Search

Detection and recovery of noise corrupted signals using adaptive filters are becoming popular due to their application in several fields, including communication and biomedical. Adaptive filters broadly utilize the LMS algorithm since it has low computational complexity and is robust in behavior. However, it fails to achieve a faster speed of convergence and minimum steady-state MSE simultaneously. Therefore, the objective is to estimate the noise-free signal faster with improved accuracy and obtain a lower steady-state MSE with a higher convergence speed. This paper introduces a multi-stage adaptive filtering model wherein the noisy signal is analyzed through a cascade of stages. The proposed signal de-noising scheme utilizes the automatic selection of stages to be cascaded such that the steady-state MSE is minimum. Further, to enhance convergence speed, the LMS adaptive filter's step-size is automatically adjusted at each stage. Hence, by controlling the number of stages to be cascaded automatically and utilizing a different step-size for each stage, we obtain a high convergence speed and minimum steady-state MSE. The proposed filter structure is tested for signal de-noising application to assess its performance concerning MSE, Signal-to-Noise Ratio (SNR), Average Noise Reduction (ANR), and convergence speed. The results attained have shown that the proposed filter structure provides remarkable performance improvement at different input noise levels. Further, the proposed Improved Variable-Stage (IVS) cascaded adaptive filter model employs LMS adaptive algorithm, hence offering a cost-effective and straightforward hardware implementation of ANC. [ABSTRACT FROM AUTHOR]

• LMS algorithm which requires a minimum number of computations is used to build a low-cost and straightforward adaptive noise canceller for efficient denoising of PCG signals. • Two modules of adaptive noise canceller are used in a cascaded connection. • In module I a multistage series-connected adaptive filter configuration is used to estimate the noise-free signal in module I. • The number of stages and the step-size for each stage is adjusted automatically. • The estimate of the clean signal obtained from this configuration is used as the reference input signal in next stage adaptive filter extract the clean signal with minimum noise. Phonocardiogram (PCG) represents the recordings of various heart sounds. To diagnose the different ailments of the heart, it is required to analyze these PCG signals. However, recording PCG signals is challenging since it is prone to surrounding noise signals. Therefore, there is a need to denoise the PCG signal before being used for advanced processing. This paper proposes an Adaptive Noise Cancellers-based filter model for effectively denoising and recovering the PCG signal. This work introduces an optimum adaptive filter structure for estimating a noise-free signal with high accuracy using Least Mean Square (LMS) algorithm. A noisy signal is processed through multiple adaptive filter stages connected in series in the proposed work. Multiple stages are automatically added, and each stage filter's step size process is dynamically changed. The estimate of clean PCG signal approximated using this multistage cascaded adaptive filter architecture is subsequently used in the next module to recover the clean PCG signal with high accuracy. The proposed robust multistage adaptive filter is evaluated for denoising synthetic and experimental PCG signals corrupted by Gaussian and pink noise of various input Signal to noise (SNR) levels. The experimental data are taken from the physionet database (Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016). The results demonstrate that the robust multistage filter model performs remarkably well. Compared with various filter configurations, the proposed filter structure achieves an 8-50% reduction in MAE values and the 45–87% reduction in MSE values. Further, there is an improved SNR of 15–60%, ANR of 15–65%, and PSNR improvement by 7–25% comparatively. The correlation between the clean signal and its estimate obtained using the proposed filter model is more than 0.99. Using an LMS adaptive filter in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy. In the future, the suggested robust multistage adaptive filter model can be tested for real-time performance when improved convergence speed and accuracy are desired. [ABSTRACT FROM AUTHOR]