Dissertation/Thesis Abstract

Visualization of music relational information sources for analysis, navigation, and discovery
by Donaldson, Justin, Ph.D., Indiana University, 2011, 180; 3449549
Abstract (Summary)

The information revolution has made digital music and related technology accessible and affordable to larger and broader demographics. In addition to the increase of music, portable music playback devices, and musicians, casual music fans are also finding it easier to communicate their musical taste online through blogs, tags, ratings, and published playlists. The metadata that is produced by these casual fans becomes a valuable source of data for recommender systems, as well as providing the basis for better understanding complex musical associations such as genre.

In addition to new social sources of information, new acoustic music analytical techniques are being developed that better match human perceptual processes. However, prevailing research indicates that certain aspects of musical cognition are trained, rather than innate. Therefore, in the case of acoustic as well as other metadata, it is necessary to consider relationships between musical pieces that are formed socially and culturally. Since these relationships cannot be derived from the music itself, it is impossible or at least intractable to model an understanding of music based on intrinsic acoustic information contained within a musical piece.

Visualizations of musical corpuses are commonly created by casual fans and academic researchers alike. The two dimensional representations based around network layout or multidimensional scaling techniques work well to capture as much of the relational data as possible. The researcher asserts the importance of such visualization for providing music recommendation and related analyses. The researcher provides a survey of existing music corpus visualization in the literature, as well as providing several new music relationships visualization related projects and techniques suitable for academic analysis and casual users.

Indexing (document details)
Advisor: Stolterman, Erik A.
Commitee: Bardzell, Jeffrey S., Byrd, Donald, Paolillo, John C., Torrens, Marc
School: Indiana University
Department: Informatics
School Location: United States -- Indiana
Source: DAI-B 72/06, Dissertation Abstracts International
Subjects: Music, Computer science
Keywords: Association, Genre, Interaction, Music relation, Recommendation, Visualization
Publication Number: 3449549
ISBN: 9781124569932
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