A novel 1-D densely connected feature selection convolutional neural network for heart sounds classification

Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract features from state-of-the-art speech emotional datasets. Subsequently, a correlation-based feature selection technique is applied to the extracted features to select the most appropriate and discriminative features for SER. For the classification of emotions, we utilize support vector machines, random forests, the k-nearest neighbors algorithm, and neural network classifiers. Experiments are performed for speaker-dependent and speaker-independent SER using four publicly available datasets: the Berlin Dataset of Emotional Speech (Emo-DB), Surrey Audio Visual Expressed Emotion (SAVEE), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and the Ryerson Audio Visual Dataset of Emotional Speech and Song (RAVDESS). Our proposed method achieves an accuracy of 95.10% for Emo-DB, 82.10% for SAVEE, 83.80% for IEMOCAP, and 81.30% for RAVDESS, for speaker-dependent SER experiments. Moreover, our method yields the best results for speaker-independent SER with existing handcrafted features-based SER approaches.

Download Full-text

Stock Price Direction Prediction Using Convolutional Neural Network : Emphasis on Correlation Feature Selection

Information Systems Review ◽

10.14329/isr.2020.22.4.021 ◽

2020 ◽

Vol 22 (4) ◽

pp. 21-39

Author(s):

Kyun Sun Eo ◽

◽

Kun Chang Lee

Keyword(s):

Neural Network ◽

Feature Selection ◽

Convolutional Neural Network ◽

Stock Price

Download Full-text

Effect on speech emotion classification of a feature selection approach using a convolutional neural network

PeerJ Computer Science ◽

10.7717/peerj-cs.766 ◽

2021 ◽

Vol 7 ◽

pp. e766

Author(s):

Ammar Amjad ◽

Lal Khan ◽

Hsien-Tsung Chang

Keyword(s):

Neural Network ◽

Feature Selection ◽

Convolutional Neural Network ◽

Feature Selection Method ◽

Classification Problem ◽

Speech Emotion Recognition ◽

Support Vector ◽

Emotion Classification ◽

K Nearest Neighbors ◽

Feature Selection Technique

Speech emotion recognition (SER) is a challenging issue because it is not clear which features are effective for classification. Emotionally related features are always extracted from speech signals for emotional classification. Handcrafted features are mainly used for emotional identification from audio signals. However, these features are not sufficient to correctly identify the emotional state of the speaker. The advantages of a deep convolutional neural network (DCNN) are investigated in the proposed work. A pretrained framework is used to extract the features from speech emotion databases. In this work, we adopt the feature selection (FS) approach to find the discriminative and most important features for SER. Many algorithms are used for the emotion classification problem. We use the random forest (RF), decision tree (DT), support vector machine (SVM), multilayer perceptron classifier (MLP), and k-nearest neighbors (KNN) to classify seven emotions. All experiments are performed by utilizing four different publicly accessible databases. Our method obtains accuracies of 92.02%, 88.77%, 93.61%, and 77.23% for Emo-DB, SAVEE, RAVDESS, and IEMOCAP, respectively, for speaker-dependent (SD) recognition with the feature selection method. Furthermore, compared to current handcrafted feature-based SER methods, the proposed method shows the best results for speaker-independent SER. For EMO-DB, all classifiers attain an accuracy of more than 80% with or without the feature selection technique.

Download Full-text

Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

10.21203/rs.2.20414/v2 ◽

2020 ◽

Author(s):

Chun Dong Xu ◽

Jing Zhou ◽

Dong Wen Ying ◽

Lei Jing Hou ◽

Qing Hua Long

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Heart Sound ◽

Gaussian Mixture ◽

Heart Sounds ◽

Spectral Amplitude ◽

Heart Sound Segmentation ◽

F Measure

Abstract Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

Download Full-text

Heart Sound Segmentation Based on Personalized Gaussian Mixture Model and Convolutional Neural Network

10.21203/rs.2.20414/v1 ◽

2020 ◽

Author(s):

Chun Dong Xu ◽

Jing Zhou ◽

Dong Wen Ying ◽

Lei Jing Hou ◽

Qing Hua Long

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Heart Sound ◽

Gaussian Mixture ◽

Heart Sounds ◽

Spectral Amplitude ◽

Heart Sound Segmentation ◽

F Measure

Abstract Background: Heart sound segmentation is a long-standing problem in heart analysis, and it is mainly caused by noise interference and diversification of heart sounds. Faced with the challenging of heart sound segmentation, a more applicable segmentation model was studied. Methods: In this process, the optimal modified Log-spectral amplitude and wavelet were used to suppress the noise in the heart sound, and used the duration-dependent hidden Markov model based on personalized Gaussian mixture model (PGMM-DHMM) to segment the fundamental heart sound (FHS) and the non-fundamental heart sound (non-FHS). Then used the optimized Mel frequency cepstral coefficient (MFCC) to realize the classification of S1 and S2 heart sound frames through the Convolutional neural network (CNN) classifier, which can avoid the errors caused by the ambiguity of the time domain features. Results: PGMM-DHMM can segment FHS more effectively, and the accuracy is 94.3%. The CNN classifier obtained the best results in the S1 and S2 classifications, the accuracy is 90.92%, the precision of S1 is 90.76%, the recall is 91.05%, the F-measure is 90.9%, and the precision of S2 is 91.07%, the recall is 90.79%, the F-measure is 90.93%. The final segmentation accuracy is 92.92%. In addition, the experimental results further indicate that CNN has more robust performance when classifying abnormal S1 and abnormal S2. Conclusions: The PGMM-DHMM model can better segment FHS and Non-FHS. The optimization of MFCC improves the classification effect of S1 and S2, and the improvement effect by the CNN classifier is significant, especially for abnormal heart sounds. The proposed algorithm is better than other algorithms at this stage.

Download Full-text

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Neural Computing and Applications ◽

10.1007/s00521-020-05362-z ◽

2020 ◽

Author(s):

Surbhi Vijh ◽

Prashant Gaurav ◽

Hari Mohan Pandey

Keyword(s):

Neural Network ◽

Feature Selection ◽

Particle Swarm Optimization ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

Lung Tumor ◽

Tumor Detection ◽

Whale Optimization Algorithm ◽

Swarm Optimization ◽

Whale Optimization

Abstract In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

Download Full-text