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Multistage Switched Adaptive Filtering Approach for Denoising Speech Signals of Parkinson’s Disease-affected Patients

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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.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Author notes

  1. S. Hannah Pauline, R. Kumar, R. Narayanamoorthi and K. W. Lai are contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Chennai, Tamil Nadu, 603203, India

    S. Hannah Pauline, Samiappan Dhanalakshmi & R. Kumar

  2. Department of Electronics and Electrical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Chennai, Tamil Nadu, 603203, India

    R. Narayanamoorthi

  3. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Khin Wee Lai

Authors
  1. S. Hannah Pauline
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  2. Samiappan Dhanalakshmi
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  3. R. Kumar
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  4. R. Narayanamoorthi
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  5. Khin Wee Lai
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Correspondence to Samiappan Dhanalakshmi.

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Appendix A

Appendix A

See Appendix Table 3.

Table 3 Comparison of MSE, SNR, ANR, PSNR, CC and MAE performance of the Proposed MS switched adaptive filter with the various existing filter models for speech signals
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Hannah Pauline, S., Dhanalakshmi, S., Kumar, R. et al. Multistage Switched Adaptive Filtering Approach for Denoising Speech Signals of Parkinson’s Disease-affected Patients. Circuits Syst Signal Process 42, 2259–2282 (2023). https://doi.org/10.1007/s00034-022-02211-3

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