Detecting protagonists and antagonists in the voice quality of American cartoon characters: a quantitative LTAS-based analysis

Phonetica ◽  
2021 ◽  
Vol 78 (4) ◽  
pp. 345-384
Author(s):  
Ke Hui Tong ◽  
Scott Reid Moisik
Keyword(s):  
A Priori ◽  
Voice Quality ◽  
Principal Component ◽  
Acoustic Properties ◽  
Support Vector ◽  
Cartoon Character ◽  
Quantitative Examination ◽  
The Difference ◽  
Cartoon Characters ◽  
Dominance Pattern

Abstract The voices of heroes and villains in cartoons contribute to their uniqueness and helps shape how we perceive them. However, not much research has looked at the acoustic properties of character voices and the possible contributions these have to cartoon character archetypes. We present a quantitative examination of how voice quality distinguishes between characters based on their alignment as either protagonists or antagonists, performing Principal Component Analysis (PCA) on the Long-term Average Spectra (LTAS) of concatenated passages of the speech of various characters obtained from four different animated cartoons. We then assessed if the categories of “protagonists” and “antagonists” (determined via an a priori classification) could be distinguished using a classification algorithm, and if so, what acoustic characteristics could help distinguish the two categories. The overall results support the idea that protagonists and antagonists can be distinguished by their voice qualities. Support Vector Machine (SVM) analysis yielded an average classification accuracy of 96% across the cartoons. Visualisation of the spectral traits constituting the difference did not yield consistent results but reveals a low-versus-high frequency energy dominance pattern segregating antagonists and protagonists. Future studies can look into how other variables might be confounded with voice quality in distinguishing between these categories.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals Discrimination between Alzheimer's Disease and Mild Cognitive Impairment Using SOM and PSO-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Shih-Ting Yang ◽  
Jiann-Der Lee ◽  
Tzyh-Chyang Chang ◽  
Chung-Hsien Huang ◽  
Jiun-Jie Wang ◽  
...  
Keyword(s):  
A Priori ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Patient Classification ◽  
Self Organizing Map ◽  
Multiple Features ◽  
Classification Framework ◽  
Volumetric Features ◽  
Processing Steps

In this study, an MRI-based classification framework was proposed to distinguish the patients with AD and MCI from normal participants by using multiple features and different classifiers. First, we extracted features (volume and shape) from MRI data by using a series of image processing steps. Subsequently, we applied principal component analysis (PCA) to convert a set of features of possibly correlated variables into a smaller set of values of linearly uncorrelated variables, decreasing the dimensions of feature space. Finally, we developed a novel data mining framework in combination with support vector machine (SVM) and particle swarm optimization (PSO) for the AD/MCI classification. In order to compare the hybrid method with traditional classifier, two kinds of classifiers, that is, SVM and a self-organizing map (SOM), were trained for patient classification. With the proposed framework, the classification accuracy is improved up to 82.35% and 77.78% in patients with AD and MCI. The result achieved up to 94.12% and 88.89% in AD and MCI by combining the volumetric features and shape features and using PCA. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

scholarly journals A Detailed Analysis on Kidney and Heart Disease Prediction using Machine Learning

2021 ◽  
pp. 9-14
Author(s):  
Claire Salkar
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Kidney Disease ◽  
Principal Component ◽  
Support Vector ◽  
Computationally Efficient ◽  
Optimal Method ◽  
The Difference

Detection of disease at earlier stages is the most challenging one. Datasets of different diseases are available online with different number of features corresponding to a particular disease. Many dimensionalities reduction and feature extraction techniques are used nowadays to reduce the number of features in dataset and finding the most appropriate ones. This paper explores the difference in performance of different machine learning models using Principal Component Analysis dimensionality reduction technique on the datasets of Chronic kidney disease and Cardiovascular disease. Further, the authors apply Logistic Regression, K Nearest Neighbour, Naïve Bayes, Support Vector Machine and Random Forest Model on the datasets and compare the performance of the model with and without PCA. A key challenge in the field of data mining and machine learning is building accurate and computationally efficient classifiers for medical applications. With an accuracy of 100% in chronic kidney disease and 85% for heart disease, KNN classifier and logistic regression were revealed to be the most optimal method of predictions for kidney and heart disease respectively.

scholarly journals Powerful processing to three-dimensional facial recognition using triple information

2020 ◽  
Vol 9 (4) ◽  
pp. 326
Author(s):  
Mohammad Karimi Moridani ◽  
Ahad Karimi Moridani ◽  
Mahin Gholipour
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Principal Component ◽  
Support Vector ◽  
The Face ◽  
Principal Component Analysis Algorithm ◽  
The Difference ◽  
Security Surveillance ◽  
The Trinity

<p><span>Face Detection plays a crucial role in identifying individuals and criminals in Security, surveillance, and footwork control systems. Face Recognition in the human is superb, and pictures can be easily identified even after years of separation. These abilities also apply to changes in a facial expression such as age, glasses, beard, or little change in the face. This method is based on 150 three-dimensional images using the Bosphorus database of a high range laser scanner in a Bogaziçi University in Turkey. This paper presents powerful processing for face recognition based on a combination of the salient information and features of the face, such as eyes and nose, for the detection of three-dimensional figures identified through analysis of surface curvature. The Trinity of the nose and two eyes were selected for applying principal component analysis algorithm and support vector machine to revealing and classification the difference between face and non-face. The results with different facial expressions and extracted from different angles have indicated the efficiency of our powerful processing.</span></p>

scholarly journals A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 259 ◽  
Author(s):  
Diana Toledo-Pérez ◽  
Miguel Martínez-Prado ◽  
Roberto Gómez-Loenzo ◽  
Wilfrido Paredes-García ◽  
Juvenal Rodríguez-Reséndiz
Keyword(s):  
Lower Limb ◽  
Classification Accuracy ◽  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Semg Signal ◽  
Zero Crossings ◽  
The Difference ◽  
The Right

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

scholarly journals Adaptive Multilevel Kernel Machine for Scene Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Junlin Hu ◽  
Liang Wang ◽  
Fuqing Duan ◽  
Ping Guo
Keyword(s):  
Principal Component ◽  
Support Vector ◽  
Scene Classification ◽  
Machine Method ◽  
Census Transform ◽  
Kernel Machine ◽  
Benchmark Datasets ◽  
The Difference ◽  
Optimal Kernel ◽  
Special Complex

Scene classification is a challenging problem in computer vision applications and can be used to model and analyze a special complex system, the internet community. The spatial PACT (Principal component Analysis of Census Transform histograms) is a promising representation for recognizing instances and categories of scenes. However, since the original spatial PACT only simply concatenates compact census transform histograms at all levels together, all levels have the same contribution, which ignores the difference among various levels. In order to ameliorate this point, we propose an adaptive multilevel kernel machine method for scene classification. Firstly, it computes a set of basic kernels at each level. Secondly, an effective adaptive weight learning scheme is employed to find the optimal weights for best fusing all these base kernels. Finally, support vector machine with the optimal kernel is used for scene classification. Experiments on two popular benchmark datasets demonstrate that the proposed adaptive multilevel kernel machine method outperforms the original spatial PACT. Moreover, the proposed method is simple and easy to implement.

Multivariate micropollutants analysis in marine waters

1995 ◽  
Vol 32 (9-10) ◽  
pp. 341-348
Author(s):  
V. Librando ◽  
G. Magazzù ◽  
A. Puglisi
Keyword(s):  
Multivariate Data Analysis ◽  
Partial Least Squares Regression ◽  
Environmental Chemistry ◽  
Sampling Site ◽  
Principal Component ◽  
Least Squares Regression ◽  
Organic Micropollutants ◽  
Suspended Material ◽  
The Difference

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a function of time. In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of organic micropollutants, particularly since some are known to be carcinogenic. As the number of measured parameters increases examining the data and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Multivariate Data Analysis (MVDA), Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR). The use of the first technique in this work has been applied to the interpretation of the quality of Augusta bay, by measuring the concentration of numerous organic micropollutants, together with the classical water pollution parameters, in different sites and at different times. The MVDA has highlighted the difference between various sampling sites whose data were initially thought to be similar. Furthermore, it has allowed a choice of more significant parameters for future monitoring and more suitable sampling site locations.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Distribution and Severity of Placental Lesions Caused by the Chlamydia abortus 1B Vaccine Strain in Vaccinated Ewes

Pathogens ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Sergio Gastón Caspe ◽  
Javier Palarea-Albaladejo ◽  
Clare Underwood ◽  
Morag Livingstone ◽  
Sean Ranjan Wattegedera ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
Principal Component ◽  
Placental Pathology ◽  
Wild Type ◽  
Cell Infiltrate ◽  
Chlamydia Abortus ◽  
Vaccine Type ◽  
The Difference ◽  
Enzootic Abortion Of Ewes

Chlamydia abortus infects livestock species worldwide and is the cause of enzootic abortion of ewes (EAE). In Europe, control of the disease is achieved using a live vaccine based on C. abortus 1B strain. Although the vaccine has been useful for controlling disease outbreaks, abortion events due to the vaccine have been reported. Recently, placental pathology resulting from a vaccine type strain (vt) infection has been reported and shown to be similar to that resulting from a natural wild-type (wt) infection. The aim of this study was to extend these observations by comparing the distribution and severity of the lesions, the composition of the predominating cell infiltrate, the amount of bacteria present and the role of the blood supply in infection. A novel system for grading the histological and pathological features present was developed and the resulting multi-parameter data were statistically transformed for exploration and visualisation through a tailored principal component analysis (PCA) to evaluate the difference between them. The analysis provided no evidence of meaningful differences between vt and wt strains in terms of the measured pathological parameters. The study also contributes a novel methodology for analysing the progression of infection in the placenta for other abortifacient pathogens.

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