Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
Document Type
Article
Publication Date
4-1-2023
Abstract
Recording the speech signals of Parkinson's Disease (PD)-affected patients is challenging due to the surrounding noise. Therefore there is a need to denoise the signals. This paper proposes an Adaptive Noise Canceller-based model for signal denoising. This paper introduces an optimal adaptive filter structure using a signed LMS algorithm to compute the best estimate of a clean signal. A noise-corrupted signal is sent across multiple adaptive filters connected in series. Multiple stages are added automatically, and the filtering algorithm for each stage is also adjusted automatically. The proposed multi-stage switched adaptive filter model is tested for reducing the noise from a speech signal recorded from Parkinson's Disease-affected patients and corrupted by Gaussian signals of different input SNR levels. The simulation results prove that the proposed filter model performs remarkably well and provides 20-30 dB higher SNR values than the existing cascaded LMS filter models. The MSE value is improved by 85-97%, and the PSNR values are increased by 7 dB. Using the Sign LMS algorithm in the proposed filter model offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.
Keywords
Sign error LMS, Sign sign LMS, MSE, Multi-stage switched, Signal de-noising
Divisions
biomedengine
Funders
None
Publication Title
Circuits Systems and Signal Processing
Volume
42
Issue
4
Publisher
Springer Birkhauser
Publisher Location
233 SPRING STREET, 6TH FLOOR, NEW YORK, NY 10013 USA