How much is a song play worth?
Posted by Paul in Music, research, The Echo Nest on March 1, 2010
Over the last 15 years or so, music listening has moved online. Now instead of putting a record on the turntable or a CD in the player, we fire up a music application like iTunes, Pandora or Spotify to listen to music. One interesting side-effect of this transition to online music is that there is now a lot of data about music listening behavior. Sites like Last.fm can keep track of every song that you listen to and offer you all sorts of statistics about your play history. Applications like iTunes can phone home your detailed play history. Listeners leave footprints on P2P networks, in search logs and every time they hit the play button on hundreds of music sites. People are blogging, tweeting and IMing about the concerts they attend, and the songs that they love (and hate). Every day, gigabytes of data about our listening habits are generated on the web.
With this new data come the entrepreneurs who sample the data, churn though it and offer it to those who are trying to figure out how best to market new music. Companies like Big Champagne, Bandmetrics, Musicmetric, Next Big Sound and The Echo Nest among others offer windows into this vast set of music data. However, there’s still a gap in our understanding of how to interpret this data. Yes, we have vast amounts data about music listening on the web, but that doesn’t mean we know how to interpret this data- or how to tie it to the music marketplace. How much is a track play on a computer in London related to a sale of that track in a traditional record store in Iowa? How do searches on a P2P network for a new album relate to its chart position? Is a track illegally made available for free on a music blog hurting or helping music sales? How much does a twitter mention of my song matter? There are many unanswered questions about how online music activity correlates with the more traditional ways of measuring artist success such as music sales and chart position. These are important questions to ask, yet they have been impossible to answer because the people who have the new data (data from the online music world) generally don’t talk to the people who own the old data and vice versa.
We think that understanding this relationship is key and so we are working to answer these questions via a research consortium between The Echo Nest, Yahoo Research and UMG unit Island Def Jam. In this consortium, three key elements are being brought together. Island Def Jam is contributing deep and detailed sales data for its music properties – sales data that is not usually released to the public, Yahoo! Research brings detailed search data (with millions and millions of queries for music) along with deep expertise in analyzing and understanding what search can predict while The Echo Nest brings our understanding of Intenet music activity such as playcount data, friend and fan counts, blog mentions, reviews, mp3 posts, p2p activity as well as second generation metrics as sentiment analysis, audio feature analysis and listener demographics . With the traditional sales data, combined with the online music activity and search data the consortium hopes to develop a predictive model for music by discovering correlations between Internet music activity and market reaction. With this model, we would be able to quantify the relative importance of a good review on a popular music website in terms of its predicted effect on sales or popularity. We would be able to pinpoint and rank various influential areas and platforms on the music web that artists should spend more of their time and energy to reach a bigger fanbase. Combining anonymously observable metrics with internal sales and trend data will give keen insight into the true effects of the internet music world.
There are some big brains working on building this model. Echo Nest co-founder Brian Whitman (He’s a Doctor!) and the team from Yahoo! Research that authored the paper “What Can Search Predict” which looks closely at how to use query volume to forecast openining box-office revenue for feature films. The Yahoo! research team includes a stellar lineup: Yahoo! Principal research scientist Duncan Watts whose research on the early-rater effect is a must read for anyone interested in recommendation and discovery; Yahoo! Principal Research Scientist David Pennock who focuses on algorithmic economics (be sure to read Greg Linden’s take on Seung-Taek Park and David’s paper Applying Collaborative Filtering Techniques to Movie Search for Better Ranking and Browsing); Jake Hoffman, expert in machine learning and data-driven modeling of complex systems; Research Scientist Sharad Goel (see his interesting paper on Anatomy of the Long Tail: Ordinary People with Extraordinary Tastes) and Research Scientist Sébastien Lahaie, expert in marketplace design, reputation systems (I’ve just added his paper Applying Learning Algorithms to Preference Elicitation to my reading list). This is a top-notch team
I look forward to the day when we have a predictive model for music that will help us understand how this:
affects this:
.
LyricWiki + Musicbrainz == ‘awesome’
Two of my favorite public resources for music data: LyricWiki and MusicBrainz are now working together: LyricWiki and MusicBrainz integration! Congrats Sean and Robert!
Here comes the antiphon
Posted by Paul in Music, remix, The Echo Nest on February 25, 2010
I’m gearing up for the SXSW panel on remix I’m giving in a couple of weeks. I thought I should veer away from ‘science experiments’ and try to create some remixes that sound musical. Here’s one where I’ve used remix to apply a little bit of a pre-echo to ‘Here Comes the Sun’. It gives it a little bit of a call and answer feel:
The core (choir?) code is thus:
for bar in enumerate(self.bar):
cur_data = self.input[bar]
if last:
last_data = self.input[last]
mixed_data = audio.mix(cur_data, last_data, mix=.3)
out.append(mixed_data)
else:
out.append(cur_data)
last = bar
Echo Nest Client Library for the Android Platform
Posted by Paul in code, Music, The Echo Nest on February 24, 2010
The Echo Nest is participating in annual mobdev contest for the Mobile Application Development (mobdev) course at Olin College offered by Mark L. Chang. Already, our participation is bearing fruit. Ilari Shafer, one of our course assistants created a version of the Echo Nest Java client library that runs on Android. You can fetch it here: echo-nest-android-java-api [zip].
I spent a few hours yesterday talking to the mobdev class. The students had lots of great questions and lots of really interesting ideas on how to use the Echo Nest APIs to build interesting mobile apps. I can’t wait to see what they build in 10 days.
Organize your online music with ExtensionFM
On Friday I installed ExtensionFM– a chrome extension that helps you manage your online music listening. Dan Kantor, the creator, has a little video that shows you how it works:
The idea behind ExtensionFM is very simple. When I visit a site that has music ExtensionFM notices and squirrels away all of the links to the music into an iTunes like player:
It does all of this work in the background without me having to do anything. After a weekend of browsing, ExtensionFM found music on 20 sites from over 300 artists, over 400 albums – for a total of over 1,000 tracks. ExtensionFM remembers the sites where the music was from and keeps track of when the links die. Note that it doesn’t actually copy music onto your computer, ExtensionFM just makes it easier to play music that is already out there.
There are many nice touches in ExtensioFM. It keeps a play queue, and when you visit a music site you can easily add music to the queue.
You can edit the play queue easily adding and removing tracks from it.
ExtensionFM also augments a music laden site with music player buttons. So a site that looks like this:
is transformed into something like this:
Dan Kantor says he’ll be adding an option soon that will allow the disabling of this re-formatting for those who don’t like their web pages tampered with.
Unfortunately, ExtensionFM doesn’t always find music on a web page. Certain sites (Hype Machine for example) doesn’t expose Mp3 links so ExtensionFM can’t find the music. Dan says that right now ExtensionFM only grabs links that end in .mp3 or .ogg. It also works on Tumblr since they offer a very easy API to get a user’s audio posts. It is going to support Soundcloud embeds soon as well since they also offer an easy API. So the best way for developers to make sure their songs work with ExtensionFM is to make sure that the audio links are exposed in the html or to use Tumblr, or Soundcloud.
ExtensionFM is still in pre-release mode, but if you are lucky enough to get a release code, get the app, install it (it’s very easy to install), and start organizing your online music listening.
LastHistory – Visualizing Last.fm Listening Histories
Posted by Paul in fun, Music, visualization on February 16, 2010
This week Klaas, one of the researchers at Last.fm released to the Last.fm playground the ability to plot data from your personal listening history. (read about it here: Now in the Playground: Scrobbling Timelines).
You can look at when you started to listen to particular bands, or even compare your listening to one of your Last.fm friends (here you can see my cumulative listening as compared to my good Last.fm friend Neil Gaiman. It’s a really neat app that highlights the awesome listening data that Last.fm has been collecting for the last 6 or so years.
With the new Last.fm plots you can look at your listening history – but there’s a new app that takes this idea one step further. LastHistory, an application by Frederik Seiffert and Dominikus Baur from the Media Informatics Group of the University of Munich allows you to analyze music listening histories from Last.fm through an interactive visualization and to explore your own past by combining the music you listened to with your own photos and calendar entries. Like Klaas’s scrobbling graphs, LastHistory lets you browse music listening history, but LastHistory goes beyond that – it lets you interact with the visualization, allowing you to use your listening history for music exploration, and playlisting. And since the listening history can be any Last.fm listener, it is a great vehicle for music discovery too. The video makes it all really clear:
The integration with your iPhoto library is genius. While you listen to the music that you played in the car on that road trip to Tennessee in 2oo8 you can see a slide show of your photos from that same trip.
LastHistory runs on a Mac. When you run it for the first time, you tell it your last.fm name. It then goes to Last.fm to collect your listening history and info about all of the tracks. (This can take a few minutes depending on how long you’ve been listening at Last.fm). But even while it is retrieving your data you can start to interact with the data. And interacting with this application is very fun.
Each dot on the display represents a single song play at a point in the past. Mouse over the point to see the song name and to see other times when you played the song. Click on the song to hear it. The dots are colored by the genre (discovered by using the last.fm tags applied to the song). It is quite fun exploring my own listening history. Here’s the time when I first got the Weezer ‘Red’ Album:
This app is cool in so many ways, I know that I’m going to spend a lot of time playing with this app. But ff you try it out, remember that it is a 1.0 version. I did experience a crash or two, but it seemed to pick up where it left off without trouble. Oh yes, one more thing that moves this app from totally cool into über-cool is that it is all open source. Get the code here: LastHistory on Github. Congrats to Frederik and Dominikus for creating the first novel music exploration app of the decade. Nice job!
Python and Music at PyCon 2010
Posted by Paul in code, Music, The Echo Nest on February 15, 2010
If you are lucky enough to be heading to PyCon this week and are interested in hacking on music, there are two talks that you should check out:
DJing in Python: Audio processing fundamentals – In this talk Ed Abrams talks about how his experiences in building a real-time audio mixing application in Python. I caught a dry-run of this talk at the local Python SIG – lots of info packed into this 30 minute talk. One of the big takeaways from this talk is the results of Ed’s evaluation of a number of Pythonic audio processing libraries. Sunday 01:15pm, Centennial I
Remixing Music Pythonically – This is a talk by Echo Nest friend and über-developer Adam Lindsay. In this talk Adam talks about the Echo Nest remix library. Adam, a frequent contributor to remix, will offer details on the concise expressiveness offered when editing multimedia driven by content-based features, and some insights on what Pythonic magic did and didn’t work in the development of the modules. Audio and video examples of the fun-yet-odd outputs that are possible will be shown. Sunday 01:55pm, Centennial I
The schedulers at PyCon have done a really cool thing and have put the talks back to back in the same room. Also, keep your eye out for the Hacking on Music OpenSpace
Name That Artist
Posted by Paul in fun, Music, The Echo Nest, web services on February 14, 2010
While watching the Olympics over the weekend, I wrote a little web-app game that uses the new Echo Nest get_images call. The game is dead simple. You have to identify the artists in a series of images. You get to chose a level of difficulty and the style of your favorite music, and if you get a high score, your name and score will appear on the Top Scores board. Instead of using a simple score of percent correct, the score gets adjusted by a number of factors. There’s a time bonus, so if you answer fast you get more points, there’s a difficulty bonus, so if you identify unfamiliar artists you get more points, and if you chose the ‘Hard’ level of difficulty you get also get more points for every correct answer. The absolute highest score possible is 600 but that any score above 200 is rather awesome.
The app is extremely ugly (I’m a horrible designer), but it is fun – and it is interesting to see how similar artists from a single genre appear. Give it a go, post some high scores and let me know how you like it.
Jason’s cool screensaver
Posted by Paul in code, fun, Music, The Echo Nest on February 13, 2010
I noticed some really neat images flowing past Jason’s computer over the last week. Whenever Jason was away from his desk, our section of the Echo Nest office would be treated to a very interesting slideshow – mostly of musicians (with an occasional NSFW image (but hey, everything is SFW here at The Echo Nest)). Since Jason is a photographer I first assumed that these were pictures that he took of friends or shows he attended – but Jason is a classical musician and the images flowing by were definitely not of classical musicians – so I was puzzled enough to ask Jason about it. Turns out, Jason did something really cool. He wrote a Python program that gets the top hotttt artists from the Echo Nest, and then collects images for all of those artists and their similars – yielding a huge collection of artist images. He then filters them to include only high res images (thumbnails don’t look great when blown up to screen saver size). He then points is Mac OS Slideshow screensaver at the image folder and voilá – a nifty music-oriented screensaver.
Jason has added his code to the pyechonest examples. So if you are interested in having a nifty screen saver, grab Pyechonest, get an Echo Nest API key if you don’t already have one and run the get_images example. Depending upon how many images you want, it may take a few hours to run. To get 100K images plan to run it over night. Once you’ve done that, point your Pictures screensaver at the image folder and you’re done.
Music Machinery 