• Journal of Web Engineering
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• v.21 no.4
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• pp.1265-1282
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• 2022

The theme, background, and signal music usage history information consists of general information such as music information, platform information, and music usage information, and sensitive information such as rights management information, music usage permission range, and contract information. If sensitive information among these is disclosed, disputes such as trade secrets and infringement of personal information protection between companies or between companies and individuals may arise. We propose a selective encryption/decryption method to secure the confidentiality, integrity, reliability and non-repudiation of sensitive music usage history information used in the theme, background, and signal music blockchain environment. In the proposed method, a monitoring company encrypts sensitive information using a secret key for usage history information, which is combined with general information, and digitally signs it using a private key to register it in a block. A trust group can view and access the information at the time of inquiry by verifying the digital signature with the public key of the monitoring company and then can decrypt the sensitive information using the private key.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1502-1508
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• 2017

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.217-223
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• 2023

In this paper, we propose a new model for the conditional generation of music, considering key and rhythm, fundamental elements of music. MIDI sheet music is converted into a WAV format, which is then transformed into a Mel Spectrogram using the Short-Time Fourier Transform (STFT). Using this information, key and rhythm details are classified by passing through two Convolutional Neural Networks (CNNs), and this information is again fed into the Music Transformer. The key and rhythm details are combined by differentially multiplying the weights and the embedding vectors of the MIDI events. Several experiments are conducted, including a process for determining the optimal weights. This research represents a new effort to integrate essential elements into music generation and explains the detailed structure and operating principles of the model, verifying its effects and potentials through experiments. In this study, the accuracy for rhythm classification reached 94.7%, the accuracy for key classification reached 92.1%, and the Negative Likelihood based on the weights of the embedding vector resulted in 3.01.

• Phonetics and Speech Sciences
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• v.4 no.1
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• pp.77-87
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• 2012

Interest in music recognition has been growing dramatically after NHN and Daum released their mobile applications for music recognition in 2010. Methods in music recognition based on audio analysis fall into two categories: music recognition using audio fingerprint and Query-by-Singing/Humming (QBSH). While music recognition using audio fingerprint receives music as its input, QBSH involves taking a user-hummed melody. In this paper, research trends are described for music recognition using audio fingerprint, focusing on two methods: one based on fingerprint generation using energy difference between consecutive bands and the other based on hash key generation between peak points. Details presented in the representative papers of each method are introduced.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1025-1032
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• 2017

Recently the domestic popular song market has been growing mainly in digital sound sources. As a result of analyzing the top 100 music charts from 2012 to 2016 through digital sound sources and musical scores, the average annual BPM has fallen by 11.26 over five years. Every year, The style of music has diversified every year, and the proportion of Hip-hop has doubled from 8.5% in 2012 to 17.8% in 2015. Dance music and ballads have a high preference rate, but the relationship is inversely proportional. Singer composition was inversely proportional to the ratio of female solo to male group. Especially, the relationship between BPM and the Major/Minor key is that 81.42% for slow tempo songs is Major key and 53.85% for fast tempo songs is minor key. In the case of TV drama OST, the solo singer 's music was preferred, the music style was 80% pop and 20% ballad.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.109-112
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• 2009

The purpose of this study was to examine the differences in the brain activation evoked by music arranged in major and minor key used with an identical motion film during the fMRI testing. A part of the audio-visual combinations composed by Iwamiya and Sano were used for the study stimuli. This audio- visual clip was originally developed by combining a small motion segment of the animation "The Snowman" and music arranged in both major and minor key from the original jazz music "Avalon" rewritten in a classical style. Twenty-seven Japanese male graduate and undergraduate students participated in the study. Brain regions more activated by the major key than the minor key when presented with the identical motion film were the left cerebellum, the right fusiform gyrus, the right superior occipital, the left superior orbito frontal, the right pallidum, the left precuneus, and the bilateral thalamus. On the other hand, brain regions more activated by the minor key than the major key when presented with the identical motion film were the right medial frontal, the left inferior orbito frontal, the bilateral superior parietal, the left postcentral, and the right precuneus. The study showed a difference in brain regions activated between the two different stimulus (i.e., major key and minor key) controlling for the visual aspect of the experiment. These findings imply that our brain systematically generates differently in the way it processes music written in major and minor key(Supported by the User Science Institute of Kyushu University, Japan and the Korea Science and Engineering Foundation).

• Proceedings of the KSPS conference
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• 2000.07a
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• pp.117-134
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• 2000

Kim, Key-Seop(2000). English Restructuring and A Use of Music in Teaching English Pronunciation. JSEP 2000 voU This study has two-fold aims: one is to clarify the restructuring of English in utterance, and the other is to relate it to teaching English pronunciation for listening and speaking with a use of music and song by suggesting a model of 10-15 minute pronunciation class syllabus for every period in class. Generally, English utterances are restructured by stress-timed rhythm, irrespective of syntactic boundaries. So the rhythmic units are arranged in isochronous groups, of which the making is to attach clitic(s) to a host or head often leftwards and sometimes rightwards, which results in linking, contraction, reduction, sound change and rhythm adjustment in utterance, just as in music and song. With English restructuring focused on, a model of English pronunciation class syllabus is proposed to be put forward in class for every period of a lesson or unit. It tries to relate the focused factor(s) in pronunciation to the integrated, with teaching techniques and music made use of.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.277-285
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• 2022

Purpose of the study To study the effects of teaching chamber music courses on the mental health of college students in music schools. The key to the results is as follows. There was a significant difference in the total level of mental health between the experimental and control classes after the experiment. The total level of mental health of male and female college students in the experimental class had significant differences after the experiment, respectively. There was no significant difference in the ten factors of scl-90 in the control class before and after the experiment, while there was a significant difference. in the ten factors of scl-90 in the experimental class before and after the experiment. The experimental teaching of chamber music courses improves the mental health level of female college students better than male college students. We have come to understand Teaching chamber music courses can significantly improve the mental health of college students in music schools.

• Asia Marketing Journal
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• v.8 no.3
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• pp.141-159
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• 2006

On November 16th 2004, SK Telecom Co., Ltd. launched 'MelOn' , an ubiquitous music service, into the first time in the world. It, new business model in Korea, was a great success in domestic music industry. Its key success factors are not only differential features of service but also the superiority of technical know-how, customer-oriented spirit, and marketing mix activities compared to many competitors. The case shows how MelOn developed and then introduces what on-line music service is identifying the key success factors and presenting visible outcomes. Furthermore discussing its business implications and future challenges.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.67-70
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• 2005

To realize a traditional music recognition system, some characteristics pertinent to Far East Asian music should be found. Using Spectrogram, some distinct attributes of Korean traditional music are surveyed. Frequency distribution, beat cycle and frequency energy intensity within samples have distinct characteristics of their own. Experiment is done for pre-experimentation to realize Korean traditional music recognition system. Using characteristics of Korean traditional music, $94.5\%$ of classification accuracy is acquired. As Korea, Japan and China have the same musical roots, both in instruments and playing style, analyzing Korean traditional music can be helpful in the understanding of Far East Asian traditional music.