Search results for: speech emotion classification

Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: M. Lim Haslip

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This quantitative study explores the correlation between child gender and father-child interactions. The author analyzes data from videotaped interactions between African-American fathers and their boy or girl toddler to explain how African-American fathers and toddlers interact with each other and whether these interactions differ by child gender. The purpose of this study is to investigate the research question: 'How, if at all, do fathers’ speech and gestures differ when interacting with their two-year-old sons versus daughters during free play?' The objectives of this study are to describe how child gender impacts African-American fathers’ verbal communication, examine how fathers gesture and speak to their toddler by gender, and to guide interventions for low-income African-American families and their children in early language development. This study involves a sample of 41 low-income African-American fathers and their 24-month-old toddlers. The videotape data will be used to observe 10-minute father-child interactions during free play. This study uses the already transcribed and coded data provided by Dr. Meredith Rowe, who did her study on the impact of African-American fathers’ verbal input on their children’s language development. The Child Language Data Exchange System (CHILDES program), created to study conversational interactions, was used for transcription and coding of the videotape data. The findings focus on the quantity of speech, diversity of speech, complexity of speech, and the quantity of gesture to inform the vocabulary usage, number of spoken words, length of speech, and the number of object pointings observed during father-toddler interactions in a free play setting. This study will help intervention and prevention scientists understand early language development in the African-American population. It will contribute to knowledge of the role of African-American fathers’ interactions on their children’s language development. It will guide interventions for the early language development of African-American children.

Keywords: parental engagement, early language development, African-American families, quantity of speech, diversity of speech, complexity of speech and the quantity of gesture

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Anja Kurz, Marc Flynn, Tobias Good, Marco Caversaccio, Martin Kompis

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Bone Anchored Hearing Implants (BAHI) are routinely used in patients with conductive or mixed hearing loss, e.g. if conventional air conduction hearing aids cannot be used. New sound processors and new fitting software now allow the adjustment of parameters such as loudness compression ratios or maximum power output separately. Today it is unclear, how the choice of these parameters influences aided speech understanding in BAHI users. In this prospective experimental study, the effect of varying the compression ratio and lowering the maximum power output in a BAHI were investigated. Twelve experienced adult subjects with a mixed hearing loss participated in this study. Four different compression ratios (1.0; 1.3; 1.6; 2.0) were tested along with two different maximum power output settings, resulting in a total of eight different programs. Each participant tested each program during two weeks. A blinded Latin square design was used to minimize bias. For each of the eight programs, speech understanding in quiet and in noise was assessed. For speech in quiet, the Freiburg number test and the Freiburg monosyllabic word test at 50, 65, and 80 dB SPL were used. For speech in noise, the Oldenburg sentence test was administered. Speech understanding in quiet and in noise was improved significantly in the aided condition in any program, when compared to the unaided condition. However, no significant differences were found between any of the eight programs. In contrast, on a subjective level there was a significant preference for medium compression ratios of 1.3 to 1.6 and higher maximum power output.

Keywords: Bone Anchored Hearing Implant, baha, compression, maximum power output, speech understanding

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Authors: Jalel Khelil

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In order to improve reliability of Gas Turbine machine especially its generator equipment, a fault diagnosis system based on fuzzy approach is proposed. Three various methods namely K-NN (K-nearest neighbors), F-KNN (Fuzzy K-nearest neighbors) and FNM (Fuzzy nearest mean) are adopted to provide the measurement of relative strength of vibration defaults. Both applications consist of two major steps: Feature extraction and default classification. 09 statistical features are extracted from vibration signals. 03 different classes are used in this study which describes vibrations condition: Normal, unbalance defect, and misalignment defect. The use of the fuzzy approaches and the classification results are discussed. Results show that these approaches yield high successful rates of vibration default classification.

Keywords: fault diagnosis, fuzzy classification k-nearest neighbor, vibration

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Siqing Guan, Fumiyo Oshima, Eiji Shimizu, Nozomi Tomita, Toru Takahashi, Hiroaki Kumano

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Risk factors for social anxiety in autism spectrum conditions involve executive attention, emotion regulation, and thought regulation as processes of cognitive dysregulation. Social camouflaging behaviors as strategies used to mask and/or compensate for autism characteristics during social interactions in autism spectrum conditions have also been emphasized. However, the role of cognitive dysregulation and social camouflaging related to social anxiety in autism spectrum conditions has not been clarified. Whether these factors are specific to social anxiety in autism spectrum conditions or common to social anxiety independent of autism spectrum conditions needs to be clarified. Here, we explored risk factors specific to social anxiety in autism spectrum conditions and general risk factors for social anxiety independent of autism spectrum conditions. From the Japanese participants in early adulthood (age=18~39) of the online survey in Japan, those who exceeded the Japanese version Autism-Spectrum Quotient cutoff (33 points or more )were divided into the autism spectrum conditions group (ASC; N=255, mean age=32.08, SD age=5.16)and those who did not exceed the cutoff were divided into the non-autism spectrum conditions group (Non-ASC; N=255, mean age=31.70, SD age=5.09). Using the Japanese versions of the Social Phobia Scale, the Social Interaction Anxiety Scale, and the Short Fear of Negative Evaluation Scale, a composite score for social anxiety was calculated using a method of principal. We also measured emotional control difficulties using the Difficulties in Emotion Regulation Scale, executive attention using the Effortful Control Scale for Adults, rumination using the Rumination-Reflection Questionnaire, and worry using the Penn State Worry Questionnaire. This study was passed through the review of the Ethics Committee. No conflicts of interest. Multiple regression analysis with forced entry method was used to predict social anxiety in the ASC and non-ASC groups separately, based on executive attention, emotion dysregulation, worry, rumination, and social camouflage. In the ASC group, emotion dysregulation (β=.277, p<.001), worry (β=.162, p<.05), assimilation (β=.308, p<.001) and masking (β=.275, p<.001) were significant predictors of social anxiety (F (7,247) = 45.791, p <.001, R2=.565). In the non-ASC groups,emotion dysregulation (β=.171, p<.05), worry (β=.344，p <.001), assimilation (β=.366,p <.001) and executive attention (β=-.132,p <.05) were significant predictors of social anxiety (F (7,207) =47.333, p <.001, R2=.615).The findings suggest that masking was shown to be a risk factor for social anxiety specific to autism spectrum conditions, while emotion dysregulation, worry, and assimilation were shown to be common risk factors for social anxiety, regardless of autism spectrum conditions. In addition, executive attention is a risk factor for social anxiety without autism spectrum conditions.

Keywords: autism spectrum, cognitive control, social anxiety, social camouflaging

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Authors: Hankyu Lee, Seoung Bum Kim

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The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

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Authors: Lamyaa Gamal El-Deen Taha, Ashraf Sharawi

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Planet image is a new data source from planet lab. This research is concerned with the assessment of Planet image for land cover mapping. Two pixel based classifiers and one subpixel based classifier were compared. Firstly, rectification of Planet image was performed. Secondly, a comparison between minimum distance, maximum likelihood and neural network classifications for classification of Planet image was performed. Thirdly, the overall accuracy of classification and kappa coefficient were calculated. Results indicate that neural network classification is best followed by maximum likelihood classifier then minimum distance classification for land cover mapping.

Keywords: planet image, land cover mapping, rectification, neural network classification, multilayer perceptron, soft classifiers, hard classifiers

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Authors: Paramjit Kaur, Harish Kundra

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In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

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Authors: Vo Ngoc Phu, Phan Thi Tuoi

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We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.

Keywords: sentiment classification, sentiment orientation, valence shifters, contextual, valence shifters, term counting

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Authors: Ashutosh Mishra, Nikhil Goyal

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A 3D facial emotion recognition model based on deep learning is proposed in this paper. Two convolution layers and a pooling layer are employed in the deep learning architecture. After the convolution process, the pooling is finished. The probabilities for various classes of human faces are calculated using the sigmoid activation function. To verify the efficiency of deep learning-based systems, a set of faces. The Kaggle dataset is used to verify the accuracy of a deep learning-based face recognition model. The model's accuracy is about 65 percent, which is lower than that of other facial expression recognition techniques. Despite significant gains in representation precision due to the nonlinearity of profound image representations.

Keywords: facial recognition, computational intelligence, convolutional neural network, depth map

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Authors: Telma Fontao de Castro, Kylee Miller, Maria Xavier Araújo, Isabel Brandao, Sandra Torres

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Objective: There is a dearth of research investigating the long-term emotional functioning of individuals recovered from anorexia nervosa (AN). This 15-year longitudinal study aimed to examine whether difficulties in cognitive processing of emotions persisted after long-term AN recovery and its link to anxiety and depression. Method: Twenty-four females, who were tested longitudinally during their acute and recovered AN phases, and 24 healthy control (HC) women, were screened for anxiety, depression, alexithymia, and emotion regulation difficulties (ER; only assessed in recovery phase). Results: Anxiety, depression, and alexithymia levels decreased significantly with AN recovery. However, scores on anxiety and difficulty in identifying feelings (alexithymia factor) remained high when compared to the HC group. Scores on emotion regulation difficulties were also lower in HC group. The abovementioned differences between AN recovered group and HC group in difficulties in identifying and accepting feelings and lack of emotional clarity were no longer present when the effect of anxiety and depression was controlled. Conclusions: Findings suggest that emotional dysfunction tends to decrease in AN recovered phase. However, using an HC group as a reference, we conclude that several emotional difficulties are still increased after long-term AN recovery, in particular, limited access to emotion regulation strategies, and difficulty controlling impulses and engaging in goal-directed behavior, thus suggesting to be a trait vulnerability. In turn, competencies related to emotional clarity and acceptance of emotional responses seem to be state-dependent phenomena linked to anxiety and depression. In sum, managing emotions remains a challenge for individuals recovered from AN. Under this circumstance, maladaptive eating behavior can serve as an affect regulatory function, increasing the risk of relapse. Emotional education and stabilization of depressive and anxious symptomatology after recovery emerge as an important avenue to protect from long-term AN relapse.

Keywords: alexithymia, anorexia nervosa, emotion recognition, emotion regulation

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Authors: Setareh Shayanfar

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Given the increasing importance of peer relationships during adolescence, the present study aimed to examine peer interactions that facilitate or hinder adolescents’ regulation of negative emotions. Using the Enhanced Critical Incident Technique, 1-hour semi-structured interviews were conducted with 16 junior high school adolescents. Participants were asked to recall situations when they experienced strong negative emotions during the past school year, indicate the peer interactions that helped or hindered their emotion regulation, and identify prospective interactions with the potential to help regulate their emotions. Data analysis extracted 182 critical incidents, including 109 helping incidents, 45 hindering incidents, and 28 wish list items, which generated 10 categories nested within four overarching themes: Positive Personal Support included (a) supportive presence, (b) expressing concern, (c) empathizing, and (d) encouraging and cheering up; while Strategy Transmission included (e) sharing perspective, and (f) giving advice; Activated Support included (g) taking action, and (h) distracting; while Negative Personal Interactions included (i) withdrawing and (j) punishing. Implications for mental health and service providers, as well as recommendations for future research, are presented.

Keywords: adolescence, emotion regulation, enhanced critical incident technique, peers

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Authors: Julio Vega

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Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

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Authors: S. B. Rathna Kumar, Pranjali A Ujwane, Panchanan Mohanty

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The present study aimed to develop a battery for assessing speech recognition performance by adults in Marathi. A total of four word lists were developed by considering word frequency, word familiarity, words in common use, and phonemic balance. Each word list consists of 25 words (15 monosyllabic words in CVC structure and 10 monosyllabic words in CVCV structure). Equivalence analysis and performance-intensity function testing was carried using the four word lists on a total of 150 native speakers of Marathi belonging to different regions of Maharashtra (Vidarbha, Marathwada, Khandesh and Northern Maharashtra, Pune, and Konkan). The subjects were further equally divided into five groups based on above mentioned regions. It was found that there was no significant difference (p > 0.05) in the speech recognition performance between groups for each word list and between word lists for each group. Hence, the four word lists developed were equally difficult for all the groups and can be used interchangeably. The performance-intensity (PI) function curve showed semi-linear function, and the groups’ mean slope of the linear portions of the curve indicated an average linear slope of 4.64%, 4.73%, 4.68%, and 4.85% increase in word recognition score per dB for list 1, list 2, list 3 and list 4 respectively. Although, there is no data available on speech recognition tests for adults in Marathi, most of the findings of the study are in line with the findings of research reports on other languages. The four word lists, thus developed, were found to have sufficient reliability and validity in assessing speech recognition performance by adults in Marathi.

Keywords: speech recognition performance, phonemic balance, equivalence analysis, performance-intensity function testing, reliability, validity

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Authors: Faisal Alshuwaier, Ali Areshey

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Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts.

Keywords: extraction, max-prod, fuzzy relations, text mining, memberships, classification, memberships, classification

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Authors: Khaled A. Abdel-Sater

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There are four areas of the temporal lobe. Primary auditory area (areas 41 and 42); it is for the perception of auditory impulse, auditory association area (area 22, 21, and 20): Areas 21 and 20 are for understanding and interpretation of auditory sensation, recognition of language, and long-term memories. Area 22, also called Wernicke’s area, and a sensory speech centre. It is for interpretation of auditory and visual information, formation of thoughts in the mind, and choice of words to be used. Ideas and thoughts originate in it. The limbic system is a part of cortical and subcortical structure forming a ring around the brainstem. Cortical structures are the orbitofrontal area, subcallosal gyrus, cingulate gyrus, parahippocampal gyrus, and uncus. Subcortical structures are the hypothalamus, hippocampus, amygdala, septum, paraolfactory area, anterior nucleus of the thalamus portions of the basal ganglia. There are several physiological functions of the limbic system, including regulation of behavior, motivation, and emotion.

Keywords: limbic system, motivation, emotions, temporal lobe

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Authors: Deepthy Ann Joy, N. Sreedevi

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Hearing impairment (HI) at an early age, identified before the onset of language development can reduce the negative effect on speech and language development of children. Early rehabilitation is very important in the improvement of speech production in children with HI. Other than conventional hearing aids, Cochlear Implants are being used in the rehabilitation of children with HI. However, delay in acquisition of speech and language milestones persist in children with Cochlear Implant (CI). Delay in speech milestones are reflected through speech sound errors. These errors reflect the temporal and spectral characteristics of speech. Hence, acoustical analysis of the speech sounds will provide a better representation of speech production skills in children with CI. The present study aimed at investigating the acoustic characteristics of vowels in Malayalam speaking children with a cochlear implant. The participants of the study consisted of 20 Malayalam speaking children in the age range of four and seven years. The experimental group consisted of 10 children with CI, and the control group consisted of 10 typically developing children. Acoustic analysis was carried out for 5 short (/a/, /i/, /u/, /e/, /o/) and 5 long vowels (/a:/, /i:/, /u:/, /e:/, /o:/) in word-initial position. The responses were recorded and analyzed for acoustic parameters such as Vowel duration, Ratio of the duration of a short and long vowel, Formant frequencies (F₁ and F₂) and Formant Centralization Ratio (FCR) computed using the formula (F₂u+F₂a+F₁i+F₁u)/(F₂i+F₁a). Findings of the present study indicated that the values for vowel duration were higher in experimental group compared to the control group for all the vowels except for /u/. Ratio of duration of short and long vowel was also found to be higher in experimental group compared to control group except for /i/. Further F₁ for all vowels was found to be higher in experimental group with variability noticed in F₂ values. FCR was found be higher in experimental group, indicating vowel centralization. Further, the results of independent t-test revealed no significant difference across the parameters in both the groups. It was found that the spectral and temporal measures in children with CI moved towards normal range. The result emphasizes the significance of early rehabilitation in children with hearing impairment. The role of rehabilitation related aspects are also discussed in detail which can be clinically incorporated for the betterment of speech therapeutic services in children with CI.

Keywords: acoustics, cochlear implant, Malayalam, vowels

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

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Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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Authors: Hanan Al-Jabri

Abstract:

Simultaneous interpreting is deemed to be the most challenging mode of interpreting by many scholars. The special constraints involved in this task including time constraints, different linguistic systems, and stress pose a great challenge to most interpreters. These constraints are likely to maximise when the interpreting task is done live on TV. The TV interpreter is exposed to a wide variety of audiences with different backgrounds and needs and is mostly asked to interpret high profile tasks which raise his/her levels of stress, which further complicate the task. Under these constraints, which require fast and efficient performance, TV interpreters of four TV channels were asked to render Trump's victory speech into Arabic. However, they had also to deal with the burden of rendering English emotive overtones employed by the speaker into a whole different linguistic system. The current study aims at investigating the way TV interpreters, who worked in the simultaneous mode, handled this task; it aims at exploring and evaluating the TV interpreters’ linguistic choices and whether the original emotive effect was maintained, upgraded, downgraded or abandoned in their renditions. It also aims at exploring the possible difficulties and challenges that emerged during this process and might have influenced the interpreters’ linguistic choices. To achieve its aims, the study analysed Trump’s victory speech delivered on November 6, 2016, along with four Arabic simultaneous interpretations produced by four TV channels: Al-Jazeera, RT, CBC News, and France 24. The analysis of the study relied on two frameworks: a macro and a micro framework. The former presents an overview of the wider context of the English speech as well as an overview of the speaker and his political background to help understand the linguistic choices he made in the speech, and the latter framework investigates the linguistic tools which were employed by the speaker to stir people’s emotions. These tools were investigated based on Shamaa’s (1978) classification of emotive meaning according to their linguistic level: phonological, morphological, syntactic, and semantic and lexical levels. Moreover, this level investigates the patterns of rendition which were detected in the Arabic deliveries. The results of the study identified different rendition patterns in the Arabic deliveries, including parallel rendition, approximation, condensation, elaboration, transformation, expansion, generalisation, explicitation, paraphrase, and omission. The emerging patterns, as suggested by the analysis, were influenced by factors such as speedy and continuous delivery of some stretches, and highly-dense segments among other factors. The study aims to contribute to a better understanding of TV simultaneous interpreting between English and Arabic, as well as the practices of TV interpreters when rendering emotiveness especially that little is known about interpreting practices in the field of TV, particularly between Arabic and English.

Keywords: emotive overtones, interpreting strategies, political speeches, TV interpreting

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Authors: Omer Leshem, Michael F. Schober

Abstract:

This paper introduces a new method for assessing how audience members and performers feel and think during live concerts, and how audience members' recognized and felt emotions are related. Two hypotheses were tested in a live concert setting: (1) that audience members’ cognitive perspective taking ability predicts their accuracy in identifying an emotion that a jazz improviser intended to express during a performance, and (2) that audience members' affective empathy predicts their likelihood of feeling the same emotions as the performer. The aim was to stage a concert with audience members who regularly attend live jazz performances, and to measure their cognitive and affective reactions during the performance as non-intrusively as possible. Pianist and Grammy nominee Andy Milne agreed, without knowing details of the method or hypotheses, to perform a full-length solo improvised concert that would include an ‘unusual’ piece. Jazz fans were recruited through typical advertising for New York City jazz performances. The event was held at the New School’s Glass Box Theater, the home of leading NYC jazz venue ‘The Stone.’ Audience members were charged typical NYC jazz club admission prices; advertisements informed them that anyone who chose to participate in the study would be reimbursed their ticket price after the concert. The concert, held in April 2018, had 30 attendees, 23 of whom participated in the study. Twenty-two minutes into the concert, the performer was handed a paper note with the instruction: ‘Perform a 3-5-minute improvised piece with the intention of conveying sadness.’ (Sadness was chosen based on previous music cognition lab studies, where solo listeners were less likely to select sadness as the musically-expressed emotion accurately from a list of basic emotions, and more likely to misinterpret sadness as tenderness). Then, audience members and the performer were invited to respond to a questionnaire from a first envelope under their seat. Participants used their own words to describe the emotion the performer had intended to express, and then to select the intended emotion from a list. They also reported the emotions they had felt while listening using Izard’s differential emotions scale. The concert then continued as usual. At the end, participants answered demographic questions and Davis’ interpersonal reactivity index (IRI), a 28-item scale designed to assess both cognitive and affective empathy. Hypothesis 1 was supported: audience members with greater cognitive empathy were more likely to accurately identify sadness as the expressed emotion. Moreover, audience members who accurately selected ‘sadness’ reported feeling marginally sadder than people who did not select sadness. Hypotheses 2 was not supported; audience members with greater affective empathy were not more likely to feel the same emotions as the performer. If anything, members with lower cognitive perspective-taking ability had marginally greater emotional overlap with the performer, which makes sense given that these participants were less likely to identify the music as sad, which corresponded with the performer’s actual feelings. Results replicate findings from solo lab studies in a concert setting and demonstrate the viability of exploring empathy and collective cognition in improvised live performance.

Keywords: audience, cognition, collective cognition, emotion, empathy, expressed emotion, felt emotion, improvisation, live performance, recognized emotion

Procedia PDF Downloads 112