Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy

Improved speaker-independent emotion recognition from speech using two-stage feature reduction

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task.The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment

IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.

Compatibilizers Effect on Recycled Acrylonitrile Butadiene Rubber with Polypropylene and Sugarcane Bagasse Composite for Mechanical Properties

Compatibilizers effect on recycled acrylonitrile butadiene rubber (NBRr) with polypropylene (PP) and sugarcane bagasse (SCB) composite for mechanical properties is evaluated. Trans-Polyoctylene Rubber (TOR) and Bisphenol a Diglycidyl Ether (DGEBA) are used as compatibilizers in this study. Three (3) different composites (80/20/15, 60/40/15, and 40/60/15), with fixed filler (15 phr) and compatibilizers (10 phr) content, were carried out. These composites were arranged via melt mixing technique utilizing a heated two-roll mill at a temperature of 180°C for 9 minutes employing a 15-rpm rotor speed. Tensile and morphological properties were evaluated. The result shown average tensile strength dropped by 48.50% as the recycle NBR content rises 20 phr. Nevertheless, subsequent compatibilization reveals that the composites’ tensile properties were all greater than control composites. The morphology discovered validates the tensile properties, indicating a stronger interaction between the PP/SCB and recycle NBR composites with the addition of compatibilizer DGEBA