise ratio. However, it is not straightforward to determine the best cut-off frequency for the filter. This paper introduces a method that is suitable for any signal filtering approach. The frequency window of the resultant energy and variance of ensemble averaged FFTs informs the best cut-off frequency. The results affirm that a lowpass finite impulse response filter with the selected cutoff frequency progressively increases the signal-to-noise ratio with increasing filter order, thus demonstrating the effectiveness and practicality of the method.

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Secure Voice Interactions With Smart Devices

Zhu

Xiao

Jiang

et al. 2023

IEEE Trans. on Mobile Comput.

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Performance analysis and enhancements of adaptive algorithms and their applications

Cited by 3 publications

References 100 publications

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

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

Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization

Secure Voice Interactions With Smart Devices

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