Music Machinery - Paul Lamere - Echonest
Do you use Smart Playlists?
Fri, 07/30/2010 - 13:41 iTunes Smart Playlists allow for very flexible creation of dynamic playlists based on a whole boat-load of parameters. But I wonder how often people use this feature. Is it too complicated? Let’s find out. I’ve created a poll that will take you about 20 seconds to complete. Go to iTunes, count up how many smart playlists you have. You can tell which playlists are smart playlists because they have the little gear icon:Don’t count the pre-fab smart playlists that come with iTunes (like 90′s music, Recently Added, My Top Rated, etc.). Once you’ve counted up your playlists, take the poll:
Categories: Music Rec
The next music tastemakers – the computer programmers
Thu, 07/29/2010 - 16:32There’s an interesting piece in the New Yorker about the future of listening. The article focuses on Pandora and MOG and the challenges of making the online listening experience. Author Sasha Frere-Jones concludes with this:
While using these services, I kept thinking about an early-eighties drum machine called the Roland TR-808, which has seduced generations of musicians with its heavy kick-drum sound and the oddly human swing of its clock. Whoever programmed this box had more impact on dance music than the hundreds of better-known musicians who used the device. Similarly, the anonymous programmers who write the algorithms that control the series of songs in these streaming services may end up having a huge effect on the way that people think of musical narrative—what follows what, and who sounds best with whom. Sometimes we will be the d.j.s, and sometimes the machines will be, and we may be surprised by which we prefer.
Read the article:
You, the D.J. Online music moves to the cloud.
Categories: Music Rec
Help researchers understand earworms
Thu, 07/29/2010 - 14:20Researchers at Goldsmiths, University of London, in a collaboration with the BBC 6 and the British Academy, are conducting research to find out about the music in people’s heads, sometimes called ’musical imagery’. They want to know what songs are the most common, whether people like it or don’t, what triggers it, and if some people have music in their head all the time, etc.
To help researchers understand this phenomenon, take part in a questionnaire (and you could win £150 too). I took the survey, it took about 10 minutes. They do ask some rather personal questions that seem related to one’s tendency towards compulsive behavior. (yes, I do sometimes count the stairs that I’m walking up).
It looks to be an interesting research project. More details about it are here: The Earwomery.com
Categories: Music Rec
Visual Music
Wed, 07/28/2010 - 10:47The week long Visual Music Collaborative Workshop held at the Eyebeam just finished up. This was an invite-only event where participants did a deep dive into sound analysis techniques, openGL programming, and interfacing with mobile control devices.
Here’s one project built during the week that uses The Echo Nest analysis output:
(Via Aaron Meyers)
Categories: Music Rec
Novelty playlist ordering
Sun, 07/25/2010 - 13:50We’ve been building a new playlisting engine here at the Echo Nest. The engine is really neat – it lets you apply a whole range of very flexible constraints and orderings to make all sorts of playlists that would be a challenge for even the most savvy DJ. Playlists like 15 songs with a tempo between 120 and 130 BPM ordered by how danceable they are by very popular female artists that sound similar to Lady Gaga, that live near London, but never ever include tracks by The Spice Girls.
I was playing with the engine this weekend, writing some rules to make novelty playlists to test the limits of the engine. I started with rules typical for a similar-artist playlist: 15 songs long, filled with songs by artists similar to a seed artist (in this case Weezer), the first and last song must be by the seed artist, and no two consecutive songs can be by the same artist. Simple enough, but then I added two more rules to turn this into a novelty playlist that would be very hard for a human to make. See if you can guess what the two rules are. I think one of the rules is pretty obvious, but the second is a bit more subtle. Post your guesses in the comments.
0 Tripping Down the Freeway - Weezer 1 Yer All I've Got Ttonight - The Smashing Pumpkins 2 The Most Beautiful Things - Jimmy Eat World 3 Someday You Will Be Loved - Death Cab For Cutie 4 Don't Make Me Prove It - Veruca Salt 5 The Sacred And Profane - Smashing Pumpkins, The 6 Everything Is Alright - Motion City Soundtrack 7 The Ego's Last Stand - The Flaming Lips 8 Don't Believe A Word - Third Eye Blind 9 Don's Gone Columbia - Teenage Fanclub 10 Alone + Easy Target - Foo Fighters 11 The Houses Of Roofs - Biffy Clyro 12 Santa Has a Mullet - Nerf Herder 13 Turtleneck Coverup - Ozma 14 Perfect Situation - Weezer
Here’s another playlist – with a different set of two novelty rules, with a seed artist of Led Zeppelin. Again, if you can guess the rules, post a comment.
0 El Niño - Jethro Tull 1 Cheater - Uriah Heep 2 Hot Dog - Led Zeppelin 3 One Thing - Lynyrd Skynyrd 4 Nightmare - Black Sabbath 5 Ezy Ryder - The Jimi Hendrix Experience 6 Soulshine - Govt Mule 7 The Gypsy - Deep Purple 8 I'll Wait - Van Halen 9 Slow Down - Ozzy Osbourne 10 Civil War - Guns N' Roses 11 One Rainy Wish - Jimi Hendrix 12 Overture (Live) - Grand Funk Railroad 13 Larger Than Life - Gov'T MuleCategories: Music Rec
The Music App Summit
Fri, 07/23/2010 - 13:30Billboard has long been known for tracking the hottest artists, albums and songs. Now they are moving into new territory – Music Apps. In October Billboard is hosting a Music App Summit – a day focused on the world of mobile music apps. The summit will focus on new companies and technologies that are now building the next generation of music applications for mobile devices. The summit has some awesome speakers and panelist lined up from a cross section of domains (technology, business and music) like Ge Wang, Ted Cohen, Dave Kusek, Brian Zisk and The Echo Nest’s CEO Jim Lucchese.
At the core of the summit are Billboard’s first ever Music App Awards. Billboard is giving awards to the best apps in a number of categories:
- Best Artist-based App: Apps created specifically for an individual artist
- Best Music Streaming App: Apps that allow users to stream, download or otherwise enjoy music, such as Internet radio or on-demand.
- Best Music Engagement App: Apps that lets users engage in music in various ways, such as music games, music ID services, etc.
- Best Music Creation App: App that lets users make their own music.
- Best Branded App: App that best incorporates a sponsor with music capabilities to promote both the sponsor’s message and highlight the music
- Best Touring App: App created in conjunction with a specific tour or festival
Judges for the apps include Eliot Van Buskirk of Wired, Ian Rogers of Top Spin and Grammy Award winner MC Hammer.
Winning developers receive some modest prizes – but the real award is getting to demo your app to the attendees of the summit – the movers and shakers of the music industry will be there looking for that killer music app – the winner in each of the app categories will get to show their stuff. If you have a mobile music app consider submitting it to the Music App Awards. The submission deadline is July 30.
Categories: Music Rec
Keith Moon meets Animal
Thu, 07/22/2010 - 13:39Another vafromb.py masterpiece from joshmillard.
Categories: Music Rec
Echo Nest Remix at the Boston Python Meetup Group
Thu, 07/15/2010 - 19:15Next week I’ll be giving a talk about remixing music with Echo Nest remix at the Boston Python Meetup Group. If you are in the Boston / Cambridge area next week, be sure to come on by and say ‘hi’. Info and RSVP for the talk are here: The Boston Python Meetup Group on Meetup.com
Here’s the abstract for the talk:
Paul Lamere will tell us about Echo Nest remix. Remix is an open source Python library for remixing music. With remix you can use Python to rearrange a track, combine it with others, beat/pitch shift it etc. – essentially it lets you treat a song like silly putty.
The Swinger is an interesting example of what it can do that made the rounds of the blogosphere: it morphs songs to give them a swing rhythm.
For more details about the type of music remixing you can do with remix, feel free to read: http://musicmachinery…
Categories: Music Rec
Some NameDropper stats
Sun, 07/11/2010 - 17:01The NameDropper has been live for less than a day and already I ‘ve collected some good data from the game play. Here are some stats:
Total games played: 1841
Total unique players: 462
Total play time: 30hrs, 20mins, 36 seconds
The artists that were most frequently confused with fake artists were:
93 Erik Satie 82 Ennio Morricone 66 Edvard Grieg 49 Nightwish 46 Yann Tiersen 40 Maurice Ravel 35 Porcupine Tree 34 Felix Mendelssohn 32 Dinah Washington 30 Antonio Vivaldi This is showing that the game (and the underlying familiarity algorithm that drives it) may have a bias toward classical musicians. One of the knobs we have in our familiarity algorithm is a boost for age of the artist (even though Beethoven doesn’t get played on the radio very often, most people have heard of him, so he gets a boost for being old). But this early data suggests that we are giving a bit too much of an age boost. Ideally, the most frequently confused artists would be spread evenly across genre. The fact that there are no Hip-Hop or Pop artists on this list suggests that our familiarity algorithm skews away from those artists. Looks like we’ll have a few knobs to tweak. Game-with-a-purpose are a great way to get data from humans that would otherwise be difficult to get. The challenge in creating such a game though is to make one that is actually fun to play. If your game is no fun, you won’t get any data at all. Looking at player stats can give us an idea of how fun the game is. Some NameDropper player stats:Games per player: Average: 4 Median: 2 Max: 182 (woah, someone played 182 times!) Time spent playing: Average: 236 secs Median: 90 secs Max: 10,995 secs (over 3 hours) Scores: Average: 9139 Median: 4054 Min: -4656 Max: 54671
Yep, someone scored -4656 points. If you take too long to answer you get dinged (to avoid the Wikipedia winner). I was a bit concerned as to whether or not someone could ‘learn’ the game (my daughter Cari suggested as much), so I looked at the scores over time of one of the frequent players to see if the score trended upwards. There doesn’t seem to be any noticeable trend for this user, so it seems that the game is perhaps a good measure of general music knowledge and not a measure of how may times you’ve played the game. People are still playing the game – and there seems to be a good fight for the top spot on the leaderboard which means that I should continue to get good data. Next step is to tweak the familiarity knobs based on the data collected so far and update the game to see if the average scores go up.
Categories: Music Rec
The Name Dropper
Sat, 07/10/2010 - 19:03TL;DR; I built a game called Name Dropper that tests your knowledge of music artists.
One bit of data that we provide via our web APIs is Artist Familiarity. This is a number between 0 and 1 that indicates how likely it is that someone has heard of that artists. There’s no absolute right answer of course – who can really tell if Lady Gaga is more well known than Barbara Streisand or whether Elvis is more well known than Madonna. But we can certainly say that The Beatles are more well known, in general, than Justin Bieber.
To make sure our familiarity scores are good, we have a Q/A process where a person knowledgeable in music ranks our familiarity score by scanning through a list of artists ordered in descending familiarity until they start finding artists that they don’t recognize. The further they get into the list, the better the list is. We can use this scoring technique to rank multiple different familiarity algorithms quickly and accurately.
One thing I noticed, is that not only could we tell how good our familiarity score was with this technique, this also gives a good indication of how well the tester knows music. The further a tester gets into a list before they can’t recognize artists, the more they tend to know about music. This insight led me to create a new game: The Name Dropper.
The Name Dropper is a simple game. You are presented with a list of dozen artist names. One name is a fake, the rest are real.
If you find the fake, you go onto the next round, but if you get fooled, the game is over. At first, it is pretty easy to spot the fakes, but each round gets a little harder, and sooner or later you’ll reach the point where you are not sure, and you’ll have to guess. I think a person’s score is fairly representative of how broad their knowledge of music artists are.
The biggest technical challenge in building the application was coming up with a credible fake artist name generator. I could have used Brian’s list of fake names – but it was more fun trying to build one myself. I think it works pretty well. I really can’t share how it works since that could give folks a hint as to what a fake name might look like and skew scores (I’m sure it helps boost my own scores by a few points). The really nifty thing about this game is it is a game-with-a-purpose. With this game I can collect all sorts of data about artist familiarity and use the data to help improve our algorithms.
So go ahead, give the Name Dropper a try and see if you can push me out of the top spot on the leaderboard:
Play the Name Dropper
Categories: Music Rec
Some preliminary Playlist Survey results
Mon, 06/21/2010 - 14:05I’m conducting a somewhat informal survey on playlisting to compare how well playlists created by an expert radio DJ compare to those generated by a playlisting algorithm and a random number generator. So far, nearly 200 people have taken the survey (Thanks!). Already I’m seeing some very interesting results. Here’s a few tidbits (look for a more thorough analysis once the survey is complete).
People expect human DJs to make better playlists:
The survey asks people to try to identify the origin of a playlist (human expert, algorithm or random) and also rate each playlist. We can look at the ratings people give to playlists based on what they think the playlist origin is to get an idea of people’s attitudes toward human vs. algorithm creation.
We see that people expect humans to create better playlists than algorithms and that algorithms should give better playlists than random numbers. Not a surprising result.
Human DJs don’t necessarily make better playlists:
Now lets look at how people rated playlists based on the actual origin of the playlists:
These results are rather surprising. Algorithmic playlists are rated highest, while human-expert-created playlists are rated lowest, even lower than those created by the random number generator. There are lots of caveats here, I haven’t done any significance tests yet to see if the differences here really matter, the survey size is still rather small, and the survey doesn’t present real-world playlist listening conditions, etc. Nevertheless, the results are intriguing.
I’d like to collect more survey data to flesh out these results. So if you haven’t already, please take the survey:
The Playlist SurveyThanks!
Categories: Music Rec
The Playlist Survey
Fri, 06/18/2010 - 12:34Playlists have long been a big part of the music experience. But making a good playlist is not always easy. We can spend lots of time crafting the perfect mix, but more often than not, in this iPod age, we are likely to toss on a pre-made playlist (such as an album), have the computer generate a playlist (with something like iTunes Genius) or (more likely) we’ll just hit the shuffle button and listen to songs at random. I pine for the old days when Radio DJs would play well-crafted sets – mixes of old favorites and the newest, undiscovered tracks – connected in interesting ways. These professionally created playlists magnified the listening experience. The whole was indeed greater than the sum of its parts.
The tradition of the old-style Radio DJ continues on Internet Radio sites like Radio Paradise. RP founder/DJ Bill Goldsmith says of Radio Paradise: “Our specialty is taking a diverse assortment of songs and making them flow together in a way that makes sense harmonically, rhythmically, and lyrically — an art that, to us, is the very essence of radio.” Anyone who has listened to Radio Paradise will come to appreciate the immense value that a professionally curated playlist brings to the listening experience.
I wish I could put Bill Goldsmith in my iPod and have him craft personalized playlists for me - playlists that make sense harmonically, rhythmically and lyrically, and customized to my music taste, mood and context . That, of course, will never happen. Instead I’m going to rely on computer algorithms to generate my playlists. But how good are computer generated playlists? Can a computer really generate playlists as good as Bill Goldsmith, with his decades of knowledge about good music and his understanding of how to fit songs together?
To help answer this question, I’ve created a Playlist Survey – that will collect information about the quality of playlists generated by a human expert, a computer algorithm and a random number generator. The survey presents a set of playlists and the subject rates each playlist in terms of its quality and also tries to guess whether the playlist was created by a human expert, a computer algorithm or was generated at random.
Bill Goldsmith and Radio Paradise have graciously contributed 18 months of historical playlist data from Radio Paradise to serve as the expert playlist data. That’s nearly 50,000 playlists and a quarter million song plays spread over nearly 7,000 different tracks.
The Playlist Survey also servers as a Radio DJ Turing test. Can a computer algorithm (or a random number generator for that matter) create playlists that people will think are created by a living and breathing music expert? What will it mean, for instance, if we learn that people really can’t tell the difference between expert playlists and shuffle play?
Ben Fields and I will offer the results of this Playlist when we present Finding a path through the Jukebox – The Playlist Tutorial – at ISMIR 2010 in Utrecth in August. I’ll also follow up with detailed posts about the results here in this blog after the conference. I invite all of my readers to spend 10 to 15 minutes to take The Playlist Survey. Your efforts will help researchers better understand what makes a good playlist.
Take the Playlist SurveyCategories: Music Rec
MeToo – a scrobbler for the room
Fri, 06/11/2010 - 10:29One of the many cool things about working at the Echo Nest is that we have an Sonos audio system with single group playlist for the office. Anyone from the CEO to the greenest intern can add music to the listening queue for everyone to listen to. The office, as a whole has a rather diverse taste in music and as a result I’ve been exposed to lots of interesting music. However, the downside of this is that since I’m not listening to music being played on my personal computer, every day I have 10 hours of music listening that is never scrobbled, and as they say, if it doesn’t scrobble, it doesn’t count. Sure the Sonos system scrobbles all of the plays to the Echo Nest account on Last.fm but I’d also like it to scrobble it to my account so I can use nifty apps like Lee Byron’s Last.fm Listening History or Matt Ogle’s Bragging Rights on my own scrobbles.
This morning while listening to that nifty Emeralds album, I decided that I’d deal with those scrobble gaps once and for all. So I wrote a little python script called MeToo that keeps my scrobbles up to date. It’s really quite simple. Whenever I’m in the office, I fire up MeToo. MeToo watches the most recent tracks played on The Echo Nest account and whenever a new track is played, it scrobbles it to my personal account. In effect, my scrobbles will track the office scrobbles. When I’m not listening I just close my laptop and the scrobbling stops.
The script itself is pretty simple – I used pylast to do interfacing to Last.fm – the bulk of the logic is less than 20 lines of code. I start the script like so:
% python metoo.py TheEchoNest lamerewhen I do that, MeToo will continuously monitor most recently played tracks on TheEchoNest and scrobble the plays on my account. When I close my laptop, the script is naturally suspended – so even though music may continue to play in the office, my laptop won’t scrobble it.
My scrobbles and Echo Nest scrobbles
I suspect that this use case is relatively rare, and so there’s probably not a big demand for something like MeToo, but if you are interested in it, leave a comment. If I see some interest, I’ll toss it up on google code so anyone can use it.
It feels great to be scrobbling again!
Categories: Music Rec
We swing both ways
Mon, 06/07/2010 - 13:00Perhaps one of the most frequently asked questions about Tristan’s Swinger is whether it can be used to ‘Un-swing’ a song. Can you take a song that already swings and straighten it out? Indeed, the answer is yes – we can swing both ways - but it is harder to unswing than it is to swing. Ammon on Happy Blog, the Happy Blog has given de-swinging a go with some success with his de-swinging of Revolution #1. Read his post and have a listen at Taking the swing out of songs. I can’t wait for the day when we can turn on the TV to watch and listen to MTV-Unswung.
Categories: Music Rec
Frasier does Nine Inch Nails
Sat, 06/05/2010 - 14:31Oh My – Musician Josh Millard has recreated The Downward Spiral using nothing but audio from the NBC sitcom Frasier. So wrong, and yet, so right. Josh has the whole remixed album plus a video on his blog:
Nine Inch Niles – The Seattleward Spiral
Categories: Music Rec
Is Music Recommendation Broken? How can we fix it?
Tue, 06/01/2010 - 20:50Save the date: 26th September 2010 for The Workshop on Music Recommendation and Discovery being held in conjunction with ACM RecSys in Barcelona, Spain. At this workshop, community members from the Recommender System, Music Information Retrieval, User Modeling, Music Cognition, and Music Psychology can meet, exchange ideas and collaborate.
Topics of interestTopics of interest for Womrad 2010 include:
- Music recommendation algorithms
- Theoretical aspects of music recommender systems
- User modeling in music recommender systems
- Similarity Measures, and how to combine them
- Novel paradigms of music recommender systems
- Social tagging in music recommendation and discovery
- Social networks in music recommender systems
- Novelty, familiarity and serendipity in music recommendation and discovery
- Exploration and discovery in large music collections
- Evaluation of music recommender systems
- Evaluation of different sources of data/APIs for music recommendation and exploration
- Context-aware, mobile, and geolocation in music recommendation and discovery
- Case studies of music recommender system implementations
- User studies
- Innovative music recommendation applications
- Interfaces for music recommendation and discovery systems
- Scalability issues and solutions
- Semantic Web, Linking Open Data and Open Web Services for music recommendation and discovery
More info: Wormrad 2010 Call for papers
Categories: Music Rec
What is the longest path though Six Degrees of Black Sabbath?
Tue, 05/25/2010 - 12:54@meekles tweeted yesterday that he had found a 25 step path through Six Degrees of Black Sabbath and challenged anyone to find a longer path that his Path from Arthur to Eivind Fjoseide.
To sweeten the challenge, I’ll offer a prize of a coveted Echo Nest Tee Shirt for each new longest path found. Here are the rules:
- When you find a path that you think is longer than any found so far, tweet the path with its length and the hashtags #6dobs and #longest. For example: I made a 25 step path from arthur to Eivind Fjoseide #6dobs #longest http://bit.ly/9DANCk
- No skipping allowed in longest paths
- Only one tee-shirt given per milestone – so if 5 people find a 27 step path, only the first who finds it gets the tee-shirt
- Only one tee-shirt per person
- You are not eligible if you work for the Echo Nest, or if your name is Kurt Jacobson
The Echo Nest tee-shirt will turn you into a music metadata superhero
Have fun finding those paths!
Update: Great work finding paths of at least 40 artists long. Tee-shirt give away ends tonight (May 25) at midnight EDT!
Categories: Music Rec
Sweet Child O’Mine – Vienna Style
Mon, 05/24/2010 - 13:17I was wondering how far one could go with the time-stretching stuff and still make something musical. Here’s an attempt to turn a rock anthem into a waltz. It is a bit rough in a few places, especially the beginning – but I think it settles into a pretty nice groove.
Categories: Music Rec
The Swinger
Fri, 05/21/2010 - 11:37One of my favorite hacks at last weekend’s Music Hack Day is Tristan’s Swinger. The Swinger is a bit of python code that takes any song and makes it swing. It does this be taking each beat and time-stretching the first half of each beat while time-shrinking the second half. It has quite a magical effect. Some examples:
Every Breath You Take
Money for Nothing
Cream
I Will
Update – a few more tracks -by request:
Enter Sandman
Daft Punk’s Around the world
Sweet Child O’ Mine
(one of my favs)
Don’t Stop Believin’
White Rabbit
(this one is hypnotic)
Swinger uses the new Dirac time-stretching capabilities of Echo Nest remix. Source code is available in the samples directory of remix.
Be sure to check out some of the other Music Hack Day hacks like Six Degrees of Black Sabbath, Jason’s Songbird Visualizer or the Artikulator.
Categories: Music Rec
Six Degrees of Black Sabbath
Thu, 05/20/2010 - 13:11My hack at last week’s Music Hack Day San Francisco was Six Degrees of Black Sabbath - a web app that lets you find connections between artists based on a wide range of artist relations. It is like The Oracle of Bacon for music.
To make the connections between the artists I rely on the relation data from MusicBrainz. MusicBrainz has lots of deep data about how various artists are connected. For instance there are about 130,000 artist-to-artist connections – connections such as:
- member of band
- is person
- personal relationship
- parent
- sibling
- married
- involved with
- collaboration
- supporting musician
- vocal supporting musician
- instrumental supporting musician
- catalogued
So from this data we know that George Harrison and Paul McCartney are related because each was a ‘member of the band’ of The Beatles. In addition to the artist-to-artist data MusicBrainz has artist-track relations (Eric Clapton played on ‘While My Guitar Gently Weeps’), artist-album (Brian Eno produced U2′s Joshua Tree), track-track (Girl Talk samples ‘Rock You Like A Hurricane’ by the Scorpions for the track ‘Girl Talk Is Here’). All told there are about 130 different types of relations that can connect two artists.
Not all of these relationships are equally important. Two artists that are members of the same band have a much stronger relationship than an artist that covers another artist. To accommodate this I assign weights to the various different types of relationships – this was perhaps the most tedious and subjective part of building this app.
Once I have all the different types of relations I created a directed graph connecting all of the artists based upon these weighted relationships. The resulting graph has 220K artists connected by over a million edges. Finding a path between a pair of artists is a simple matter of finding the shortest weighted path through the graph.
We can learn a little bit about music by looking at some of the properties of the graph. First of all, the average distance in the graph between any two artists in the graph chosen at random is 7. Some of the top most connected artists along with the number of connections:
-
- 5372 Various Artists
- 1604 Wolfgang Amadeus Mozart
- 1275 Johann Sebastian Bach
- 905 Ludwig van Beethoven
- 696 Linda Ronstadt
- 611 Diana Ross
- 560 [traditional]
- 538 Antonio Vivaldi
- 534 Jay-Z
- 528 Georg Friedrich Händel
- 494 Giuseppe Verdi
- 491 Johannes Brahms
- 490 Bob Dylan
- 465 The Beatles
- 442 Aaron Neville
Here we see some of the anomalies in the connection data - any classical performer who performs a piece by Mozart is connected to Mozart – thus the high connectivity counts for classical composers. A more interesting metric is the ‘betweeness centrality’ – artists that occur on many shortest paths between other artists have higher betweenness than those that do not. Artists with high betweenness centrality are the connecting fibers of the music space. Here are the top connecting artists:
-
- 565 Pigface
- 312 Various Artists
- 135 Mick Harris
- 122 Black Sabbath
- 120 The The
- 115 Youth
- 93 Bill Laswell
- 79 J.G. Thirlwell
- 74 Painkiller
- 72 F.M. Einheit
- 71 Napalm Death
- 63 Paul McCartney
- 63 Flea
- 60 Material
- 60 Andrew Lloyd Webber
- 57 Luciano Pavarotti
- 57 Raimonds Macats
- 56 Ginger Baker
- 56 Mike Patton
- 54 Johnny Marr
- 54 Paul Raven
- 53 Brian Eno
I had never heard of Pigface before I started this project – and was doubtful that they could really be such a connecting node in the world of music – but a look a their wikipedia page makes it instantly clear why they are such a central node – they’ve had well over a hundred members in the band over their history. Black Sabbath, while not at the top of the list is still extremely well connected.
I wrote the app in python, relying on networkx for the graph building and path finding. The system performs well, even surviving an appearance on the front page of Reddit. It was a fun app to write – and I enjoy seeing all the interesting pathways people have found through the artist space.
Categories: Music Rec
