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SingDistVis allows to explore F0 trajectories interactively by combining two views: OverallView and DetailedView. OverallView visualizes a distribution of the F0 trajectories of the song in a time-frequency heatmap. When a user specifies an interesting part, DetailedView zooms in on the specified part and visualizes singing assessment (rating) results. Here, it displays high-rated singings in red and low-rated singings in blue. When the user clicks on a particular singing, the audio source is played and its F0 trajectory through the song is displayed in OverallView. We selected heatmap-based visualization for OverallView to provide an overview of a large-scale F0 dataset, and polyline-based visualization for DetailedView to provide a more precise representation of a small number of particular F0 trajectories. This paper introduces a subjective experiment using 1,000 singing voices to determine suitable visualization parameters. Then, this paper presents user evaluations where we asked participants to compare visualization results of four types of Overview+Detail designs and concluded that the presented design archived better evaluations than other designs in all the seven questions. Finally, this paper describes a user experiment in which eight participants compare SingDistVis with a baseline implementation in exploring interested singing voices and concludes that the proposed SingDistVis archived better evaluations in nine of the questions.<\/jats:p>","DOI":"10.1007\/s11042-024-18932-3","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T03:29:12Z","timestamp":1712719752000},"page":"1057-1077","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SingDistVis: interactive Overview+Detail visualization for F0 trajectories of numerous singers singing the same song"],"prefix":"10.1007","volume":"84","author":[{"given":"Takayuki","family":"Itoh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoyasu","family":"Nakano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoru","family":"Fukayama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Hamasaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masataka","family":"Goto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"18932_CR1","unstructured":"WELCOME TO STANFORD\u2019S DAMP: Stanford Digital Archive of Mobile Performances, a repository of geo-tagged mobile performances to facilitate the research of amateur practices. https:\/\/ccrma.stanford.edu\/damp\/"},{"key":"18932_CR2","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1007\/s00371-019-01673-y","volume":"35","author":"M Ali","year":"2019","unstructured":"Ali M, Jones MW, Xie X, Williams M (2019) TimeCluster: dimension reduction applied to temporal data for visual analytics. 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