scholarly journals Big Data Classification for the Analysis MEL Scale Features Using KNN Parameterization

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning Algorithms ◽  
Classification Model ◽  
Emotional States ◽  
Prosodic Features ◽  
Classification Feature ◽  
Emotional Aspects ◽  
Loaded Part ◽  
The Mean ◽  
Big Data Classification

The role of human speech is intensified by the emotion it conveys. The parameterization of the vector obtained from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the comparison of both. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms.

Analysis of speech MEL scale and its classification as big data by parameterized KNN

2021 ◽  
Vol 26 (jai2021.26(1)) ◽  
pp. 42-57
Author(s):  
Skuratovskii R ◽  
◽  
Bazarna A ◽  
Osadhyy E ◽  
◽  
...  
Keyword(s):  
Human Perception ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Emotional States ◽  
Prosodic Features ◽  
Language Recognition ◽  
Human Speech ◽  
Classification Feature ◽  
Loaded Part ◽  
Learning Platforms

Recognizing emotions and human speech has always been an exciting challenge for scientists. In our work the parameterization of the vector is obtained and realized from the sentence divided into the containing emotional-informational part and the informational part is effectively applied. The expressiveness of human speech is improved by the emotion it conveys. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the correspondent comparison of them. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms. In addition to the static analysis of the author's tonal portrait, which is used in particular in MFFC, we propose a new method for the dynamic analysis of the phrase in processing and studying as a new linguistic-emotional entity pronounced by the same author. Due to the ranking by the importance of the MEL scale features, we are able to parameterize the vectors coordinates be processed by the parametrized KNN method. Language recognition is a multi-level task of pattern recognition. Here acoustic signals are analyzed and structured in a hierarchy of structural elements, words, phrases and sentences. Each level of such a hierarchy may provide some temporal constants: possible word sequences or known types of pronunciation that reduce the number of recognition errors at a lower level. An analysis of voice and speech dynamics is appropriate for improving the quality of human perception and the formation of human speech by a machine and is within the capabilities of artificial intelligence. Emotion results can be widely applied in e-learning platforms, vehicle on-board systems, medicine, etc

scholarly journals The functions of the acoustic images in the early prose works of Alexander Grin

Rhema ◽  
2020 ◽  
pp. 9-17
Author(s):  
Natalia M. Solntseva ◽  
Zhang Rui
Keyword(s):  
Semantic Structure ◽  
Semantic Features ◽  
Emotional States ◽  
Prosodic Features ◽  
Temporal Characteristics ◽  
Acoustic Images ◽  
Plot Structure

The article deals with the phonostylistic and semantic features of acoustic images in the early realist stories of Alexander Grin. The conclusion is made about their important compositional role. Attention is focused on vocabulary with acoustic semantics, as well as onomatopoeia understood as the imitation of both the sound and its meaning. The functions of sound polyphony in the depicting of landscapes and emotional states and in the plot structure are discussed. The role of alliteration, the temporal characteristics of acoustic images, a combination of autologous and associative images, and variable functionality of remarks in dialogs are described. The prosodic features of the narrative are analyzed. The motifs of silence and music and their role in the semantic structure of Grin’s stories are noted.

Long-term dynamics and destruction of marsh vegetation in the Kolokolkova Bay of the Barents Sea

2012 ◽  
pp. 66-77 ◽  
Author(s):  
I. A. Lavrinenko ◽  
O. V. Lavrinenko ◽  
D. V. Dobrynin
Keyword(s):  
Species Composition ◽  
Plant Communities ◽  
Barents Sea ◽  
Mean Value ◽  
Marsh Vegetation ◽  
Composition And Structure ◽  
The Mean ◽  
Species Composition And Structure

The satellite images show that the area of marshes in the Kolokolkova bay was notstable during the period from 1973 up to 2011. Until 2010 it varied from 357 to 636 ha. After a severe storm happened on July 24–25, 2010 the total area of marshes was reduced up to 43–50 ha. The mean value of NDVI for studied marshes, reflecting the green biomass, varied from 0.13 to 0.32 before the storm in 2010, after the storm the NDVI decreased to 0.10, in 2011 — 0.03. A comparative analysis of species composition and structure of plant communities described in 2002 and 2011, allowed to evaluate the vegetation changes of marshes of the different topographic levels. They are fol­lowing: a total destruction of plant communities of the ass. Puccinellietum phryganodis and ass. Caricetum subspathaceae on low and middle marches; increasing role of halophytic species in plant communities of the ass. Caricetum glareosae vic. Calamagrostis deschampsioides subass. typicum on middle marches; some changes in species composition and structure of plant communities of the ass. Caricetum glareosae vic. Calamagrostis deschampsioides subass. festucetosum rubrae on high marches and ass. Parnassio palustris–Salicetum reptantis in transition zone between marches and tundra without changes of their syntaxonomy; a death of moss cover in plant communities of the ass. Caricetum mackenziei var. Warnstorfia exannulata on brackish coastal bogs. The possible reasons of dramatic vegetation dynamics are discussed. The dating of the storm makes it possible to observe the directions and rates of the succession of marches vegetation.

Seasonal trends in the stable isotopic composition of snow and meltwater runoff in a subarctic catchment at Okstindan, Norway

2004 ◽  
Vol 35 (2) ◽  
pp. 119-137 ◽  
Author(s):  
S.D. Gurney ◽  
D.S.L. Lawrence
Keyword(s):  
Isotopic Composition ◽  
Mean Value ◽  
Mean Values ◽  
Stable Isotopic Composition ◽  
Meltwater Runoff ◽  
Stable Isotopic ◽  
The Mean ◽  
The Difference ◽  
Δ18o And Δd

Seasonal variations in the stable isotopic composition of snow and meltwater were investigated in a sub-arctic, mountainous, but non-glacial, catchment at Okstindan in northern Norway based on analyses of δ18O and δD. Samples were collected during four field periods (August 1998; April 1999; June 1999 and August 1999) at three sites lying on an altitudinal transect (740–970 m a.s.l.). Snowpack data display an increase in the mean values of δ18O (increasing from a mean value of −13.51 to −11.49‰ between April and August), as well as a decrease in variability through the melt period. Comparison with a regional meteoric water line indicates that the slope of the δ18O–δD line for the snowpacks decreases over the same period, dropping from 7.49 to approximately 6.2.This change points to the role of evaporation in snowpack ablation and is confirmed by the vertical profile of deuterium excess. Snowpack seepage data, although limited, also suggest reduced values of δD, as might be associated with local evaporation during meltwater generation. In general, meltwaters were depleted in δ18O relative to the source snowpack at the peak of the melt (June), but later in the year (August) the difference between the two was not statistically significant. The diurnal pattern of isotopic composition indicates that the most depleted meltwaters coincide with the peak in temperature and, hence, meltwater production.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Alcohol Dependence Conceptualized as a Stress Disorder

2019 ◽  
pp. 198-220
Author(s):  
Leandro F. Vendruscolo ◽  
George F. Koob
Keyword(s):  
Prefrontal Cortex ◽  
Alcohol Use ◽  
Alcohol Dependence ◽  
Glucocorticoid Receptors ◽  
Critical Role ◽  
Emotional States ◽  
Brain Circuits ◽  
Negative Emotional State ◽  
Alcohol Alcohol

Alcohol use disorder is a chronically relapsing disorder that involves (1) compulsivity to seek and take alcohol, (2) difficulty in limiting alcohol intake, and (3) emergence of a negative emotional state (e.g., dysphoria, anxiety, irritability) in the absence of alcohol. Alcohol addiction encompasses a three-stage cycle that becomes more intense as alcohol use progresses: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. These stages engage neuroadaptations in brain circuits that involve the basal ganglia (reward hypofunction), extended amygdala (stress sensitization), and prefrontal cortex (executive function disorder). This chapter discusses key neuroadaptations in the hypothalamic and extrahypothalamic stress systems and the critical role of glucocorticoid receptors. These neuroadaptations contribute to negative emotional states that powerfully drive compulsive alcohol drinking and seeking. These changes in association with a disruption of prefrontal cortex function that lead to cognitive deficits and poor decision making contribute to the chronic relapsing nature of alcohol dependence.

scholarly journals Forensic Investigation of Four Monitored Green Infrastructure Inlets

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1787
Author(s):  
Leena J. Shevade ◽  
Franco A. Montalto
Keyword(s):  
Measurement Uncertainty ◽  
Green Infrastructure ◽  
Stormwater Management ◽  
Catchment Area ◽  
Low Flow ◽  
Inflow Rate ◽  
Forensic Investigation ◽  
Flow Conditions ◽  
The Mean

Green infrastructure (GI) is viewed as a sustainable approach to stormwater management that is being rapidly implemented, outpacing the ability of researchers to compare the effectiveness of alternate design configurations. This paper investigated inflow data collected at four GI inlets. The performance of these four GI inlets, all of which were engineered with the same inlet lengths and shapes, was evaluated through field monitoring. A forensic interpretation of the observed inlet performance was conducted using conclusions regarding the role of inlet clogging and inflow rate as described in the previously published work. The mean inlet efficiency (meanPE), which represents the percentage of tributary area runoff that enters the inlet was 65% for the Nashville inlet, while at Happyland the NW inlet averaged 30%, the SW inlet 25%, and the SE inlet 10%, considering all recorded events during the monitoring periods. The analysis suggests that inlet clogging was the main reason for lower inlet efficiency at the SW and NW inlets, while for the SE inlet, performance was compromised by a reverse cross slope of the street. Spatial variability of rainfall, measurement uncertainty, uncertain tributary catchment area, and inlet depression characteristics are also correlated with inlet PE. The research suggests that placement of monitoring sensors should consider low flow conditions and a strategy to measure them. Additional research on the role of various maintenance protocols in inlet hydraulics is recommended.

Gender Differences in Empathy During Adolescence: Does Emotional Self-Awareness Matter?

2021 ◽  
pp. 003329412097663
Author(s):  
Cristina Trentini ◽  
Renata Tambelli ◽  
Silvia Maiorani ◽  
Marco Lauriola
Keyword(s):  
Gender Differences ◽  
Reactivity Index ◽  
Self Awareness ◽  
Emotional States ◽  
Personal Distress ◽  
Full Knowledge ◽  
Emotional Concern ◽  
Insight Into ◽  
Tas 20

Empathy refers to the capacity to experience emotions similar to those observed or imagined in another person, with the full knowledge that the other person is the source of these emotions. Awareness of one's own emotional states is a prerequisite for self-other differentiation to develop. This study investigated gender differences in empathy during adolescence and tested whether emotional self-awareness explained these differences. Two-hundred-eleven adolescents (108 girls and 103 boys) between 14 and 19 years completed the Interpersonal Reactivity Index (IRI) and the Toronto Alexithymia Scale (TAS-20) to assess empathy and emotional self-awareness, respectively. Overall, girls obtained higher scores than boys on IRI subscales like emotional concern, personal distress, and fantasy. Regarding emotional self-awareness, we found gender differences in TAS-20 scores, with girls reporting greater difficulty identifying feelings and less externally oriented thinking than boys. Difficulty identifying feelings explained the greatest personal distress experienced by girls. Lower externally oriented thinking accounted for girls’ greater emotional concern and fantasy. These findings offer an insight into the role of emotional self-awareness–which is essential for self-other differentiation–as an account for gender differences in empathic abilities during adolescence. In girls, difficulty identifying feelings can impair the ability to differentiate between ones’ and others’ emotions, leading them to experience self-focused and aversive responses when confronted with others’ suffering. Conversely, in boys, externally oriented thinking can mitigate personal distress when faced with others’ discomfort.

scholarly journals Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 72
Author(s):  
Li Wang ◽  
Yong Zhou ◽  
Qing Li ◽  
Tao Xu ◽  
Zhengxiang Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Index System ◽  
Quality Index ◽  
Hubei Province ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Soil Conditions ◽  
The Mean ◽  
County Scale ◽  
Assessment Results

Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

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