• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.3-14
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• 2006

Music summarization means a technique which automatically generates the most importantand representative a part or parts ill music content. The techniques of music summarization have been studied with two categories according to summary characteristics. The first one is that the repeated part is provided as music summary and the second provides the combined segments which consist of segments with different characteristics as music summary in music content In this paper, we propose and evaluate two kinds of music summarization techniques. The algorithm using multi-level vector quantization which provides a repeated part as music summary gives fixed-length music summary is evaluated by overlapping ration between hand-made repeated parts and automatically generated summary. As results, the overlapping ratios of conventional methods are 42.2% and 47.4%, but that of proposed method with fixed-length summary is 67.1%. Optimal length music summary is evaluated by the portion of overlapping between summary and repeated part which is different length according to music content and the result shows that automatically-generated summary expresses more effective part than fixed-length summary with optimal length. The cluster-based algorithm using 2-D similarity matrix and k-means algorithm provides the combined segments as music summary. In order to evaluate this algorithm, we use MOS test consisting of two questions(How many similar segments are in summarized music? How many segments are included in same structure?) and the results show good performance.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.521-529
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• 2007

In this paper, we introduce a music recommendation system that automatically recommends music according to users' musical tastes. The recommendation system uses a graph-based collaborating filtering in which similarities between musics are saved as a graph, and so it can perform fast recommendation based on the implicit preference information. It also has capability of recommending music according to users' dynamically changing preferences as well as users' static preferences. The recommendation server is implemented as an independent server using Java, and communicates with clients according to a specified protocol. A demo web site has been built by using the server and music download data from actual users, and the accuracy of recommendation has been measured through experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.291-300
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• 2020

Plagiarism concerns in the melody of popular music are on the rise. Despite these concerns, standards and methods for addressing these issues are lacking. This study is significant in the fact that it is the first case in the media which started as a controversy on plagiarism of popular music and even progressed to Supreme Court ruling. The first and second trial courts declared the existence of infringement of copyright by recognizing that the music in question was substantially alike as a result of comparing and reviewing the melody, rhythm, and harmony. However, the Supreme Court came to a different verdict on the infringement of musical work by reversing and remanding the case to the Seoul High Court. The Supreme Court indicated that even though the music presented in the first trial is a creative work entirely protected under the Copyright Act, expression without creativity is an area that is not protected under the law. Based on this case, this study seeks to compare and analyze the essential characteristics of melody in the judgment of infringement of copyrights in popular music, and factors related to the judgment of practical similarity and the judgment of idioms that are the criteria for judging infringement of musical work.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.409-419
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• 2007

Database community focuses on the similar music retrieval systems for music database when a humming query is given. One of the approaches is converting the midi data to time series, building their indices and performing the similarity search on them. Queries based on humming can be transformed to time series by using the known pitch detection algorithms. The recently suggested algorithm, scaled and warped matching, is based on dynamic time warping and uniform scaling. This paper proposes Humming BIRD(Humming Based sImilaR mini music retrieval system) using sliding window and center-aligned scaled and warped matching. Center-aligned scaled and warped matching is a mixed distance measure of center-aligned uniform scaling and time warping. The newly proposed measure gives tighter lower bound than previous ones which results in reduced search space. The empirical results show the superiority of this algorithm comparing the pruning power while it returns the same results.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.175-182
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• 2020

In this study, we introduced the technique of matching classical music using deep learning to design soundscape that can help the viewer appreciate painting and proposed an evaluation index to evaluate how well matching painting and music. The evaluation index was conducted with suitability evaluation through the Likeard 5-point scale and evaluation in a multimodal aspect. The suitability evaluation score of the 13 test participants for the deep learning based best match between painting and music was 3.74/5.0 and band the average cosine similarity of the multimodal evaluation of 13 participants was 0.79. We expect multimodal evaluation to be an evaluation index that can measure a new user experience. In addition, this study aims to improve the experience of multisensory artworks by proposing the interaction between visual and auditory. The proposed matching of painting and music method can be used in multisensory artwork exhibition and furthermore it will increase the accessibility of visually impaired people to appreciate artworks.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.673-676
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• 2010

빅스 바이더벡이라는 트럼펫 연주자는 일반적으로 백인재즈라는 새로운 길을 개척한 인물로 거론된다. 그러나 이 연구에서는 일반론적인 관점에서 벗어나 1920년대 초 흑인 재즈라는 테두리 안에서의 빅스 바이더벡의 독창성이 20세기 초 영문학에서의 새로운 사조였던 ‘의식의 흐름' 이라는 표현법과 그 유사성이 깊은 것으로 본다. 음악과 문학과의 그 흐름상의 유사성을 직접적으로 영향을 서로 어떻게 받았는지를 증명할 수 는 없지만, 표현방법 면에서 볼 때 두 다른 예술 분야에서의 유사성은 충분히 흥미로운 것이다. 특히 미국의 한 재즈연주자의 연주가 같은 시대 영국의 문학에서의 새로운 시도와 그 흐름을 같이 한다면, 현재 우리에게도 다른 분야의 흐름과 음악의 흐름상 연관성에 대한 연구도 보다 폭넓게 이루어 져야 할 것이다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.122-125
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• 2010

사용자와 취향이 비슷한 사용자를 찾고, 이 유사 사용자가 선호한 아이템을 추천하는 협력적 필터링방식은 일반적으로 많이 사용되는 추천 방식이다. 하지만 협력적 필터링 방식은 어떤 상황적 요소도 고려하지 않아 모든 상황에서 동일한 추천 결과를 제시하게 된다. 반면, 상황을 고려한 추천 방식은 다른 상황에서 그 상황에 적합하다고 판단되는 추천 리스트를 보여주는 다양성을 가지지만 개인의 선호를 반영하지 못하는 한계를 가진다. 이에 협력적 필터링 방식과 상황에 따른 추천 방식을 함께 고려하려는 시도가 있다. 본 논문에서는 시간 상황에 따른 음악 추천 시, 전체 상황에서 가장 유사한 사용자를 찾고 이 유사 사용자의 현재 상황에서의 선호 아이템을 추천하는 모델을 제시하고 실험을 통하여 이 모델의 한계와 실용 가능한 상황을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.731-732
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• 2024

본 논문은 음원 메타데이터 임베딩 방법론을 기반으로 새로운 음원 추천 방법을 제안한다. 사용자 행동 데이터를 활용한 개인 맞춤형 음악 추천 모델은 신규 사용자의 데이터가 부족할 경우, 적절한 추천이 어려운 콜드스타트 현상을 초래할 수 있다. 본 연구에서는 플레이리스트의 음원 메타데이터를 Song sentence 로 구성하고, 고차원 벡터 공간에 임베딩하여 유사도를 계산한 추천 알고리즘을 구축한다. 사용자 행동 데이터가 아닌 음원의 자체적인 정보에 근거하기 때문에 콜드 스타트 현상을 보완하여 사용자에게 편리한 음악 감상 경험을 제공할 수 있을 것으로 기대된다.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.35-42
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• 2022

This study is to examine how architecture is affected by music through practical examples of VEX house that designed based on music Vexations to suggest for engraftment potential and application of musical elements to architectural design based on the correlation between music and architecture. VEX house has been transformed with repetition and musical elements such as loops, erratic scales and harmonies. Also, apart from VEX house, the album 'Vex' was applied from architecture to music. That is, those are taking into account the results of the exchanges of different arts. Through the application of various musical elements in the construction process of the building, the potential of the new aesthetics of the building is considered.