• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.600-610
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• 2022

While Korean piano pedagogy has seen a remarkable growth, the relatively weak attention to intermediate level has emerged as a pending problem. The limited literature review, specifically the lack of playing Korean original works, has been considered as a serious issue. To emphasize the usefulness of Kim Kukjin's "Pieces of Korean Melody for Piano" as an intermediate work, this study presents practical teaching guidelines by classifying of difficulty in his 25 pieces and providing step by step learning goals and teaching plan. The difficulty stage was based on Jane Magret's 10-step classification table for comparison with other intermediate piano literature, and this study more specifically classified Kim's pieces according to Korean melody, rhythm, and texture. As a result of the difficulty classification, it was found that the pieces from stages 4 to 10 was organized to systematically and comprehensively learn step by step from the most basic progression to Korean 'Jangdan' rhythm patterns, various articulations and decorations that express 'Sigimsae' of Korean Traditional Music, and heterophony texture. In addition, this study proposed the order of pieces for the effective teaching according to the characteristics and difficulty of the pieces. This study suggests that the findings lead to the expansion of Korean intermediate literature study and the revitalization of Korean original works teaching method.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.111-118
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• 2008

In this paper, we propose a new automatic taxonomy generation algorithm for the audio genre classification. The proposed algorithm automatically generates hierarchical taxonomy based on the estimated classification accuracy at all possible nodes. The estimation of classification accuracy in the proposed algorithm is conducted by applying the training data to classifier using k-fold cross validation. Subsequent classification accuracy is then to be tested at every node which consists of two clusters by applying one-versus-one support vector machine. In order to assess the performance of the proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigated classification performance using the proposed algorithm and previous flat classifiers. The classification accuracy reaches to 89 percent with proposed scheme, which is 5 to 25 percent higher than the previous flat classification methods. Using low-dimensional feature vectors, in particular, it is 10 to 25 percent higher than previous algorithms for classification experiments.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.293-297
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• 1996

For the representative beat calculation and efficient rhythm analysis new method, that is, QRS typification were proposed. A problem that were resulted from pattern classification based on binary logic could be solved out by the fuzzy clustering and classification nodes could be reduced by using the proposed new feature vector. The accurate representative beat could be obtained by excluding the ST-T segment that happened outlier through ST-T segment typification procedure.

• Journal of Korean society of Dental Hygiene
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• v.10 no.1
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• pp.173-183
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• 2010

Objectives : The purpose of this study was to provide basic materials for defining educational direction of dental hygiene department and establishing the instruction to improve direction consulting and academic effect of dental hygiene department student. Methods : The researcher surveyed the relation among job aptitude, academic ability and academic performance by selecting 131 dental hygiene department students of P university as study targets. Results : For high school classifications, direction searches and academic abilities of dental hygiene department students of P university, it was found that classical high school was 68.7% and vocational high school was 31.3%. For job aptitude, social type was 58.0% and artistic type was 26.0% so they were usual. For academic ability, interpersonal relation($12.78{\pm}1.34$), music/rhythm was($12.32{\pm}1.09$) and natural($12.32{\pm}1.00$) showed high scores in order over the first, the second and the third field and language/vocabulary(22.6%) and music/rhythm(21.6%) was the next. For academic performance depending on high school classification, job aptitude and academic ability, there was a significant difference in high school classification by classical high school($86.55{\pm}8.21$) and vocational high school($85.34{\pm}11.31$)(p<0.05) and there was also a significant difference in job aptitude by social type($85.45{\pm}9.42$) and artistic type($88.41{\pm}6.93$)(p<0.05). In the mutual relation between academic ability and academic performance, the high academic ability score in the first field was led to the high score in the second and the third field, showing significant mutual relation(p<0.00). Conclusions : This research has been accomplished by college students of dental hygeine department, so you have to consider before generalizing these results. Therefore it is required to research more, likewise using a comparison with other students or it should be conducted by general people.

• Korean Journal of Clinical Pharmacy
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• v.26 no.1
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• pp.84-95
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• 2016

Objective: Atrial fibrillation (AF) guidelines have been published in the USA and Europe. Recently, the USA and Europe have updated their guidelines, respectively. These new AF guidelines help in addressing key management issues in clinical situations. This study, therefore, systematically compared guidelines for rate and rhythm control pharmacotherapy of patients with AF between the USA (American College of Cardiology and American Heart Association, ACC/AHA) and Europe (European Society of Cardiology, ESC). Methods: This study investigated and compared American guidelines (2014) and European guidelines (2010 and 2012). Results: Generally, there are four meaningful differences between ACC/AHA and ESC guidelines. Important differences are treatment classification system, level of recommendation, drug list, and dosage. In addition, ACC/AHA described pharmacokinetic drug interactions for antiarrhythmic drugs. ESC emphasized ECG and atrioventricular nodal slowing as feature of antiarrhythmic drugs. Conclusion: This research addresses important use of anti-arrhythmic drugs and movement to accept recent recommendations in Korea. For the successful application of the guidelines, a role of pharmacists is crucial in clinical situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3943-3959
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• 2022

People are constantly exposed to stress and anxiety environment, which could contribute to a variety of psychological and physical health problems. Therefore, it is particularly important to identify psychological stress in time and to find a feasible and universal method of stress reduction. This research investigated the influence of different music, such as relaxation music and natural rhythm music, on stress relief based on Electroencephalogram signals. Mental arithmetic test was implemented to create a stressful environment. 23 participants performed the mental arithmetic test with and without music respectively, while their Electroencephalogram signal was recorded. The effect of music on stress relief was verified through stress test questionnaires, including Trait Anxiety Inventory (STAI-6) and Self-Stress Assessment. There was a significant change in the stress test questionnaire values with and without music according to paired t-test (p<0.01). Furthermore, a model based on Transformer for stress level classification from Electroencephalogram signal was proposed. Experimental results showed that the method of listening to relaxation music and natural rhythm music achieved the effect of reducing psychological stress and the proposed model yielded a promising accuracy in classifying the Electroencephalogram signal of mental stress.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.965-974
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• 2007

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

• ETRI Journal
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• v.44 no.3
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• pp.413-425
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• 2022

The study of literary texts is one of the earliest disciplines practiced around the globe. Poetry is artistic writing in which words are carefully chosen and arranged for their meaning, sound, and rhythm. Poetry usually has a broad and profound sense that makes it difficult to be interpreted even by humans. The essence of poetry is Rasa, which signifies mood or emotion. In this paper, we propose a poetry classification-based approach to automatically extract similar poems from a repository. Specifically, we perform a novel Rasa-based classification of Hindi poetry. For the task, we primarily used lexical features in a bag-of-words model trained using the support vector machine classifier. In the model, we employed Hindi WordNet, Latent Semantic Indexing, and Word2Vec-based neural word embedding. To extract the rich feature vectors, we prepared a repository containing 37 717 poems collected from various sources. We evaluated the performance of the system on a manually constructed dataset containing 945 Hindi poems. Experimental results demonstrated that the proposed model attained satisfactory performance.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.414-422
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• 2001

This paper proposes a classifier of music genre using time delay neural network(TDNN) fur an audio data retrieval systems. The classifier considers eight kinds of genres such as Blues, Country, Hard Core, Hard Rock, Jazz, R&B(Soul), Techno and Trash Metal. The comparative unit to classify the genres is a melody between bars. The melody pattern is extracted based un snare drum sound which represents the periodicity of rhythm effectively. The classifier is constructed with the TDNN and uses fourier transformed feature vector of the melody as input pattern. We experimented the classifier on eighty training data from ten musics for each genres and forty test data from five musics for each genres, and obtained correct classification rates of 92.5% and 60%, respectively.