Visualization of Music Data Visualization Tools that deal with music Mary Carter Information Visualization, §554 Fall 2012

Definition of the data set MUSIC

Definition of the data set MUSIC Can be as complex as “the science or art of ordering tones or sounds in succession, in combination, and in temporal relationships to produce a composition having unity and continuity,” or as simple as “an agreeable sound.”

Definition of the data set MUSIC Can be as complex as “the science or art of ordering tones or sounds in succession, in combination, and in temporal relationships to produce a composition having unity and continuity,” or as simple as “an agreeable sound.” Or as simple as “an agreeable sound.”

Definition of the data set MUSIC Can be as complex as “the science or art of ordering tones or sounds in succession, in combination, and in temporal relationships to produce a composition having unity and continuity,” or as simple as “an agreeable sound.” Or as simple as “an agreeable sound.” “When we conceptualize music as information, we are referring to elements of music such as pitches, chords, tempo and dynamics on one level, and contextual information such as genres, performers, dates and instrumentation on another.” -Margaret Lam

Definition of the data set MUSIC Can be as complex as “the science or art of ordering tones or sounds in succession, in combination, and in temporal relationships to produce a composition having unity and continuity,” or as simple as “an agreeable sound.” Or as simple as “an agreeable sound.” “When we conceptualize music as information, we are referring to elements of music such as pitches, chords, tempo and dynamics on one level, and contextual information such as genres, performers, dates and instrumentation on another.” -Margaret Lam Many of the visualizations rely heavily on context, i.e. the visualizing by genre or artist.

Definition of the data set MUSIC 20 tools were examined, and fell into three categories:

Definition of the data set MUSIC 20 tools were examined, and fell into three categories: Music discovery tools

Definition of the data set MUSIC 20 tools were examined, and fell into three categories: Music discovery tools Tools for research

Definition of the data set MUSIC 20 tools were examined, and fell into three categories: Music discovery tools Tools for research Tools for the consumer

Definition of the data set MUSIC 20 tools were examined, and fell into three categories: Music discovery tools

Music discovery tools MUSIC MusicMap [music-map.com] Music Sun [http://pampalk.at/MusicSun/] current information and links NOT available Music Rainbow [http://pampalk.at/MusicRainbow/] current information and links NOT available MusicLens [http://www.ddd-system.com/produkte/musiclens/] PROTOTYPE: Liveplasma [www.liveplasma.com]

Characteristics Music discovery tools are by far the most prevalent visualization tools for music. These tools can be manifested in a variety of ways: Treemaps: Most tools are hybrid visualizations, usually a combinations of flow charts, connection visualizations, and interactive visualizations ( see LivePlasma, and Music Sun, and MusicLens) These tools show relations amongst musicians. The idea is that, if a user likes one kind of music, he or she can use an artist or particular song to find similar music.

PROTOTYPE DISCOVERY TOOL LIVE PLASMA [www.liveplasma.com] -courtesy of ed tech toolbox

Perceptual x Interaction x TOOLBOX FOR MusicLens Info Density x X Position x Size Orientation Texture Shape Color Shading Depth Cues Surface Motion Stereo Proximity Similarity Continuity Connectedness Closure Containment Interaction Direct Manipulation x Immediate Feedback Linked Displays Animate Shift of Focus Dynamic Sliders Semantic Zoom Focus + Content Details on-demand Output  Input TOOLBOX FOR MusicLens Color distinguishes between different kinds of electronic/different instruments associated in the making of the music Only a connoisseur of the techno music industry would be able to digest everything on the page. No direct manipulation with the data. Info Density Maximize data ratio x Maximize data density Minimize Lie Factor X

Music Viz Tools for Researchers Visualization of the last.gm dataset [http://www.visual-telling.com/?p=18] Genealogy of Music and Pop Rock by Rebee Garofalo [http://www.historyshots.com/rockmusic/index.cfm] Musicovery: [musicovery.com] Last.fm Heatmap Calendars [http://dekstop.de/weblog/2011/09/lastfm_heatmap_calendars/] Last Graph [http://lastgraph3.aeracode.org/] Musician Map [http://www.sfu.ca/~jdyim/musicianMap/] Music listening habits via streamed graphs [http://www.leebyron.com/what/lastfm/] PROTOTYPE: Ishkur’s Guide to Electronic Dance Music [http://techno.org/electronic-music-guide/]

CHARACTERISTICS Researchers are any users who are not using the following information visualization tools to enhance their own personal music taste, but instead are using the tools to further their knowledge for the purposes of proving a thesis (however tentative it may be). Research visualization tools are an amalgam of many different types of visualizations, including heatmaps (see Last.fm Heatmap Calendars), graphs (see Last Graph), maps (see Genealogy of Music and Pop Rock by Rebee Garofolo, Musicovery, Musician Map, and Ishkur’s Guide to Electronic Music)

PROTOTYPE RESEARCHER TOOL Ishkur's Guide to Electronic Music [http://techno.org/electronic-music-guide/] -courtesy of DJ Vibe

TOOLBOX FOR ISHKUR’S GUIDE TO ELECTRONIC MUSIC Perceptual Position x Size Orientation Texture Shape Color Shading Depth Cues Surface Motion Stereo Proximity Similarity Continuity Connectedness Closure Containment Interaction Direct Manipulation Immediate Feedback Linked Displays Animate Shift of Focus Dynamic Sliders Semantic Zoom Focus + Content Details on-demand Output  Input TOOLBOX FOR ISHKUR’S GUIDE TO ELECTRONIC MUSIC Color distinguishes between different kinds of electronic/different instruments associated in the making of the music Only a connoisseur of the techno music industry would be able to digest everything on the page. No direct manipulation with the data. Info Density Maximize data ratio Maximize data density Minimize Lie Factor x

MUSIC Tools for the Consumer

Tools for the Consumer MUSIC MusicNodes [http://www.musicnodes.no/AboutUs.aspx] Zune MixView (retired) [http://www.dailymotion.com/video/x9nmnh_microsoft-zune-mixview-ad_news#.UNGBlXdQCig] Tuneglue [http://audiomap.tuneglue.net/] Moody [http://www.crayonroom.com/moody.php] PROTOTYPE: Hitlantis [hitlantis.com]

CHARACTERISTICS Consumer tools are very similar to music discovery tools, but their ultimate goals are different Consumer tools entice users to purchase music, and thus come with more added information, as well as the ability to share information about music preferences. Visualizations are most often nodal maps (Hitlantis, MusicNodes), some are just interactive visuals (Zune MixView).

PROTOTYPE CONSUMER TOOL Hitlantis [www.hitlantis.com] - courtesy of tight mix blog

Perceptual x Interaction x TOOLBOX FOR HITLANTIS Position x Size Orientation Texture Shape Color Shading Depth Cues Surface Motion Stereo Proximity Similarity Continuity Connectedness Closure Containment Interaction Direct Manipulation x Immediate Feedback Linked Displays Animate Shift of Focus Dynamic Sliders Semantic Zoom Focus + Content Details on-demand Output  Input TOOLBOX FOR HITLANTIS Hitlantis makes good use of color, but perhaps at the expense of other perceptual cues. There is quite a bit of interaction to be had, with rich feedback and details provided Info Density Maximize data ratio x Maximize data density Minimize Lie Factor

CONCLUSION From the above analyses of twenty music information visualization tools, it is apparent that most tools are used for music discover. All tools rely heavily on color as a method of conveying information. Very few tools are misleading, though some may not maximize data ratio and density, instead preferring too much “flare” over efficient presentation of information. Direct manipulation is a preferable, though not always present facet of many tools analyzed herein. Research tools make more use of a variety of different types of visualizations (graphs and heatmaps as well as nodal maps).