Archive for category visualization
Visualizing the active years of popular artists
Posted by Paul in data, Music, The Echo Nest, visualization on June 21, 2011
This week the Echo Nest is extending the data returned for an artist to include the active years for an artist. For thousands of artists you will be able to retrieve the starting and ending date for an artists career. This may include multiple ranges as groups split and get back together for that last reunion tour. Over the weekend, I spent a few hours playing with the data and built a web-based visualization that shows you the active years for the top 1000 or so hotttest artists.
The visualization shows the artists in order of their starting year. You can see the relatively short careers of artists like Robert Johnson and Sam Cooke, and the extremely long careers of artists like The Blind Boys of Alabama and Ennio Morricone. The color of an artist’s range bar is proportional to the artist’s hotttnesss. The hotter the artist, the redder the bar. Thanks to 7Digital, you can listen to a sample of the artist by clicking on the artist. To create the visualization I used Mike Bostock’s awesome D3.js (Data Driven Documents) library.
It is fun to look at some years active stats for the top 1000 hotttest artists:
- Average artist career length: 17 years
- Percentage of top artists that are still active: 92%
- Longest artist career: The Blind Boys of Alabama - 73 Years and still going
- Gone but not forgotten – Robert Johnson – Hasn’t recorded since 1938 but still in the top 1,000
- Shortest Career – Joy Division – Less than 4 Years of Joy
- Longest Hiatus – The Cars – 22 years – split in 1988, but gave us just what we needed when they got back together in 2010
- Can’t live with’em, can’t live without ‘em - Simon and Garfunkel – paired up 9 separate times
- Newest artist in the top 1000 – Birdy – First single released in March 2011
Check out the visualization here: Active years for the top 1000 hotttest artists and read more about the years-active support on the Echo Nest blog
Masters of Media » Music visualizations as a means for discovery
Posted by Paul in Music, visualization on April 15, 2011
Nifty blog post by Megan Adams about using music visualization as a means for discovery:
Masters of Media » Music visualizations as a means for discovery.
Megan points to this thread on Tufte’s BBS about Reebee Garofalo’s ‘Genealogy of Pop/Rock Music”:
The comments in the BBS thread point to a number of visualizations that I hadn’t seen before including: this book on Rock Family Trees along with this incredibly detailed view of the Liverpool music scene.
Finding Music With Pictures – The Video
Posted by Paul in events, Music, visualization on March 28, 2011
I gave a panel at SXSWi this year on using data visualization for music discovery. Mike Hochanadel made a video of the talk and has posted it online.
Be sure to check out the rest of Mike’s blog hoketronics – he has solid coverage on many of the SXSWi panels.
Finding music with pictures: Data visualization for discovery
Posted by Paul in data, Music, visualization on March 13, 2011
I just finished giving my talk at SXSW called – ‘Finding Music with Pictures”. A few people asked for the slides - I’ve posted them to Slideshare. Of course all the audio and video is gone, but you can follow the links to see the vids. Here are the slides:
Lots of good tweets from the audience. And Hugh Garry has Storify’d the talk.
The SXSW Music Maze
Posted by Paul in code, fun, visualization on March 10, 2011
There are thousands of artists playing at SXSW this year. To help sort it all out, I thought I’d adapt my Music Maze to work within the world of SXSW 2011 artists. It is a good way to figure out which bands you’d like to see.
This visualization fits in with the SXSW talk I’m giving in a few days: Finding Music With Pictures
What’s your favorite music visualization for discovery?
Posted by Paul in Music, visualization on February 28, 2011
In a couple of weeks I’m giving a talk at SXSW called Finding Music with pictures : Data visualization for discovery. In this panel I’ll talk about how visualizations can be used to help people explore the music space and discover new, interesting music that they will like. I intend to include lots of examples both from the commercial world as well as from the research world.
Ishkur's guide to electronic music - One of my favorite visualizations for discovery
I’ll be drawing material from many sources including the Tutorial that Justin and I gave at ISMIR in Japan in October 2009: Using visualizations for music discovery. Of course lots of things have happened in the year and a half since we put together that tutorial such as iPads, HTML5, plus tons more data availability. If you happen to have a favorite visualization for music discovery, post a link in the comments or send me an email: paul [at] echonest.com.
The 3D Music Explorer
Posted by Paul in events, Music, visualization on January 27, 2011
Next month I’m giving a talk at SXSW Interactive on using visualizations for discovering music. In my talk I’ll be giving a number of demos of various types of visualizations used for music exploration and discovery. One of the demos is an interactive 3D visualizer that I built a few years back. The goal of this visualizer is to allow you to use 3D game mechanics to interact with your music collection. Here’s a video
Hope to see you at the talk.
Six Clicks to Imogen
Posted by Paul in events, fun, Music, visualization on January 25, 2011
For my weekend Music Hack Day hack I built in app called Six Clicks to Imogen. The hack is a game where the goal is to find the shortest path from a randomly selected artist to Imogen Heap.
To build the hack I used the Musicbrainz artist relationship data to find all the artist connections, and plotted the graph with the JavaScript Infoviz toolkit . The game has about 55,000 artist nodes that are connected to Imogen by millions of artist relation ship edges. The hack is live, so go ahead a play the game:
Thanks much to Hannah for contributing excellent design suggestions for the app.
The Labyrinth of Genre
Posted by Paul in code, data, tags, The Echo Nest, visualization on January 16, 2011
I’m fascinated with how music genres relate to each other, especially how one can use different genres as stepping stones as a guide through the vast complexities of music. There are thousands of genres, some like rock or pop represent thousands of artists, while some like Celtic Metal or Humppa may represent only a handful of artists. Building a map by hand that represents the relationships of all of these genres is a challenge. Is Thrash Metal more closely related to Speed Metal or to Power Metal? To sort this all out I’ve built a Labyrinth of Genre that lets you explore the many genres. The Labyrinth lets you wander though about a 1000 genres, listening to samples from representative artists.
Click on a genre and the labyrinth will be expanded to show similar half a dozen similar genres and you’ll hear songs in the genre.
I built the labyrinth by analyzing a large collection of last.fm tags. I used the cosine distance of tf-idf weighted tagged artists as a distance metric for tags. When you click on a node, I attach the six closest tags that haven’t already been attached to the graph. I then use the Echo Nest APIs to get all the media.
Even though it’s a pretty simple algorithm, it is quite effective in grouping similar genre. If you are interested in wandering around a maze of music, give the Labyrinth of Genre a try.
A Genre Map
Inspired by an email exchange with Samuel Richardson, creator of ‘Know your genre‘ I created a genre map that might serve as a basis for a visual music explorer (perhaps something to build at one of the upcoming music hack days). The map is big and beautiful (in a geeky way). Here’s an excerpt, click on it to see the whole thing.
Update – I’ve made an interactive exploration tool that lets you wander through the genre graph. See the Labyrinth of Genre
Update 2 – Colin asked the question “What’s the longest path between two genres?” – If I build the graph by using the 12 nearest neighbors to each genre, find the minimum spanning tree for that graph and then find the longest path, I find this 31 step wonder:
Of course there are lots of ways to skin this cat – if I build the graph with just the nearest 6 neighbors, and don’t extract the minimum spanning tree, the longest path through the graph is 10 steps:
