High-Dimensional Time Series Feature Extraction for Low-Cost Machine Olfaction

2020 ◽  
pp. 1-1
Author(s):  
Pratistha Shakya ◽  
Eamonn Kennedy ◽  
Christopher Rose ◽  
Jacob K. Rosenstein
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Low Cost ◽  
High Dimensional ◽  
Machine Olfaction

Time Series Feature Extraction for Machine Olfaction

2019 ◽  
Author(s):  
Pratistha Shakya ◽  
Eamonn Kennedy ◽  
Christopher Rose ◽  
Jacob K. Rosenstein
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Machine Olfaction

Time series forecasting based on wavelet decomposition and feature extraction

2016 ◽  
Vol 28 (S1) ◽  
pp. 183-195 ◽  
Author(s):  
Tianhong Liu ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Kanjian Zhang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Time Series Forecasting

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

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