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Mel frequency cepstral coefficient and its applications: A review
Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …
A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical
implications. Early and accurate diagnosis of PD is crucial for timely intervention and …
implications. Early and accurate diagnosis of PD is crucial for timely intervention and …
The detection of Parkinson disease using the genetic algorithm and SVM classifier
The speech signal is like the black box of human beings where much information is hidden.
The treatment of this signal provides us with the speaker's identity. In a way, it is similar to an …
The treatment of this signal provides us with the speaker's identity. In a way, it is similar to an …
Detection of COVID-19 from speech signal using bio-inspired based cepstral features
The early detection of COVID-19 is a challenging task due to its deadly spreading nature
and existing fear in minds of people. Speech-based detection can be one of the safest tools …
and existing fear in minds of people. Speech-based detection can be one of the safest tools …
Detection of Parkinson's disease using automated tunable Q wavelet transform technique with EEG signals
Deep brain simulations play an important role to study physiological and neuronal behavior
during Parkinson's disease (PD). Electroencephalogram (EEG) signals may faithfully …
during Parkinson's disease (PD). Electroencephalogram (EEG) signals may faithfully …
Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features
We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet
Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting …
Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting …
[HTML][HTML] Machine learning methods with decision forests for Parkinson's detection
Biomedical engineers prefer decision forests over traditional decision trees to design state-
of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However …
of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However …
[PDF][PDF] An intelligent approach based on the combination of the discrete wavelet transform, delta delta MFCC for Parkinson's disease diagnosis
To diagnose Parkinson's disease (PD), it is necessary to monitor the progression of
symptoms. Unfortunately, diagnosis is often confirmed years after the onset of the disease …
symptoms. Unfortunately, diagnosis is often confirmed years after the onset of the disease …
[PDF][PDF] Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease
Among the several ways followed for detecting Parkinson's disease, there is the one based
on the speech signal, which is a symptom of this disease. In this paper focusing on the …
on the speech signal, which is a symptom of this disease. In this paper focusing on the …
A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier
Identifying and assessing Parkinson's disease in its early stages is critical to effectively
monitoring the disease's progression. Methodologies based on machine learning enhanced …
monitoring the disease's progression. Methodologies based on machine learning enhanced …