Noise reduction in speech signal of Parkinson’s Disease (PD) patients using optimal variable stage cascaded adaptive filter configuration

Pauline, S. Hannah; Dhanalakshmi, Samiappan; Kumar, Ritesh; Narayanamoorthi, R.

doi:10.1016/j.bspc.2022.103802

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…For example, Jaiswal et al [63] suggested a concealed Wiener filter-based technique for voice augmentation to improve the common spectral subtraction algorithm. Pauline and Dhanalakshmi [64] presented an efficient adaptive filter structure for noise reduction in voice signals that utilized the least mean square (LMS) and normalized LMS algorithms. They evaluated the proposed filter model on both normal speech signals and speech signals from Parkinson's disease patients.…”

Section: Algorithms For Alaryngeal Speech Enhancementmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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Section: Algorithms For Alaryngeal Speech Enhancementmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease

Cantürk,

Günay

2024

Cogn Comput

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Parkinson’s disease (PD) is a neurological condition that millions of people worldwide suffer from. Early symptoms include a slight sense of weakness and a propensity for involuntary tremulous motion in body limbs, particularly in the arms, hands, and head. PD is diagnosed based on motor symptoms. Additionally, scholars have proposed various remote monitoring tests that offer benefits such as early diagnosis, ease of application, and cost-effectiveness. PD patients often exhibit voice disorders. Speech signals of the patients can be used for early diagnosis of the disease. This study proposed an artificial intelligence–based approach for PD diagnosis using speech signals. Scalogram images, generated through the Continuous Wavelet Transform of the speech signals, were employed in deep learning techniques to detect PD. The scalograms were tested with various deep learning techniques. In the first part of the experiment, AlexNet, GoogleNet, ResNet50, and a majority voting-based hybrid system were used as classifiers. Secondly, a deep feature fusion method based on DenseNet and NasNet was investigated. Several evaluation metrics were employed to assess the performance. The deep feature fusion system achieved an accuracy of 0.95 and an F1 score with stratified 10-fold cross-validation, improving accuracy by 38% over the ablation study. The key contributions of this study include the investigation of scalogram images with a comprehensive analysis of deep learning models and deep feature fusion for PD detection.

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A Comprehensive Investigation into The Noise Reduction Techniques for Speech

Tyagi

Várkonyi-Kóczy

Szénási

2023

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Noise reduction in speech signal of Parkinson’s Disease (PD) patients using optimal variable stage cascaded adaptive filter configuration

Cited by 4 publications

References 21 publications

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease

A Comprehensive Investigation into The Noise Reduction Techniques for Speech

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