• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.696-702
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• 2014

Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. A selection of feature vectors is very important for the performance of EEG pattern classifiers. This paper proposes a comparison of EEG feature vectors for emotion classification according to music listening. For this, we extract some feature vectors like DAMV, IAV, LPC, LPCC from EEG signals in each class related to music listening and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification according to music listening.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.693-694
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• 2014

This short paper briefly describes the proposed music recommendation method that provides suitable music pieces to a listener depending on both listeners' ratings and content of music pieces. The proposed method consists of two methods. First, listeners' ratings prediction method is a combination the traditional user-based and item-based collaborative filtering methods. Second, genre classification method is a combination of feature extraction and classification procedures. The feature extraction step obtains audio signal information and stores it in data structure, while the second one classifies the music pieces into various genres using decision tree algorithm.

• Journal for History of Mathematics
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• v.17 no.4
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• pp.65-76
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• 2004

Even though mathematics and music play very different roles in our society, they are closely related to each other. In this paper, we studies relations between mathematics and Korean traditional music, and give some ideas to use such relations in mathematics education.

• Journal of Korean Library and Information Science Society
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• v.43 no.4
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• pp.297-316
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• 2012

The purpose of this study is to investigate the problems concerning the arrangement of 679 Korean music schedules in the fifth edition of KDC and to propose improvements of that problems. In this study, therefore, the theoretical knowledge background of Korean music is examined first. Then, the development of 679 Korean music section and subsection from first edition to the fifth edition of KDC were examined. And the expansion aspects and their problems of 679 Korean music of the fifth edition of KDC were analyzed and some suggestions to solve that problems were proposed.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.1-7
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• 2009

An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.461-467
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• 2011

Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.239-248
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• 2018

Many new technologies are studied with the arrival of the 4th industrial revolution. In particular, emotional intelligence is one of the popular issues. Researchers are focused on emotional analysis studies for music services, based on artificial intelligence and pattern recognition. However, they do not consider how we recommend proper music according to the specific emotion of the user. This is the practical issue for music-related IoT applications. Thus, in this paper, we propose an probability-based music emotion classification technique that makes it possible to classify music with high precision based on the range of emotion, when developing music related services. For user emotion recognition, one of the popular emotional model, Russell model, is referenced. For the features of music, the average amplitude, peak-average, the number of wavelength, average wavelength, and beats per minute were extracted. Multiple regressions were derived using regression analysis based on the collected data, and probability-based emotion classification was carried out. In our 2 different experiments, the emotion matching rate shows 70.94% and 86.21% by the proposed technique, and 66.83% and 76.85% by the survey participants. From the experiment, the proposed technique generates improved results for music classification.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.400-416
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• 2017

Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.399-406
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• 2021

With the steady growth of the content industry, the need for research that automatically recommending content suitable for individual tastes is increasing. In order to improve the accuracy of automatic content recommendation, it is needed to fuse existing recommendation techniques using users' preference history for contents along with recommendation techniques using content metadata or features extracted from the content itself. In this work, we propose a new graph-based music recommendation method which learns an LSTM-based classification model to automatically extract appropriate tagging words from sound data and apply the extracted tagging words together with the users' preferred music lists and music metadata to graph-based music recommendation. Experimental results show that the proposed method outperforms existing recommendation methods in terms of the recommendation accuracy.