One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks

2017 25th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco.2017.8081222 ◽

2017 ◽

Cited By ~ 4

Author(s):

Johan Brynolfsson ◽

Maria Sandsten

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

One Dimensional ◽

Stationary Signals

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Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

Entropy ◽

10.3390/e22060688 ◽

2020 ◽

Vol 22 (6) ◽

pp. 688

Author(s):

Adrián Vázquez-Romero ◽

Ascensión Gallardo-Antolín

Keyword(s):

Neural Networks ◽

Support Vector Machines ◽

Convolutional Neural Networks ◽

Support Vector ◽

Final Decision ◽

Experimental Protocol ◽

Ensemble Averaging ◽

One Dimensional ◽

Vector Machines ◽

Detection Of Depression

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.

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