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.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Sign error LMS; Sign sign LMS; MSE; Multi-stage switched; Signal de-noising |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 28 Nov 2023 07:52 |
| Last Modified: | 28 Nov 2023 07:52 |
| URI: | http://eprints.um.edu.my/id/eprint/39376 |
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