• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.272-278
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• 2015

This paper studied various parameter extraction methods using pitch information of speech for the development of the emotion recognition system. For this purpose, pitch parameters were extracted from korean speech database containing various emotions using stochastical information and numerical analysis techniques. GMM based emotion recognition system were used to compare the performance of pitch parameters. Sequential feature selection method were used to select the parameters showing the best emotion recognition performance. Experimental results of recognizing four emotions showed 63.5% recognition rate using the combination of 15 parameters out of 56 pitch parameters. Experimental results of detecting the presence of emotion showed 80.3% recognition rate using the combination of 14 parameters.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.381-387
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• 2013

Recently, emotional contents that induce emotions and respond to emotions are given attention in the field of cultural industries, and extracting emotion caused by multimedia contents is being noted. Furthermore, since multimedia contents have been quickly produced and distributed these days, researches automatically to extract the feeling of multimedia contents are being accelerated. In this paper, we will study the method of emotional value extraction in the multimedia contents using the volume value of the multimedia contents in a certain frequency among sound informations. This study allows to extract the emotion of multimedia contents automatically, and the extracted information will be used to provide user's current emotion, weather, etc. for the users.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.49-54
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• 2017

In this paper, we use face database to detect human emotion in real time. Although human emotions are defined globally, real emotional perception comes from the subjective thoughts of the judging person. Therefore, judging human emotions using computer image processing technology requires high technology. In order to recognize the emotion, basically the human face must be detected accurately and the emotion should be recognized based on the detected face. In this paper, based on the Cohn-Kanade Database, one of the face databases, faces are detected by combining the detected faces with the database.

• Fashion & Textile Research Journal
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• v.22 no.2
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• pp.240-249
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• 2020

This study reviews studies that used skin temperature in order to establish an emotion evaluation protocol based on skin temperature for home appliances. A survey of skin temperature evaluation papers was conducted by the following five stages: (1) keyword search, (2) title screening, (3) abstract screening, (4) full paper screening, and (5) relevance evaluation. Selected papers were reviewed for: purpose, recruitment criteria of participants, the number of participants, apparatus, procedure, measures, analysis methods, and major findings. Thermistor sensors and thermography are used for the measurement of skin temperature. Skin temperature sensors are attached to 4 - 10 locations on the body and their mean of skin temperature is calculated by Ramanatan's 4-point or Hardy & Dubois's 7-point method. Semantic differential (SD) method and thermography measuring facial surface temperature have been used for emotion evaluation. The SD method provides a set of adjective pairs related to a product and evaluates changes in emotion from the use of the product. The range of facial surface analyzed is defined in the thermal image and temperature changes before and after the evaluation are analyzed. The evaluation items of home appliances include form, color, material, aesthetics, satisfaction, novelty, convenience, pleasantness, and excellence. Many existing emotion studies using skin temperature do not apply physiological and psychological methods. This study provides basic data to establish a skin temperature and emotion evaluation protocol by examining literature for skin temperature and evaluation of sensitivity.

• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.67-74
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• 2017

The goal of this study is to present quantification method for empathy. The micro-movement technology (non-contact sensing method) was used to identify empathy level. Participants were first divided into two groups: Empathized and not empathized. Then, the upper body data of participants were collected utilizing web-cam when participants carried expression tasks. The data were analyzed and categorized into 0.5 Hz, 1 Hz, 3 Hz, 5 Hz, 15 Hz. The average movement, variation, and synchronization of the movement were then compared. The results showed a low average movement and variation in a group who empathized. Also, the participants, who empathized, synchronized their movement during the task. This indicates that the people concentrates with each other when empathy has been established and show different levels of movement. These findings suggest the possibility of empathy quantification using non-contact sensing method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1265-1271
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• 2017

In this paper, the speech parameter fusion method is studied to improve the performance of the conventional emotion recognition system. For this purpose, the combination of the parameters that show the best performance by combining the cepstrum parameters and the various pitch parameters used in the conventional emotion recognition system are selected. Various pitch parameters were generated using numerical and statistical methods using pitch of speech. Performance evaluation was performed on the emotion recognition system using Gaussian mixture model(GMM) to select the pitch parameters that showed the best performance in combination with cepstrum parameters. As a parameter selection method, sequential feature selection method was used. In the experiment to distinguish the four emotions of normal, joy, sadness and angry, fifteen of the total 56 pitch parameters were selected and showed the best recognition performance when fused with cepstrum and delta cepstrum coefficients. This is a 48.9% reduction in the error of emotion recognition system using only pitch parameters.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.75-80
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• 2008

There are a lot of interest and research on practical area of marine leisure but few research on fundamental area. We need to suggest the theoretical basis on the merit of marine leisure. The author analyzed in visual and audio informations of seashore environment based on psychology of emotion aesthetically and musically. As a results, Peoples could get affirmative emotion through participating in seashore relaxation and changed their negative emotion into affirmative.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.149-156
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• 2013

In this study, the Model of Emotion Evaluation, an emotional analysis actively applied in environmental assessment, was divided into two parts, the abbreviated model and the inferential model, through pilot study and experiment. In addition, an analysis was conducted through the experiment on the attributes of the evaluation vocabularies of two additional types of representative models, the EPA Model and PAD Model, and the results show a huge difference in the development approach and lexical constitution of the two models. It was also identified through factor analysis that the vocabularies were abbreviated according to the respective models. Similarity relationships were analyzed using multidimensional scaling and the results show that mutual relationship was established to some degree. Based on this, we can conclude that, rather than a biased use of the Model of Emotion Evaluation in emotion evaluation, a more objective image analysis is possible by analyzing the characteristics of the model before applying it. In this study, the evaluation target was confined only to the environmental assessment of streetscape and continuous research on the Model of Emotion Evaluation that allows for the comparison of evaluation models in various areas is needed.