Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals

2013 ◽  
Vol 60 (5) ◽  
pp. 1401-1413 ◽  
Author(s):  
Ali Shahidi Zandi ◽  
R. Tafreshi ◽  
M. Javidan ◽  
G. A. Dumont
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Epileptic Seizures ◽  
Gaussian Mixture ◽  
Zero Crossing ◽  
Variational Bayesian ◽  
Scalp Eeg

scholarly journals Speech Activity Detection and its Evaluation in Speaker Diarization System

2017 ◽  
Vol 16 (1) ◽  
pp. 7567-7572
Author(s):  
Sukhvinder Kaur ◽  
J. S. Sohal
Keyword(s):  
Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Training Data ◽  
Speaker Diarization ◽  
Activity Detection ◽  
Operating Characteristics ◽  
Zero Crossing ◽  
Artificial Neural Network Ann

In speaker diarization, the speech/voice activity detection is performed to separate speech, non-speech and silent frames. Zero crossing rate and root mean square value of frames of audio clips has been used to select training data for silent, speech and nonspeech models. The trained models are used by two classifiers, Gaussian mixture model (GMM) and Artificial neural network (ANN), to classify the speech and non-speech frames of audio clip. The results of ANN and GMM classifier are compared by Receiver operating characteristics (ROC) curve and Detection ErrorTradeoff (DET) graph. It is concluded that neural network based SADcomparatively better than Gaussian mixture model based SAD.

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