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Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients

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 f...

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Main Authors: Pauline, S. Hannah, Dhanalakshmi, Samiappan, Kumar, R., Narayanamoorthi, R., Lai, Khin Wee
Format: Article
Published: Springer Birkhauser 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/39376/
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spelling my.um.eprints.393762023-11-28T07:52:01Z http://eprints.um.edu.my/39376/ Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients Pauline, S. Hannah Dhanalakshmi, Samiappan Kumar, R. Narayanamoorthi, R. Lai, Khin Wee TK Electrical engineering. Electronics Nuclear engineering 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. Springer Birkhauser 2023-04 Article PeerReviewed Pauline, S. Hannah and Dhanalakshmi, Samiappan and Kumar, R. and Narayanamoorthi, R. and Lai, Khin Wee (2023) Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients. Circuits Systems and Signal Processing, 42 (4). pp. 2259-2282. ISSN 0278-081X, DOI https://doi.org/10.1007/s00034-022-02211-3 <https://doi.org/10.1007/s00034-022-02211-3>. 10.1007/s00034-022-02211-3
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Pauline, S. Hannah
Dhanalakshmi, Samiappan
Kumar, R.
Narayanamoorthi, R.
Lai, Khin Wee
Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
description 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.
format Article
author Pauline, S. Hannah
Dhanalakshmi, Samiappan
Kumar, R.
Narayanamoorthi, R.
Lai, Khin Wee
author_facet Pauline, S. Hannah
Dhanalakshmi, Samiappan
Kumar, R.
Narayanamoorthi, R.
Lai, Khin Wee
author_sort Pauline, S. Hannah
title Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
title_short Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
title_full Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
title_fullStr Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
title_full_unstemmed Multistage switched adaptive filtering approach for denoising speech signals of Parkinson's Disease-affected patients
title_sort multistage switched adaptive filtering approach for denoising speech signals of parkinson's disease-affected patients
publisher Springer Birkhauser
publishDate 2023
url http://eprints.um.edu.my/39376/
_version_ 1783876687794012160
score 13.211869

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