The states that Americans sing about. Some data to back up those bar bets?
Discograph generates an interactive visualization of relationships between nearly 5 million artists, bands and labels, based on data from the Discogs.com database.
Examples: The Beatles | The Fall | Neil Young
Examples: The Beatles | The Fall | Neil Young
Best Ever Albums aggregates 17,000 "greatest album" charts to establish a statistical consensus on popular music rankings. [more inside]
Pianogram - histogram + piano notes = pianogram; select from existing pieces or import your MIDI file. A part of Joey's Visual Playground.
Music Machinery presents a map of each U.S. state's most distinct favorite band or recording artist, as well as an app for playing with the data.
"Exploring Gender Bias in listening Do men listen to different music than women do? Anecdotally, we can think of lots of examples that point to yes – it seems like more of One Direction’s fans are female, while more heavy metal fans are male, but let's take a look at some data to see if this is really the case." An examination of music listening data from Paul Lamere of The Echo Nest.
GaMuSo is an application of BioGraph-based data mining to music, which helps you get recommendations for other musicians. Based on 140K user-defined tags from last.fm that are collected for over 400K artists, results are sorted by the "nearest" or most probable matches for your artist of interest (algorithm described here). [more inside]
Listen to Wikipedia edits in real-time. Bells are additions, strings are subtractions. Pitch is the size of the edit. One can listen to the edits in various languages too: Japanese | Swedish | German | a mix of various languages. Wikidata as well. It was based on Listen to Bitcoin. [more inside]
Every Noise At Once. A map of musical genres, built by Glenn McDonald of The War Against Silence and the Echo Nest. Click on a genre name to hear a sound sample, or pop it open to see a map of bands within that genre.
In The Geographic Flow of Music (arxiv), researchers Conrad Lee and Pádraig Cunningham propose a method to use data from the last.fm API to track the world's listening habits by location and time, showing where shifts in musical tastes have originated and subsequently migrated. Results show music trends originating in smaller cities and flowing outward in unexpected ways, contradicting some assumptions in social science about larger cities being more efficient engines of (cultural) invention.
The Music Ngram Viewer from Peachnote tracks appearances of any given note or chord sequence in a corpus of 60,000 optically scanned public-domain classical scores, ranging from the 17th century to the present -- a la what Google Ngram Viewer does for words and phrases. A fuller description with examples. And if you don't like the Google-esque GUI, you can download the raw data and mess with it yourself. (Via Music Hack Day Boston.)
The Billboard Wayback Machine is an interactive that lets you explore the Billboard charts spanning from 1964 to 2011
A corpus analysis of rock harmony [PDF] - The analyses were encoded using a recursive notation, similar to a context-free grammar, allowing repeating sections to be encoded succinctly. The aggregate data was then subjected to a variety of statistical analyses. We examined the frequency of different chords and chord transitions ... Other results concern the frequency of different root motions, patterns of co-occurrence between chords, and changes in harmonic practice across time. More information, analysis, and explanation here.
The intersect of data visualization and aural phenomena is a fascinating space, from simple chartings of the history of sampling to mapping the entire world of music (or even just electronica). Pop songs become sketches, iTunes libraries become twisted geometric forms, and last.fm listening behaviors form coloured orbs and waves. The collaborative networks of comtemporary rappers, jazz musicians, and classical composers are revealing of specific and meaningful community structures. Explore the algorithmic music of Stephan Wolfram's computational universe, listen to pi or e or the Mona Lisa or the weather or the temperature in New York City, discover the shape of sound, or just, you know, see music. Use the Echo Nest to visualize your own music (example), tag your music collection with colours, or just wade through the plethora of ways to map connections between artists and genres. (several previously)
1980s Vinyl Multimedia In the 1980s UK, artists were busy embedding multimedia-enabling compiled computer code into the locked grooves of their vinyl releases (and some cassette tapes). Who knew?