• The Transactions of the Korea Information Processing Society
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• v.5 no.1
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• pp.1-11
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• 1998

This paper describes design and implementation of the system that is used to efficiently retrieve music information at a digital music library. Unlike typical music information retrieval systems, this system allows the user to sing a part of the melody through the microphone which he/she wants to find rather than using title, composer or the subject catalog to search. The system then recognizes the musical notes information through the signal processing of the sounds of the entered melody, and the intervals contour is created based on this information and used as a search pattern. By running the proposed notes string search algorithm that uses the musical notes information processed with the user input, and it produces the approximate search results. Therefore, users are able to retrieve and appreciate the music whenever he/she can sing any portion of the desired music.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1098-1101
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• 2022

With the development of computer music notation programs, when drawing sheet music, it is often drawn using a computer. However, there are still many use of hand-written notations for educational purposes or to quickly draw sheet music such as listening and dictating. In previous studies, OMR focused on recognizing the printed music sheet made by music notation program. the result of handwritten OMR with camera is poor because different people have different writing methods, and lens distortion. In this study, as a pre-processing process for recognizing handwritten music sheet, we propose a method for recognizing a staff using linear regression and a method for recognizing a bar using CNN. F₁ scores of staff recognition and barline detection are 99.09% and 95.48%, respectively. This methodologies are expected to contribute to improving the accuracy of handwriting.

• Information Systems Review
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• v.16 no.3
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• pp.49-68
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• 2014

These days, consumers have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the consumers. Accordingly, sales of music in compact disk formats have steadily declined. In this regards, online digital music has become a new communication channel to listen musics, where digital files can be delivered over various online networks to people's computing devices. The majority of online digital music distributors has Music Recommender Systems for sales of digital music on their websites. Music Recommender Systems are parts of information filtering systems that provide the ratings or preferences that users give to music. Korean online digital music distributors have Music Recommender Systems. But those online music distributors didn't provide any rules or clear procedures that recommend music. Therefore, we raise important questions as follows: "Is Music Recommender Systems Fair?", "What is the impact of Music Recommender Systems on online music rankings and sales?" While previous studies have focused on usefulness of Music Recommender Systems, this study investigates not only fairness of Current Music Recommender Systems but also Relationship between Music Recommender Systems and online Music Charts. This study examines these issues based on Bandwagon effect, ranking effect, Slot effect theories. For our empirical analysis, we selected the most famous five online digital music distributors in terms of market shares. We found that all recommended music is exposed to the top of 'daily music charts' in online digital music distributors' websites. We collected music ranking data and recommended music data from 'daily music chart' during a one month. The result shows that online music recommender systems are not fair, since they mainly recommend particular music that supported by a specific music production company. In addition, the recommended music are always exposed to the top of music ranking charts. We also find that recommended music usually appear at the top 20 ranking charts within one or two days. Also, the most music in the top 50 or 100 ranks are the recommended music. Moreover, recommended music usually remain the ranking charts more than one month while non-recommended music often disappear at the ranking charts within two week. Our study provides an important implication to online music industry. Because music recommender systems and music ranking charts are closely related, music distributors may improperly use their recommender systems to boost the sales of music that related to their own companies. Therefore, online digital music distributor must clearly announce the rules and procedures about music recommender systems for the better music industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1322-1328
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• 1999

In this paper, we propose a Multiple Signal Classification(MUSIC) in conjunction with signal enhancement (SE-MUSIC) for solving the direction-of-arrival estimation problem of multiple incoherent plane waves incident on a uniform linear array. The proposed SE-MUSIC algorithms involve the following main two-step procedure : ( i )to find the enhanced matrix that possesses the prescribed properties and which lies closest to a given covariance matrix estimate in the Frobenius norm sense and (ii) to apply the MUSIC to the enhanced matrix. Simulation results are illustrated to demonstrate the better resolution and statistical performance of the proposed method than MUSIC at lower SNR.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.880-885
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• 2017

In AD-MUSIC algorithm, DOD/DOA can be estimated without computationally expensive two-dimensional search. In this paper, to further reduce the computational complexity, the Newton type method has been applied to one-dimensional search. In this paper, we summarize the formulation of the AD-MUSIC algorithm, and present how to apply Newton-type iteration to AD-MUSIC algorithm for improvement of the accuracy of the DOD/DOA estimates. Numerical results are presented to show that the proposed scheme is efficient in the viewpoints of computational burden and estimation accuracy.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• The Korean Journal of Health Service Management
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• v.14 no.1
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• pp.103-110
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• 2020

Objectives: In this paper, we propose the ethical education direction by analyzing the personal information recognition and practice of music therapists. Methods: For the analyses, we selected 60 music therapists who answered a questionnaire from members of K Music Therapy Association, and analyzed task recognition and practice ask performance using IPA method. Results: In the IPA table, the areas of high recognition and practice (1) are the areas of personal information protection information management. In the IPA table, the areas of low awareness and high practice (2) are areas of privacy communication for those who have completed ethics education. In the IPA table, the areas of low awareness and low practice (3) are areas of privacy communication when ethics education is not completed. In the IPA table, areas of high awareness and low levels of practice (4) are areas of privacy protection. Conclusions: Continuing education should be provided to improve the curriculum on the protection of personal information for music therapists, thereby raising the awareness and practice of privacy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.181-182
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• 2015

음악 검색에 있어 소셜 태그 정보는 사용자로 하여금 음악의 내재적 의미를 빠르게 파악할 수 있도록 한다. 음악의 소셜 태그 정보는 음악 추천 시스템을 활용하는 사용자(청취자)에 의해 점진적으로 완성되기 때문에 초기에 완전한 태그 정보를 수집하는 것은 어렵다. 본 논문에서는 음악의 일부 태그가 누락되어 있는 상황에서 음악 정보 검색을 자동으로 수행할 수 있는 클래스 분류 알고리즘을 제안하고자 한다.

• Journal of Information Technology Services
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• v.13 no.1
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• pp.161-179
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• 2014

Added value of music industry in Korea is not distributed and calculated properly, and this obstacle brings various problems in a creative environment. Meanwhile, a new business model such as Open Collaboration, Crowdsourcing and platform that makes decisions and innovation from external resources has been appeared in commercial area. This new model like a composer delivers to consumers directly through Youtube.com, and multi collaboration is applied to the music industry, and it enables a new type of mechanism for creation, distribution, division, and calculation of music. However there are not enough empirical study of the music market because existing relative researches has been centered around fundamental concepts and application methodologies. This research defines Open Collaboration Platform in the music industry, and studies affecting factors of Participation Intention for example Justice, Information System Quality and Perceived Value. For a survey we apply PLS(Partial Least Square) to analyse Equity, Information System Quality and structural equation between Perceived Value and Participation Intention. Analysis results show Distributive Justice and Procedural Justice affects Platform Trust, and Service Quality, Economical Value and Emotional Value affects Platform Usefulness. Also Platform Trust and Platform Usefulness affects Platform Participation Intention. We discussed academic and practical implication based on research results.

• 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.