2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · 2020 · 15 citations · 35 references
EngineeringBiomedical EngineeringElectrical EngineeringBiomedical Signal ProcessingFeature EngineeringMachine LearningBiometric AuthenticationDigital Signal ProcessingElectromyogram AnalysisMuscle Signal ProcessingPattern RecognitionSupport Vector MachinesElectromyography BiometricsElectromyography Feature ExtractionEmpirical Mode DecompositionFrequency-domain FeaturesMuscle Signal Feature ExtractionSpeech Signal AnalysisSpeech Signal ProcessingTime-frequency Features
Electromyography (EMG) of speech is the recording of muscle action potentials in the neck region during the speech. This paper proposed a first of its kind method of a biometric system using EMG of a speech signal. Signals were denoised by using empirical mode decomposition (EMD). All the noise was removed leaving us with the regions of interest. Next, feature extraction was performed using a combination of time and frequency-based features. The classification was then performed using different classifiers like Support Vector Machines (SVM), K-nearest neighbor (KNN), Ensemble classifiers, and Decision Tree (DT). The results showed that the maximum accuracy of 95.3% was obtained by using the Quadratic SVM classifier for all 10 classes.
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Authentication gets personal with biometrics
Javier Ortega-García, Josef Bigün, D.A. Reynolds et al. · IEEE Signal Processing Magazine · 2004 · 152 citations · Full text