Posts Tagged Music
I’ve started to build the ultimate list of music APIs. My goal for the list is for it to be a one-stop spot to find the best music apis. Currently 65 APIs are listed across 10 categories. Check out the list here: Music APIs
I still remember the evening well. It was midnight during the summer of 1982. I was living in a thin-walled apartment, trying unsuccessfully to go to sleep while the people who lived upstairs were music bingeing on The B52′s Rock Lobster. They listened to the song continuously on repeat for hours, giving me the chance to ponder the rich world of undersea life, filled with manta rays, narwhals and dogfish.
We tend to binge on things we like – potato chips, Ben & Jerry’s, and Battlestar Galactica. Music is no exception. Sometimes we like a song so much, that as soon as it’s over, we want to hear it again. But not all songs are equally replayable. There are some songs that have some secret mysterious ingredients that makes us want to listen to the song over and over again. What are these most replayed songs? Let’s look at some data to find out.
The Data - For this experiment I used a week’s worth of song play data from the summer of 2013 that consists of user / song / play-timestamp triples. This data set has on the order of 100 million of these triples for about a half million unique users and 5 million unique songs. To find replays I looked for consecutive plays by a user of song within a time window (to ensure that the replays are in the same listening session). Songs with low numbers of plays or fans were filtered out.
For starters, I simply counted up the most replayed songs. As expected, this yields very boring results – the list of the top most replayed songs is exactly the same as the most played songs. No surprise here. The most played songs are also the most replayed songs.
Top Most Replayed Songs - (A boring result)
- Robin Thicke — Blurred Lines featuring T.I., Pharrell
- Jay-Z — Holy Grail featuring Justin Timberlake
- Miley Cyrus — We Can’t Stop
- Imagine Dragons — Radioactive
- Macklemore — Can’t Hold Us (feat. Ray Dalton)
To make this more interesting, instead of looking at the absolute number of replays, I adjusted for popularity by looking at the ratio of replays to the total number of plays for each song. This replay ratio tells us the what percentage of plays of a song are replays. If we plot the replay ratio vs. the number of fans a song has the outliers become quite clear. Some songs are replayed at a higher rate than others.
I made an interactive version of this graph, you can mouse over the songs to see what they are and click on the songs to listen to them.
Sorting the results by the replay ratio yields a much more interesting result. It surfaces up a few classes of frequently replayed songs: background noise, children’s music, soft and smooth pop and friday night party music. Here’s the color coded list of the top 20:
Top Replayed songs by percentage
- 91% replays White Noise For Baby Sleep — Ocean Waves
- 86% replays Eric West — Reckless (From Playing for Keeps)
- 86% replays Soundtracks For The Masters — Les Contes D’hoffmann: Barcarole
- 83% replays White Noise For Baby Sleep — Warm Rain
- 83% replays Rain Sounds — Relax Ocean Waves
- 82% replays Dennis Wilson — Friday Night
- 81% replays Sleep — Ocean Waves for Sleep – White Noise
- 74% replays White Noise Sleep Relaxation White Noise Relaxation: Ocean Waves 7hz
- 74% replays Ween — Ocean Man
- 73% replays Children’s Songs Music — Whole World In His Hands
- 71% replays Glee Cast — Friday (Glee Cast Version)
- 63% replays Rain Sounds — Rain On the Window
- 63% replays Rihanna — Cheers (Drink To That)
- 60% replays Group 1 Crew — He Said (feat. Chris August)
- 59% replays Karsten Glück Simone Sommerland — Schlaflied für Anne
- 56% replays Monica — With You
- 54% replays Jessie Ware — Wildest Moments
- 53% replays Tim McGraw — I Like It, I Love It
- 53% replays Rain Sounds — Morning Rain In Sedona
- 52% replays Rain Sounds — Rain Sounds
It is no surprise that the list is dominated by background noise. There’s nothing like ambient ocean waves or rain sounds to help baby go to sleep in the noisy city. A five minute track of ambient white noise may be played dozens of times during every nap. It is not uncommon to find 8 hour long stretches of the same five minute white noise audio track played on auto repeat.
The top most replayed song is Reckless by Eric West from the ‘shamelessly sentimental’ 2012 movie Playing for Keeps (4% rotten). 86% of the time this song is played it is a replay. This is the song that you can’t listen to just once. It is the Lays potato chip of music. Beware, if you listen to it, you may be caught in its web and you’ll never be able to escape. Listen at your own risk:
Luckily, most people don’t listen to this song even once. It is only part of the regular listening rotation of a couple hundred listeners. Still, it points to a pattern that we’ll see more of – overly sentimental music has high replay value.
Top Replayed Popular Songs
Perhaps even more interesting is to look at the top most replayed popular songs. We can do this by restricting the songs in the results to those that are by artists that have a significant fan base:
- 31% replays Miley Cyrus — The Climb
- 16% replays August Alsina — I Luv This sh*t featuring Trinidad James
- 15% replays Brad Paisley — Whiskey Lullaby
- 14% replays Tamar Braxton — The One
- 14% replays Chris Brown — Love More
- 14% replays Anna Kendrick — Cups (Pitch Perfect’s “When I’m Gone”)
- 13% replays Avenged Sevenfold — Hail to the King
- 13% replays Jay-Z — Big Pimpin’
- 13% replays Labrinth — Beneath Your Beautiful
- 13% replays Karmin — Acapella
- 12% replays Lana Del Rey — Summertime Sadness [Lana Del Rey vs. Cedric Gervais]
- 12% replays MGMT — Electric Feel
- 12% replays One Direction — Best Song Ever
- 12% replays Big Sean — Beware featuring Lil Wayne, Jhené Aiko
- 12% replays Chris Brown — Don’t Think They Know
- 11% replays Justin Bieber — Boyfriend
- 11% replays Avicii — Wake Me Up
- 11% replays 2 Chainz — Feds Watching featuring Pharrell
- 10% replays Paramore — Still Into You
- 10% replays Alicia Keys — Fire We Make
- 10% replays Lorde — Royals
- 10% replays Miley Cyrus — We Can’t Stop
- 10% replays Ciara — Body Party
- 9% replays Marc Anthony — Vivir Mi Vida
- 9% replays Ellie Goulding — Burn
- 9% replays Fantasia — Without Me
- 9% replays Rich Homie Quan — Type of Way
- 9% replays The Weeknd — Wicked Games (Explicit)
- 9% replays A$AP Ferg — Work REMIX
- 9% replays Jay-Z – Part II (On The Run) featuring Beyoncé
It is hard to believe, but the data doesn’t lie – More than 30% of the time after someone listens to Miley Cyrus’s The Climb they listen to it again right away – proving that there is indeed always going to be another mountain that you are going to need to climb. Miley Cyrus is well represented – her aptly named song We can’t Stop is the most replayed song of the top ten most popular songs.
Here are the top 30 most replayed popular songs in Spotify and Rdio playlists for you to enjoy, but I’m sure you’ll never get to the end of the playlist, you’ll just get stuck repeating The Best Song Ever or Boyfriend forever.
Here’s the Rdio version of the Top 30 Most Replayed popular songs:
Most Manually Replayed
More than once I’ve come back from lunch to find that I left my music player on auto repeat and it has played the last song 20 times while I was away. The song was playing, but no one was listening. It is more interesting to find songs replays in which the replay is manually initiated. These are the songs that grabbed the attention of the listener enough to make them interact with their player and actually queue the song up again. We can find manually replayed songs by looking at replay timestamps. Replays generated by autorepeat will have a very regular timestamp delta, while manual replay timestamps will have more random delta between timestamps.
Here are the top manually replayed songs:
- Body Party by Ciara
- Still Into You by Paramore
- Tapout featuring Lil Wayne, Birdman, Mack Maine, Nicki Minaj, Future by Rich Gang
- Part II (On The Run) featuring Beyoncé by Jay-Z
- Feds Watching featuring Pharrell by 2 Chainz
- Royals by Lorde
- V.S.O.P. by K. Michelle
- Just Give Me A Reason by Pink
- Don’t Think They Know by Chris Brown
- Wake Me Up by Avicii
There’s an Rdio playlist of these songs: Most Manually Replayed
Why do we care which songs are most replayed? It’s part of our never ending goal to try to better understand how people interact with music. For instance, recognizing when music is being used in a context like helping the baby go to sleep is important – without taking this context into account, the thousands of plays of Ocean Waves and Warn Rain would dominate the taste profile that we build for that new mom and dad. We want to make sure that when that mom and dad are ready to listen to music, we can recommend something besides white noise.
Looking at replays can help us identify new artists for certain audiences. For instance, parents looking for an alternative to Miley Cyrus for their pre-teen playlists after Miley’s recent VMA performance, may look to an artist like Fifth Harmony. Their song Miss Movin’ On has similar replay statistics to the classic Miley songs:
Finally, looking at replays is another tool to help us understand the music that people really like. If the neighbors play Rock Lobster 20 times in a row, you can be sure that they really, really like that song. (And despite, or perhaps because of, that night 30 years ago, I like the song too). You should give it a listen, or two…
SXSW 2014 PanelPicker has opened up. I took a tour through the SXSW Music panel proposals to highlight the ones that are of most interest to me … typically technical panels about music discovery and interaction. Here’s the best of the bunch. You’ll notice a number of Echo Nest oriented proposals. I’m really not shilling, I genuinely think these are really interesting talks (well, maybe I’m shilling for my talk).
I’ve previously highlighted the best the bunch for SXSW Interactive.
A Genre Map for Discovering the World of Music
All the music ever made (approximately) is a click or two away. Your favorite music in the world is probably something you’ve never even heard of yet. But which click leads to it?
Most music “discovery” tools are only designed to discover the most familiar thing you don’t already know. Do you like the Dave Matthews Band? You might like O.A.R.! Want to know what your friends are listening to? They’re listening to Daft Punk, because they don’t know any more than you. Want to know what’s hot? It’s yet another Imagine Dragons song that actually came out in 2012. What we NEED are tools for discovery through exploration, not dictation.
This talk will provide a manic music-discovery demonstration-expedition, showcasing how discovery through exploration (The Echo Nest Discovery list & the genre mapping experiment, Every Noise at Once) in the new streaming world is not an opportunity to pay different people to dictate your taste, but rather a journey, unearthing new music JUST FOR YOU.
The Predictive Power of Music
Music taste is extremely personal and an important part of defining and communicating who we are.
Musical Identity, understanding who you are as a music fan and what that says about you, has always been a powerful indicator of other things about you. Broadcast radio’s formats (Urban, Hot A/C, Pop, and so on) are based on the premise that a certain type of music attracts a certain type of person. However, the broadcast version of Musical Identity is a blunt instrument, grouping millions of people into about 12 audience segments. Now that music has become a two-way conversation online, Musical Identity can become considerably more precise, powerful, and predictive.
In this talk, we’ll look at why music is one of the strongest predictors and how music preference can be used to make predictions about your taste in other forms of entertainment (books, movies, games, etc).
Your Friends Have Bad Taste: Fixing Social Music
Music is the most social form of entertainment consumption, but online music has failed to deliver truly social & connected music experiences. Social media updates telling you your aunt listened to Hall and Oates doesn’t deliver on the promise of social music. As access-based, streaming music becomes more mainstream, the current failure & huge potential of social music is becoming clearer. A variety of app developers & online music services are working to create experiences that use music to connect friends & use friends to connect you with new music you’ll love. This talk will uncover how to make social music a reality.
Anyone Can Be a DJ: New Active Listening on Mobile
The mobile phone has become the de facto device for accessing music. According to a recent report, the average person uses their phone as a music player 13 times per day. With over 30 million songs available, any time, any place, listening is shifting from a passive to a personalized and interactive experience for a highly engaged audience.
New data-powered music players on sensor-packed devices are becoming smarter, and could enable listeners to feel more like creators (e.g. Instagram) by dynamically adapting music to its context (e.g. running, commuting, partying, playing). A truly personalized pocket DJ will bring music listening, discovery, and sharing to an entirely new level.
In this talk, we’ll look at how data-enhanced content and smarter mobile players will change the consumer experience into a more active, more connected, and more engaged listening experience.
Human vs. Machine: The Music Curation Formula
Recreating human recommendations in the digital sphere at scale is a problem we’re actively solving across verticals but no one quite has the perfect formula. The vertical where this issue is especially ubiquitous is music. Where we currently stand is solving the integration of human data with machine data and algorithms to generate personalized recommendations that mirrors the nuances of human curation. This formula is the holy grail.
Algorithmic, Curated & Social Music Discover
As the Internet has made millions of tracks available for instant listening, digital music and streaming companies have focused on music recommendations and discovery. Approaches have included using algorithms to present music tailored to listeners’ tastes, using the social graph to find music, and presenting curated & editorial content. This panel will discuss the methods, successes and drawbacks of each of these approaches. We will also discuss the possibility of combining all three approaches to present listeners with a better music discovery experience, with on-the-ground stories of the lessons from building a Discover experience at Spotify.
Beyond the Play Button – The Future of Listening (This is my talk)
35 years after the first Sony Walkman shipped, today’s music player still has essentially the same set of controls as that original portable music player. Even though today’s music player might have a million times more music than the cassette player, the interface to all of that music has changed very little.
In this talk we’ll explore new ways that a music listener can interact with their music. First we will explore the near future where your music player knows so much about you, your music taste and your current context that it plays the right music for you all the time. No UI is needed.
Next, we’ll explore a future where music listening is no longer a passive experience. Instead of just pressing the play button and passively listening you will be able to jump in and interact with the music. Make your favorite song last forever, add your favorite drummer to that Adele track or unleash your inner Skrillex and take total control of your favorite track.
5 Years of Music Hack Day
Started in 2009 by Dave Haynes and James Darling, Music Hack Day has become the gold standard of music technology events. Having grown to a worldwide, monthly event that has seen over 3500 music hacks created in over 20 cities the event is still going great guns. But, what impact has this event had on the music industry and it’s connection with technology? This talk looks back at the first 5 years of Music Hack Day, from it’s origins to becoming something more important and difficult to control than it’s ‘adhocracy’ beginnings. Have these events really impacted the industry in a positive way or have the last 5 years simply seen a maturing attitude towards technologies place in the music industry? We’ll look at the successes, the hacks that blew people’s minds and what influence so many events with such as passionate audience have had on changing the relationship between music and tech.
The SXSW organizers pay attention when they see a panel that gets lots of votes, so head on over and make your opinion be known.
A bunch of music tech folk will be in Dublin Ireland next week to attend ACM Recommender Systems 2012. We’ll be heading over to the Bull & Castle, beside Christ Church, Dublin City on September 13 at 18:30 to join <Pub> Standands Dublin, to hang out and chat about hacking music. Pub Standards is a post-conference drink-up without the conference. There’s no format, talks or presentations. It’s just geeks + beer. If you are in the area and are interested in hanging out, feel free to come on down and have a beer.
I’ve received quite a bit of feedback on my recent Most Musical City post, especially from folks from Austin that didn’t like Austin’s 14th place ranking. This reddit/austin comment thread was rather brutal, and this Austinist article Wait, What?! Austin Not Ranked In Top 10 Musical Cities List even closed with this appeal: any data analysts out there up for the challenge to get Austin closer to the top?
Well, John Rees, the Director of Community & Economic Development at Capital Area Council of Governments in Austin is just the data analyst that the Austinist was looking for. He re-ran the analysis but instead of using city populations he calculated the rankings based upon metropolitan statistical areas. In the May issue of Data Points Newsletter John reports on this analysis:
When data from The Echo Nest is adjusted to include metropolitan statistical area population data, the rankings of America’s most musical places changes significantly. Topping the list is Nashville, San Francisco and Los Angeles (which includes Beverly Hills). The Austin region jumps ten places from the original list to become America’s forth [sic] most musical region.
John goes on to point out some of the non-quantifiable aspects of the Austin music scene such as the diversity of music as well as the presence of events such as SXSW and Austin City Limits. John makes a strong argument that Austin is one of the country’s premier music destinations. Even the reaction of Austin’s residents to my post says a lot about Austin as a music city. People from Austin really care about music and don’t take it kindly when they are not at the top of the most musical city list. So congrats to Austin, not just for moving up the chart but also for demonstrating that Austin is the city that is most passionate about music
Lots of ink has been spilled about the Loudness war and how modern recordings keep getting louder as a cheap method of grabbing a listener’s attention. We know that, in general, music is getting louder. But what are the loudest songs? We can use The Echo Nest API to answer this question. Since the Echo Nest has analyzed millions and millions of songs, we can make a simple API query that will return the set of loudest songs known to man. (For the hardcore geeks, here’s the API query that I used). Note that I’ve restricted the results to those in the 7Digital-US catalog in order to guarantee that I’ll have a 30 second preview for each song.
So without further adieu, here are the loudest songs
The song Topping and Core by Grmalking555 has a whopping loudness of 4.428 dB.
The song Modifications by Micron has a loudness of 4.318 dB.
The song Hey You Fuxxx! by Kylie Minoise with a loudness of 4.231 dB
Here’s a little taste of Kylie Minoise live (you may want to turn down your volume)
The song War Memorial Exit by Noma with a loudness of 4.166 dB
The song Hello Dirty 10 by Massimo with a loudness of 4.121 dB.
These songs are pretty niche. So I thought it might be interesting to look the loudest songs culled from the most popular songs. Here’s the query to do that. The loudest popular song is:
The loudest popular song is Welcome to the Jungle by Guns ‘N Roses with a loudness of -1.931 dB.
You may be wondering how a loudness value can be greater than 0dB. Loudness is a complex measurement that is both a function of time and frequency. Unlike traditional loudness measures, The Echo Nest analysis models loudness via a human model of listening, instead of directly mapping loudness from the recorded signal. For instance, with a traditional dB model a simple sinusoidal function would be measured as having the same exact “amplitude” (in dB) whether at 3KHz or 12KHz. But with The Echo Nest model, the loudness is lower at 12KHz than it is at 3KHz because you actually perceive those signals differently.
Thanks to the always awesome 7Digital for providing album art and 30 second previews in this post.
Yesterday, SXSW opened up the 2012 Panel Picker allowing you to vote up (or down) your favorite panels. The SXSW organizers will use the voting info to help whittle the nearly 3,600 proposals down to 500. I took a tour through the list of music related panel proposals and selected a few that I think are worth voting for. Talks in green are on my “can’t miss this talk” list. Note that I work with or have collaborated with many of the speakers on my list, so my list can not be construed as objective in any way.
There are many recurring themes. Turnatable.fm is everywhere. Everyone wants to talk about the role of the curator in this new world of algorithmic music recommendations. And Spotify is not to be found anywhere!
I’ve broken my list down into a few categories:
Social Music – there must be a twenty panels related to social music. (Eleven(!) have something to do with Turntable.fm) My favorites are:
- Social Music Strategies: Viral & the Power of Free – with folks from MOG, Turntable, Sirius XM, Facebook and Fred Wilson. I’m not a big fan of big panels (by the time you get done with the introductions, it is time for Q&A), but this panel seems stacked with people with an interesting perspective on the social music scene. I’m particularly interested in hearing the different perspectives from Turntable vs. Sirius XM.
- Can Social Music Save the Music Industry? – Rdio, Turntable, Gartner, Rootmusic, Songkick – Another good lineup of speakers (Turntable.fm is everywhere at SXSW this year) exploring social music. Curiously, there’s no Spotify here (or as far as I can tell on any talks at SXSW).
- Turntable.fm the Future of Music is Social - Turntable.fm – This is the turntable.fm story.
- Reinventing Tribal Music in the land of Earbuds - AT&T – this talk explores how music consumption changes with new social services and the technical/sociological issues that arise when people are once again free to choose and listen to music together.
Man vs. Machine – what is the role of the human curator in this age of algorithmic recommendation and music. Curiously, there are at least 5 panel proposals on this topic.
- Music Discovery:Man Vs. Machine – MOG, KCRW, Turntable.fm, Heather Browne
- Music/Radio Content: Tastemakers vs. Automation – Slacker
- Editor vs. Algorithm in the Music Discovery Space – SPIN, Hype Machine, Echo Nest, 7Digital
- Curation in the age of mechanical recommendations – Matt Ogle / Echo Nest – This is my pick for the Man vs. Machine talk. Matt is *the* man when it comes to understanding what is going on in the world of music listening experience.
- Crowding out the Experts – Social Taste Formation – Last.fm, Via, Rolling Stone - Is social media reducing the importance of reviewers and traditional cultural gatekeepers? Are Yelp, Twitter, Last.fm and other platforms creating a new class of tastemakers?
Music Discovery – A half dozen panels on music recommendation and discovery. Favs include:
- YouKnowYouWantIt: Recommendation Engines that Rock - Netflix, Pandora, Match.com – this panel is filled with recommendation rock stars
- The Dark Art of Digital Music Recommendations - Rovi – Michael Papish of Rovi promises to dive under the hood of music recommendation.
- No Points for Style: Genre vs. Music Networks – SceneMachine – Any talk proposal with statements like “Genre uses a 19th-century tool — a Darwinian tree — to solve a 21st-century problem. And unlike evolutionary science, it’s subjective. By the time a genre branch has been labeled (viz. “grunge”), the scene it describes is as dead as Australopithecus.” is worth checking out.
Mobile Music – Is that a million songs in your pocket or are you just glad to see me?
- Music Everywhere: Are we there yet? – Soundcloud, Songkick, Jawbone – Have we arrived at the proverbial celestial jukebox? What are the challenges?
Big Data – exploring big data sets to learn about music
- Data Mining Music - Paul Lamere – Shameless self promotion. What can we learn if we have really deep data about millions of songs?
- The Wisdom of Thieves: Meaning in P2P Behavior - Ben Fields – Don’t miss Ben’s talk about what we can learn about music (and other media) from mining P2P behavior. This talk is on my must see list.
- Big data for Everyman: Help liberate the data serf - Splunk – webifying and exploiting big data
Echo Nesty panels – proposals from folks from the nest. Of course, I recommend all of these fine talks.
- Active Listening – Tristan Jehan - Tristan takes a look at how the music experience is changing now that the listener can take much more active control of the listening experience. There’s no one who understands music analysis and understanding better than Tristan.
- Data Mining Music - Paul Lamere – This is my awesome talk about extracting info from big data sets like the Million Song Dataset. If you are a regular reader of this blog, you’ll know that I’ll be looking at things like click track detectors, passion indexes, loudness wars and son on.
- What’s a music fan worth? – Jim Lucchese - Echo Nest CEO takes a look at the economics of music, from iOS apps to musicians. Jim knows this stuff better than anyone.
- Music Apps Gone Wild – Eliot Van Buskirk – Eliot takes a tour of the most advanced, wackiest music apps that exist — or are on their way to existing.
- Curation in the age of mechanical recommendations – Matt Ogle – Matt is a phenomenal speaker and thinker in the music space. His take on the role of the curator in this world of algorithms is at the top of my SXSW panel list.
- Editor vs. Algorithm in the Music Discovery Space - SPIN, Hype Machine, Echo Nest (Jim Lucchese), 7Digital
- Defining Music Discovery through Listening – Echo Nest (Tristan Jehan), Hunted Media - This session will examine “true” music discovery through listening and how technology is the facilitator.
- Designing Future Music Experiences – Rdio, Turntable, Mary Fagot – A look at the user experience for next generation music apps.
- Music at the App Store: Lessons from Eno and Björk – Are albums as apps gimmicks or do they provide real value?
- Participatory Culture: The Discourse of Pitchfork – An analysis of ten years of music writing to extract themes.
I’ve submitted a proposal for a SXSW 2012 panel called Data Mining Music. The PanelPicker page for the talk is here: Data Mining Music. If you feel so inclined feel free to comment and/or vote for the talk. I promise to fill the talk with all sorts of fun info that you can extract from datasets like the Million Song Dataset.
Here’s the abstract:
Data mining is the process of extracting patterns and knowledge from large data sets. It has already helped revolutionized fields as diverse as advertising and medicine. In this talk we dive into mega-scale music data such as the Million Song Dataset (a recently released, freely-available collection of detailed audio features and metadata for a million contemporary popular music tracks) to help us get a better understanding of the music and the artists that perform the music.
We explore how we can use music data mining for tasks such as automatic genre detection, song similarity for music recommendation, and data visualization for music exploration and discovery. We use these techniques to try to answers questions about music such as: Which drummers use click tracks to help set the tempo? or Is music really faster and louder than it used to be? Finally, we look at techniques and challenges in processing these extremely large datasets.
- What large music datasets are available for data mining?
- What insights about music can we gain from mining acoustic music data?
- What can we learn from mining music listener behavior data?
- Who is a better drummer: Buddy Rich or Neil Peart?
- What are some of the challenges in processing these extremely large datasets?
Flickr photo CC by tristanf
I just fired up my Google Music account this afternoon and this is what I found:
All 7,861 songs are gone. I hope they come back. Apparently, I’m not the only one this is happening to.
Update – all my music has returned sometime overnight.
Peter Sobot (@psobot ) has used The Echo Nest Remix to automatically add dubstep to any song.
The Crash Bandicoot Dubset remix is pretty wild. Peter says that The Wub Machine is still work in progress. Check out how it works and add your ideas to the mix on Peter’s blog.