• Journal of the Korean Magnetic Resonance Society
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• v.24 no.1
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• pp.23-29
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• 2020

Mozart's music has been suggested to affect spatio-temporal reasoning of listeners, which has been called "Mozart effect". However, the effects of Mazart's music on human metabolism have not been known. We dissected Mozart's music into its compositional elements and studied their effects on metabolism of experimental animals. Mozart music significantly reduced cortisol level induced by stress. NMR metabolomic study revealed different urine metabolic profile according to the listening to Mozart's music. In addition, each element of music exhibited different metabolic profile. Functional MRI study also showed enhanced brain activity upon listening to Mozart's music. Taken together, Mozart's music seems to be related with brain activity, stress hormone and whole body metabolism.

• Journal of Advanced Navigation Technology
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• v.17 no.4
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• pp.386-395
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• 2013

This paper proposes a root-assisted MUSIC algorithm which uses a combination of the MUSIC and the root-MUSIC algorithm. This algorithm consists of two steps. Firstly, a coarse DOA is computed by the root-MUSIC algorithm. Secondly, a precise DOA estimation is carried out by the MUSIC algorithm in the reduced searching range. This paper analyzes the accuracy and the resolution performance of the proposed DOA estimation method using a software simulation platform.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.300-308
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• 2021

The processes of informatization of the modern educational space are inextricably linked with the active introduction of innovative information technologies, which diversify the forms of education and upbringing. The use of these technologies in education due to their specific properties significantly enhances the clarity of learning, emotional impact on students, helps to deepen interdisciplinary links, intensifies students' work, and improves the organization of educational activities. Innovative information technologies offer new opportunities for the use of text, audio, graphic, and video information in lessons, enriching the methodological possibilities of the lesson. Today, the use of these technologies is becoming an integral part of the study of any subject. Using multimedia presentations, publications, and websites created by students in the learning process, they can develop learning skills. According to researchers, there are many multimedia programs for working with a computer in a music lesson, namely: a music player, a program for singing karaoke, a music constructor, music encyclopedias, and training programs. The introduction of innovative information technologies in the system of music education allows expanding learning opportunities.

• Journal of the Ergonomics Society of Korea
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• v.32 no.5
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• pp.421-427
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• 2013

Objective: This paper is to analyze the impact of musical training to the fast ${\alpha}$ wave activation of the EEG. Background: EEG is neurological research method that can observe the brain function in real time. EEG can be used to determine the nervousness and relaxedness of a person who receives stimuli in a structured environment. Therefore, it is possible to interpret the functional state of human brain by the analysis of EEG. Method: The brain activities of two groups of university students in the point of RFA(Relative Fast Alpha) caused by different music are analyzed in this paper. One is the group of music majors and the other is the group of non-majors. Results: Music major and non-major groups show meaningful differences in RFA during exposed to classic and metal music. Conclusion: Learning experience on music affects RFA increment of music majors. Application: The result of this study will be used as basic data to evaluate the learning effects of students who want to study music.

• Journal of Web Engineering
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• v.20 no.8
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• pp.2335-2360
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• 2021

A folksonomy is a classification system in which volunteers collaboratively create and manage tags to annotate and categorize content. The folksonomy has several problems in retrieving music using tags, including problems related to synonyms, different tagging levels, and neologisms. To solve the problem posed by synonyms, we introduced a mood vector with 12 possible moods, each represented by a numeric value, as an internal tag. This allows moods in music pieces and mood tags to be represented internally by numeric values, which can be used to retrieve music pieces. To determine the mood vector of a music piece, 12 regressors predicting the possibility of each mood based on acoustic features were built using Support Vector Regression. To map a tag to its mood vector, the relationship between moods in a piece of music and mood tags was investigated based on tagging data retrieved from Last.fm, a website that allows users to search for and stream music. To evaluate retrieval performance, music pieces on Last.fm annotated with at least one mood tag were used as a test set. When calculating precision and recall, music pieces annotated with synonyms of a given query tag were treated as relevant. These experiments on a real-world data set illustrate the utility of the internal tagging of music. Our approach offers a practical solution to the problem caused by synonyms.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.32-55
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• 2018

The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures.

• International Journal of Contents
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• v.10 no.4
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• pp.63-68
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• 2014

In this paper, we propose a smart system that can optically recognize a music score within a document and can play the music after recognition. Many historic handwritten documents have now been digitalized. Converting images of a music score within documents into digital files is particularly difficult and requires considerable resources because a music score consists of a 2D structure with both staff lines and symbols. The proposed system takes an input image using a mobile device equipped with a camera module, and the image is optimized via preprocessing. Binarization, music sheet correction, staff line recognition, vertical line detection, note recognition, and symbol recognition processing are then applied, and a music file is generated in an XML format. The Music XML file is recorded as digital information, and based on that file, we can modify the result, logically correct errors, and finally generate a MIDI file. Our system reduces misrecognition, and a wider range of music score can be recognized because we have implemented distortion correction and vertical line detection. We show that the proposed method is practical, and that is has potential for wide application through an experiment with a variety of music scores.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.343-350
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• 2017

Music Braille is essential and academic materials for the blind to learn and enjoy music. In Korea, National Library for the Disabled is the public institution for the manufacture digital music braille by the request and the center of music rehabilitation the disabled also make the digital music braille on a welfare level. However, most of braille libraries in Korea do have little music braille compared to other braille books. This paper reports the current situations how braille libraries including National library for the Disabled contain braille books, music brailles and digital music brailles to find out that the blinds can be provided exact informations about music brailles and digital music brailles so they can learn music with the right materials such as music brailles. In conclusion, the construction of Database for the digital music brailles is required and necessary for the blind to have better activities of music from the welfare and artistic point of view.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.258-261
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• 2007

This paper presents techniques to recognize music using back propagation network, one of the neural network algorithms, and to preprocess technique for music image. Music symbols and music notes are segmented by preprocessing such as binarization, slope correction, staff line removing, etc. Segmented music symbols and music notes are recognized by music note recognizing network and non-music note recognizing network. We proved correctness of proposed music recognition algorithm through experiments and analysis with various kind of musics.