Fractal dimension pattern-based multiresolution analysis for rough estimator of speaker-dependent audio emotion recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750042 ◽  
Author(s):  
Miao Cheng ◽  
Ah Chung Tsoi
Keyword(s):  
Fractal Dimension ◽  
Emotion Recognition ◽  
Multiresolution Analysis ◽  
Real Life ◽  
Emotional States ◽  
Acoustic Feature ◽  
Comparative Performance ◽  
Audio Analysis ◽  
The Arts ◽  
The Given

As a general means of expression, audio analysis and recognition have attracted much attention for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand the emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human–machine interfaces. Though there have been several the-state-of-the-arts auditory methods devised to audio recognition, most of them focus on discriminative usage of acoustic features, while feedback efficiency of recognition demands is ignored. This makes possible application of AER, and rapid learning of emotion patterns is desired. In order to make predication of audio emotion possible, the speaker-dependent patterns of audio emotions are learned with multiresolution analysis, and fractal dimension (FD) features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn the intrinsic characteristics of auditory emotions, while the utterance features are learned from FDs of each sub-band. Experimental results show the proposed method is able to provide comparative performance for AER.

scholarly journals A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

2021 ◽  
Vol 335 ◽  
pp. 04001
Author(s):  
Didar Dadebayev ◽  
Goh Wei Wei ◽  
Tan Ee Xion
Keyword(s):  
Feature Selection ◽  
Fractal Dimension ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Feature Selection Method ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Emotional States ◽  
Affective States ◽  
Emotion Classification

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

scholarly journals ON THE CHARACTERIZATION OF SCALING FUNCTIONS ON NON-ARCHEMEDEAN FIELDS

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ishtaq Ahmed ◽  
Owias Ahmad ◽  
Neyaz Ahmad Sheikh
Keyword(s):  
Multiresolution Analysis ◽  
Scaling Function ◽  
Real Life ◽  
Discrete Subgroup ◽  
Local Fields ◽  
Mathematical Tool ◽  
Spectral Pair ◽  
Spectral Pairs ◽  
The Given

In real life application all signals are not obtained from uniform shifts; so there is a natural question regarding analysis and decompositions of these types of signals by a stable mathematical tool.  This gap was filled by Gabardo and Nashed [11]   by establishing a constructive algorithm based on the theory of spectral pairs for constructing non-uniform wavelet basis in \(L^2(\mathbb R)\). In this setting, the associated translation set \(\Lambda =\left\{ 0,r/N\right\}+2\,\mathbb Z\) is no longer a discrete subgroup of \(\mathbb R\) but a spectrum associated with a certain one-dimensional spectral pair and the associated dilation is an even positive integer related to the given spectral pair. In this paper, we characterize the scaling function for non-uniform multiresolution analysis on local fields of positive characteristic (LFPC). Some properties of wavelet scaling function associated with non-uniform multiresolution analysis (NUMRA) on LFPC are also established.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

The Influence of Assessment on Learning and Teaching

2019 ◽  
pp. 166-185
Author(s):  
Susan Hallam
Keyword(s):  
Communities Of Practice ◽  
Teaching And Learning ◽  
Formal Education ◽  
Peer Assessment ◽  
Real Life ◽  
Summative Assessment ◽  
Learning And Teaching ◽  
The Arts ◽  
Assessment Procedures ◽  
Assessment Research

It is debatable whether it is appropriate to assess performance in the arts. However, formal education institutions and the systems within which they operate continue to require summative assessment to take place in order to award qualifications. This chapter considers the extent to which such summative assessment systems in music determine not only what is taught but also what learners learn. The evidence suggests that any learning outcome in formal education that is not assessed is unlikely to be given priority by either learners or teachers. To optimize learning, the aims and the processes of learning, including formative, self-, and peer assessment procedures, should be aligned with summative assessment. Research addressing the roles, methods, and value of formative, self-, and peer assessment in enhancing learning is considered. A proposal is made that the most appropriate way of enhancing learning is to ensure that summative assessment procedures are authentic and have real-life relevance supporting the teaching and learning process, to ensure that learners are motivated and see the relevance of what they are learning. This might take many forms depending on musical genre, communities of practice, and the wider cultural environment.

scholarly journals Competence-based assignment of tasks to workers in factories with demand-driven manufacturing

2021 ◽  
Vol 29 (2) ◽  
pp. 553-565
Author(s):  
Bożena Staruch ◽  
Bogdan Staruch
Keyword(s):  
Production Management ◽  
Real Life ◽  
Programming Models ◽  
Specific Task ◽  
Generalized Assignment Problem ◽  
Existence Of A Solution ◽  
Life Problems ◽  
Generalized Assignment ◽  
The Given ◽  
Potential Use

AbstractThe paper is motivated by real problems concerning tasks assignment to workers in medium-sized upholstered furniture plants managed using the Demand-Driven Manufacturing. Although the methodology was developed for furniture plants it can be applied to other types of production plants. We involve competence coefficients, which describe the level of the worker’s skills or capabilities to perform a specific task. The competence coefficients are also used to block the possibility of assigning the given task to a worker that has no skills to do it. Additionally, we involve a dummy worker to the model which guarantees the existence of a solution to the problem. We present and discuss Integer Linear Programming Models for the posted problem that are closely related to the Generalized Assignment Problem. We also discuss the potential use of the presented methodology to solve real-life problems related to production management.

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals Neural mechanisms underlying regulation of empathy and altruism by beliefs of others' pain

2021 ◽  
Author(s):  
Tao Yu ◽  
Shihui Han
Keyword(s):  
Real Life ◽  
Stimulus Onset ◽  
Neural Mechanisms ◽  
Altruistic Behavior ◽  
Emotional States ◽  
Neural Responses ◽  
Temporoparietal Junction ◽  
Subjective Estimation ◽  
Show Evidence

Perceived cues signaling others' pain induce empathy that in turn motivates altruistic behavior toward those who appear suffering. This perception-emotion-behavior reactivity is the core of human altruism but does not always occur in real life situations. Here, by integrating behavioral and multimodal neuroimaging measures, we investigate neural mechanisms underlying the functional role of beliefs of others' pain in modulating empathy and altruism. We show evidence that decreasing (or enhancing) beliefs of others' pain reduces (or increases) subjective estimation of others' painful emotional states and monetary donations to those who show pain expressions. Moreover, decreasing beliefs of others' pain attenuates neural responses to perceived cues signaling others' pain within 200 ms after stimulus onset and modulate neural responses to others' pain in the frontal cortices and temporoparietal junction. Our findings highlight beliefs of others' pain as a fundamental cognitive basis of human empathy and altruism and unravel the intermediate neural architecture.

scholarly journals Interoceptive sensibility predicts the ability to infer others’ emotional states

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258089
Author(s):  
Amelie M. Hübner ◽  
Ima Trempler ◽  
Corinna Gietmann ◽  
Ricarda I. Schubotz
Keyword(s):  
Emotion Recognition ◽  
Reaction Times ◽  
Emotional Expressions ◽  
Emotional States ◽  
Differential Modulation ◽  
Skin Conductance Responses ◽  
Weak Evidence ◽  
Fearful Expression ◽  
Bodily Sensations ◽  
Response Task

Emotional sensations and inferring another’s emotional states have been suggested to depend on predictive models of the causes of bodily sensations, so-called interoceptive inferences. In this framework, higher sensibility for interoceptive changes (IS) reflects higher precision of interoceptive signals. The present study examined the link between IS and emotion recognition, testing whether individuals with higher IS recognize others’ emotions more easily and are more sensitive to learn from biased probabilities of emotional expressions. We recorded skin conductance responses (SCRs) from forty-six healthy volunteers performing a speeded-response task, which required them to indicate whether a neutral facial expression dynamically turned into a happy or fearful expression. Moreover, varying probabilities of emotional expressions by their block-wise base rate aimed to generate a bias for the more frequently encountered emotion. As a result, we found that individuals with higher IS showed lower thresholds for emotion recognition, reflected in decreased reaction times for emotional expressions especially of high intensity. Moreover, individuals with increased IS benefited more from a biased probability of an emotion, reflected in decreased reaction times for expected emotions. Lastly, weak evidence supporting a differential modulation of SCR by IS as a function of varying probabilities was found. Our results indicate that higher interoceptive sensibility facilitates the recognition of emotional changes and is accompanied by a more precise adaptation to emotion probabilities.

scholarly journals Speech-based human emotion recognition

2021 ◽  
Author(s):  
Talieh Seyed Tabtabae
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Speaker Identification ◽  
Recognition System ◽  
Research Area ◽  
Speech Signals ◽  
Spectral Features ◽  
Emotional States ◽  
Human Communication ◽  
Set Up

Automatic Emotion Recognition (AER) is an emerging research area in the Human-Computer Interaction (HCI) field. As Computers are becoming more and more popular every day, the study of interaction between humans (users) and computers is catching more attention. In order to have a more natural and friendly interface between humans and computers, it would be beneficial to give computers the ability to recognize situations the same way a human does. Equipped with an emotion recognition system, computers will be able to recognize their users' emotional state and show the appropriate reaction to that. In today's HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know "how it is said" to react more appropriately, and make the interaction more natural. One of the most important human communication channels is the auditory channel which carries speech and vocal intonation. In fact people can perceive each other's emotional state by the way they talk. Therefore in this work the speech signals are analyzed in order to set up an automatic system which recognizes the human emotional state. Six discrete emotional states have been considered and categorized in this research: anger, happiness, fear, surprise, sadness, and disgust. A set of novel spectral features are proposed in this contribution. Two approaches are applied and the results are compared. In the first approach, all the acoustic features are extracted from consequent frames along the speech signals. The statistical values of features are considered to constitute the features vectors. Suport Vector Machine (SVM), which is a relatively new approach in the field of machine learning is used to classify the emotional states. In the second approach, spectral features are extracted from non-overlapping logarithmically-spaced frequency sub-bands. In order to make use of all the extracted information, sequence discriminant SVMs are adopted. The empirical results show that the employed techniques are very promising.

scholarly journals Emotional processing of ironic vs. literal criticism in autistic and non-autistic adults: Evidence from eye-tracking

2019 ◽  
Author(s):  
Mahsa Barzy ◽  
Ruth Filik ◽  
David Williams ◽  
Heather Jane Ferguson
Keyword(s):  
Emotional Processing ◽  
Real Life ◽  
Emotional Responses ◽  
Target Sentence ◽  
Emotional States ◽  
Typically Developing ◽  
Two Stage ◽  
Autistic People ◽  
Autistic Adults ◽  
Specific Difficulty

Typically developing (TD) adults are able to keep track of story characters’ emotional states online while reading. Filik et al. (2017) showed that initially, participants expected the victim to be more hurt by ironic comments than literal, but later considered them less hurtful; ironic comments were regarded as more amusing. We examined these processes in autistic adults, since previous research has demonstrated socio-emotional difficulties among autistic people, which may lead to problems processing irony and its related emotional processes despite an intact ability to integrate language in context. We recorded eye movements from autistic and non-autistic adults while they read narratives in which a character (the victim) was either criticised in an ironic or a literal manner by another character (the protagonist). A target sentence then either described the victim as feeling hurt/amused by the comment, or the protagonist as having intended to hurt/amused the victim by making the comment. Results from the non-autistic adults broadly replicated the key findings from Filik et al. (2017), supporting the two-stage account. Importantly, the autistic adults did not show comparable two-stage processing of ironic language; they did not differentiate between the emotional responses for victims or protagonists following ironic vs. literal criticism. These findings suggest that autistic people experience a specific difficulty taking into account other peoples’ communicative intentions (i.e. infer their mental state) to appropriately anticipate emotional responses to an ironic comment. We discuss how these difficulties might link to atypical socio-emotional processing in autism, and the ability to maintain successful real-life social interactions.

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