DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Nam, Juhan | - |
dc.contributor.advisor | 남주한 | - |
dc.contributor.author | Park, Chaelin | - |
dc.date.accessioned | 2019-08-28T02:46:04Z | - |
dc.date.available | 2019-08-28T02:46:04Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843088&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/266017 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 문화기술대학원, 2019.2,[iv, 23 p. :] | - |
dc.description.abstract | Artists have their own way of expressing their own songs so that when we think of an artist we can understand which music the artist mainly performs and distinguish it from other artists. The fact that artists have their own unique characteristics means that artist can be used as a categorical label to classify songs. This artist label is more specific than genre which is often too broad or in its scope and definition, whereas it is more inclusive than a single song or album. Therefore, many music streaming services provide artist-based music recommendation and search. However, the interface is still based on a simple list of selected artists and their songs that exposes very limited information. There have been many visualization methods to effectively show a large number of artist information at a time. They are usually based on similarity among artists and displayed them in a two-dimensional or three-dimensional space. When there are too many artists, the number of artists on the space should be summarized due to the spatial limitation. Many of previous methods filtered them using musical elements. However, this was not intuitive for users who do not have much musical knowledge. On the other hand, popularity is one of the information that an artist has, and it does not require any musical knowledge in order to understand it. It is possible to find an artist who can be a representative in similar artists through popularity. Therefore, popularity can be used as a basis for displaying artist information. In this thesis, we propose a visualization system using artist embedding vectors and popularity for artist search. We extend the study of learning audio features using artist labels and attempt to implement an intuitive search method for artist data by utilizing popularity data. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Artist data visualization system▼amusic information retrieval▼adata visualization▼aartist map▼amusic metadata | - |
dc.subject | 아티스트 정보 시각화 모델▼a음악 정보 검색▼a데이터 시각화▼a아티스트 지도▼a음악 메타데이터 | - |
dc.title | "Artist-on-the-Map" | - |
dc.title.alternative | 콘텐츠 기반의 아티스트 임베딩 벡터와 인기도를 통한 아티스트 데이터 시각화 시스템 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :문화기술대학원, | - |
dc.contributor.alternativeauthor | 박채린 | - |
dc.title.subtitle | visualization system of music artist data using content-based artist embedding vectors and popularity | - |
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