• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.3
• /
• pp.159-165
• /
• 2015

The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1138-1156
• /
• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• KIISE Transactions on Computing Practices
• /
• v.23 no.6
• /
• pp.386-391
• /
• 2017

In this paper, we propose a system that automatically assigns user's experience-based emotion tags from wearable sensor data collected in real life. Four types of emotional tags are defined considering the user's own emotions and the information which the user sees and listens to. Based on the collected wearable sensor data from multiple sensors, we have trained a machine learning-based tagging system that combines the known auxiliary tools from the existing affective computing research and assigns emotional tags. In order to show the usefulness of this multi-modality-based emotion tagging system, quantitative and qualitative comparison with the existing single-modality-based emotion recognition approach are performed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.262-267
• /
• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.249-255
• /
• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.985-987
• /
• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 1998.04a
• /
• pp.183-188
• /
• 1998

미래를 예측하는 가장 정확한 방법은 미래를 창조하는 것이다. 본 논문은 멀지않은 미래를 창조하는 것이다. 본 논문은 멀지않은 미래에 우리 앞에 놓일 기술과 제품에 대한 예측과 창조를 위하여 연구된 결과이다. 인간의 생활을 풍요롭게 할 수 있는 기술롸 제품을 인간 중심으로, 인간에게 만족스럽게 개발하기 위해서 인간의 생활 특성과 함께 기계화 인간 사이의 관계를 새롭게 정리하였다. 또 인간의 COMMUNICATION생활에서 그 비중을 급속히 증가시켜 가고 있는 Haman-Machine(Computer) Interaction에 대한 정밀한 고찰을 통하여 인간 중심의 Human-Machine Interaction 을 가능하게 할 Interface로서의 MultiMedia개념을 도출하였다. 개인의 감성은 감정과 구분되는 심리 변화로, 감정에 비하여 강도는 약하나 일상 생활에서 개인의 생각과 행동에 중요한 영향을 미친다. 감성은 외부의 감각자극에 대하여 직관적이고 반사적으로 발생되며, 개인의 생활경험과 상황에 따라 다양하게 변화된다. 제품에 대한 소비자의 욕구는 단순한 보유욕구에서 비교우위욕구, 사용성 욕구를 거쳐 감성욕구로 변환된다. 미래의 인간 생활에 필요한 기술과 제품은 인간의 COMMUNICATION생활과 감성 특성을 반영하여 감성적 MultiMedia 와 감성인터페이스의 개념으로 창조되었다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.411-412
• /
• 2023

Speech emotion recognition(SER) is one of the interesting topics in the machine learning field. By developing SER, we can get numerous benefits. By using a convolutional neural network and Long Short Term Memory (LSTM ) method as a part of Artificial intelligence, the SER system can be built.

• Science of Emotion and Sensibility
• /
• v.8 no.1
• /
• pp.37-45
• /
• 2005

This paper reports the remedial effect measurement of an obesity remedy machine for home use which has been developed by H Co. and the authors. It is expected that the machine enhances it's remedial effect and usability by utilizing medium frequency and thermotherapy belt etc. In order to measure it's remedial effect, a clinical experiment, which participates eight young female subjects, has been conducted for one month. The experiment includes the measurements on the changes of Gas-Exchange Responses of subjects through Cardio-Pulmonary Exercise Testing. The experimental results show that the obesity remedy machine helps the subjects to reduce their weights, fat rates, and $VCO_2s$. Thus, it turns out that the machine can be a good candidate for medical treatment on the obesity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.3099-3120
• /
• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.