• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.807-824
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• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• Korean Journal of Cognitive Science
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• v.19 no.4
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• pp.477-497
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• 2008

Mood classification of blog text is an interesting problem, with a potential for a variety of services involving the Web. This paper introduces an approach to mood classification enhancements through the normalized negation n-grams which contain mood clues and corpus-specific term weighting(CSTW). We've done experiments on blog texts with two different classification methods: Enhanced Mood Flow Analysis(EMFA) and Support Vector Machine based Mood Classification(SVMMC). It proves that the normalized negation n-gram method is quite effective in dealing with negations and gave gradual improvements in mood classification with EMF A. From the selection of CSTW, we noticed that the appropriate weighting scheme is important for supporting adequate levels of mood classification performance because it outperforms the result of TF*IDF and TF.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.25-35
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• 2013

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. The experiments show that the feature reduction methods suggested in this paper have better performance than that of the conventional dimension reduction methods, R-Square and PCA. As performance improvement by feature reduction only is subject to limit, modular neural network is used as another method to improve the performance. The experiments show that the method also improves performance effectively.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.

• Journal of IKEEE
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• v.13 no.2
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• pp.159-166
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• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.23-29
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• 2019

In this paper, the mood of images was classified into eight categories through a deep convolutional neural network and video was automatically generated using proper background music. Based on the collected image data, the classification model is learned using a multilayer perceptron (MLP). Using the MLP, a video is generated by using multi-class classification to predict image mood to be used for video generation, and by matching pre-classified music. As a result of 10-fold cross-validation and result of experiments on actual images, each 72.4% of accuracy and 64% of confusion matrix accuracy was achieved. In the case of misclassification, by classifying video into a similar mood, it was confirmed that the music from the video had no great mismatch with images.

• Journal of Oriental Neuropsychiatry
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• v.26 no.1
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• pp.11-22
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• 2015

Objectives: The purpose of this study is to find the relationship between Chiljeong, as a stress factor and pathological symptoms, by using Several Mood state Questionnaires. Methods: A total of 144 students of Dongshin University Oriental Medicine responded to the Questionnaires for Sasang Constitution Classification II (QSCCII) and regarding several mood states. In this study, 132 students' data were used, excluding the data from 12 of the students. The included 132 students were classified into four groups according to QSCCII. The effects of Chiljeong as a stress factor in diseases were determined by Several mood state Questionnaires. These data were analyzed by frequency, Person's chi-square Test, ANOVA, Scheffe, Multiple Comparison, and Correlation using IBM SPSS 19.0K for Windows. Results: 1. Sasang Constitution made no difference on the level of mood and the variability of mood. 2. In physical symptoms scale, the average of "Noh" was higher than that of other emotions in Gastrointestinal, Cardiovascular, and Pain symptoms. The average of "Gyeong" was higher than that of other emotions in Insomnia symptoms.

• Journal of Oriental Neuropsychiatry
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• v.22 no.4
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• pp.63-75
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• 2011

Objectives : One's mind is turned over by environment and personal relationship. This Emotion is called Chiljung in Oriental Medicine. Sasang Constitution is sorted each Emotion by Nature & Emotion(性情). So, this study aimed at figuring out the relations on Sasang Constitution, and emotion traits of oriental medicine students by EEQ and CISS(as named Mood scale). Methods : 199 students of Dongshin university oriental medicine were tested by Questionnaire for Sasang Constitution ClassificationII(QSCCII) and Mood scale. In this study is used 156 students' data, except 43 students' one for research. 156 students are classified four groups by QSCCII. The degree of emotion was determined by Mood scale. These data ware analyzed by frequency, t-test, ANOVA, Multiple comparison, Correlation, Regression with SPSS windows 15.0. Results : 1. Soeumin has high score on EEQ more than Soyangin. 2. Sasang constitution make no difference on CISS, except emotion-oriented coping in not classify group. 3. It has influence on Emotional express by Sasang constotution that Task-oriented coping, EEQ and CISS. Conclusions : Sasang constitution has significant difference on Emotional express.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.391-400
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• 2007

According to the recent advances in multimedia computing, storage and searching technology have made large volume of music contents become prevalent. Also there has been increasing needs for the study on efficient categorization and searching technique for music contents management. In this paper, a new classifying method using the local information of music content and music tone feature is proposed. While the conventional classifying algorithms are based on entire information of music content, the algorithm proposed in this paper focuses on only the specific local information, which can drastically reduce the computing time without losing classifying accuracy. In order to improve the classifying accuracy, it uses a new classification feature based on music tone. The proposed method has been implemented as a part of MuSE (Music Search/Classification Engine) which was installed on various systems including commercial PDAs and PCs.