Time-Frequency Analysis of Speech Signal Using Wavelet Synchrosqueezing Transform for Automatic Detection of Parkinson's Disease
Author:
Affiliation:
1. Department of Electronics Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7782634/10242612/10238777.pdf?arnumber=10238777
Reference24 articles.
1. Parkinson disease prediction using intrinsic mode function based features from speech signal
2. More about the basic assumptions of t-test: normality and sample size
3. End-to-end deep learning approach for Parkinson’s disease detection from speech signals
4. T test as a parametric statistic
5. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool
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1. BEiT Transformer Models to Aid in the Early Detection of Parkinson Illness;2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI);2024-05-09
2. “You don't sound well, you should take the day off”: Automatic detection of upper respiratory tract infections from speech using time-frequency domain deep convolutional neural network;Applied Acoustics;2024-04
3. Investigation of Scalograms with a Deep Feature Fusion Approach for Detection of Parkinson’s Disease;Cognitive Computation;2024-02-02
4. LSTM- and GRU-Based Common Cold Detection from Speech Signal;Advances in Intelligent Systems and Computing;2024
5. Fourier Model-Based Analysis of LP Residual for Diagnosing Parkinson’s Disease Using Speech;Advances in Intelligent Systems and Computing;2024
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