• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.115-116
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• 2016

PC용 작곡 프로그램의 보편화에도 일반인들이 음악제작을 어려워하는 이유는 악기를 다룬 경험의 부족, 음악 또는 작곡의 지식부족, MIDI 소프트웨어 학습에 대한 두려움 등이다. 본 논문에서는 이러한 문제를 해결하기 위한 기술로 MIDI를 전혀 모르고, 악기를 다룬 경험이 부족하고 또한 악보조차 읽지 못하더라도 음악의 제작 및 작곡, 편곡이 가능한 효과적인 일반 텍스트 기반 음악 표기법을 제안한다. 이를 통해 음악을 좋아하지만 자신의 음악을 제작하지 못했던 많은 일반인들에게 음악을 제작, 보급하는데 큰 도움이 될 뿐 아니라 타인과의 협업을 통해 우수한 품질의 음악을 제작할 수 있다.

• Annual Conference of KIPS
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• 2013.05a
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• Journal of The Korean Association of Information Education
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• v.6 no.1
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• pp.42-52
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• 2002

For an effective listening to music, a repeated listening to music should be possible without any restriction of time and space as well as to music for a music class and the various multi-media learning materials for listening to music is required. However, an individualized and repeated listening is difficult and an individualized listening materials are lacking. To solve these problems, learning system for listening to music in elementary school sharing learning materials required for it and including learning factors of listening to music is required. Therefore, this study analyzes learning factors of listening to music in elementary school and the learning system for listening to music is designed and developed to make elementary students have their own active listening to music and feel achievement and confidence by teacher's evaluation of their writings after listening to music and experience a joyful musical activity. And it is applied to elementary school.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.215-220
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• 2016

Music braille is made up of more systematic and various symbols compared with text braille. Therefore, the translation of braille music requires extensive knowledge of the music and the braille symbols. Because currently the computer program for the text braille has already developed, we don't need any help from the braille translator. However, the translation of music braille is hard without the help of a professional music braille translator, because the translation computer program is not perfect. The current situation is that the music braille translator reads the music score and translates it into the braille himself. In this paper, we designed and implemented the "Braille Music Converter", you can implement a person does not understand the braille translation into music braille well. It includes translation into text braille for the lyrics processing and rest, octave, key signature, time signature, tie, slur, repeat mark was confirmed that the successful conversion to the actual music score.

• Phonetics and Speech Sciences
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• v.10 no.3
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• pp.19-29
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• 2018

We propose to use independent component analysis (ICA) and deep neural network (DNN) to detect music sections in broadcast drama contents. Drama contents mainly comprise silence, noise, speech, music, and mixed (speech+music) sections. The silence section is detected by signal activity detection. To detect the music section, we train noise, speech, music, and mixed models with DNN. In computer experiments, we used the MUSAN corpus for training the acoustic model, and conducted an experiment using 3 hours' worth of Korean drama contents. As the mixed section includes music signals, it was regarded as a music section. The segmentation error rate (SER) of music section detection was observed to be 19.0%. In addition, when stereo mixed signals were separated into music signals using ICA, the SER was reduced to 11.8%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.10-16
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• 1985

In this paper, a computer vision system, which catches printed music score image using CCTV camera and microcomputer, and then recognizes the image and performs tar music with speaker, is discussed. Integral projection method is adopted for feature detection and recognition of the music score image. The range of recognition is con(ined to staffs, perpen-dicular lines and musical notes including chord notes among the various kinds of elements of music score. The practical recognition algorithm considering noises, the preprocessing processes getting rid of noises are also showed, and simple hardware system playing chord is made, In the results, good recognition ratio and performance are obtained.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.83-91
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• 2017

In this paper, we propose a thorough solution for adjusting virtual sound field with different kinds of devices and software in preliminary procedure and late stage of music processing. The basic process of music production includes composing, arranging and recording at pre-production stage as well as sound mixing and mastering at post-production stage. At the initial stage of music creation, it should be checked whether the design of virtual sound field, the choice of the tone and the instrument used in the arrangement match the virtual sound field required for the final work. In later recording, mixing and mastering, elaborate adjustments should be done to the virtual sound field. This study also analyzed how to apply the parameter of the effectors to the design and adjustment of the virtual sound field, making it the source of our creation.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.93-100
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• 2017

In this paper, we propose a methodology of using time-lag, make it serve the sound field, in order to smoothen the music production and reduce conflicts. The importance of music production in today's music industry chain is becoming more and more apparent. In the process of music production, the creators pay more attention to the design and adjustment of virtual sound field, especially the late mixing and production. In the process, as a commonly used tool for the adjustment of sound field, "time-lapse" plays a decisive role.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.417-420
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• 2005

This report shows the new music player's system which could separate music's tempo by analysis Peak level frequency by time from some percussion instruments. After this process, the new music player could classify some fast or slow genre's musics without a user's order, then we should listen to a fast or slow genre's music by a button.

• Annual Conference of KIPS
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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.