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PAPPA – Paul’s Awesome Party Playlisting App

For my Boston Music Hack Day hack I built Yet Another Party Playlisting App (YAPPA), because the world needed another party playlister – but really, I built it because I needed another hack, because 15 hours into the 24 hour hackathon I realized that my first hack just wasn’t going to work (more on that in another post). And so, with 9 hours left in the hack day, I thought I would try my hand at the party playlisting app.

The YAPPA is a frequently built app. In some sense one can look at the act of building a YAPPA as a hacking exercise. Just as a still life painter will practice by painting a bowl of fruit, or a pianist will practice scales, a music hacker can build their hacking muscle by creating a YAPPA.

The essential features of a YAPPA are straightforward – create a listening experience for a party based upon the tastes of the guests.  Allow guests to suggest music for the party, apply some rules to select music that satisfies all the guests, and keep the music flowing.

With those features in mind, I created my party playlisting app. The interface is dead simple – guests can add music to the party via the master web interface or text the artist and song from the mobile phones to the party phone number. Once the party has started, PAPPA will keep the music flowing.

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The key technology of PAPPA is how it picks the music to play next. Most YAPPAs will try to schedule music based on fairness so that everyone’s music taste is considered. Some YAPPAs also use song attributes such as song hotttnesss, song energy and danceability to make sure that the music matches the vibe of the party. PAPPA takes a very different approach to scheduling music. That’s because PAPPA takes a very different approach to parties. PAPPA doesn’t like parties. PAPPA wants everyone to go home. So PAPPA takes all of these songs that have been carefully texted to the party phone number, along with all the artist and song suggestions submitted via the web and throws them away. It doesn’t care about the music taste of the guests at the party. In fact it despises their taste (and the guests as well). Instead, PAPPA selects and plays the absolute worst music it can find. It gives the listener an endless string of the most horrible (but popular) music. Here’s a sample (the first 3 songs are bait to lure in the unwitting party guests):

  1. Royals by Lorde
  2. Levels by Avicii
  3. Blurred Lines by Robin Thicke
  4. #Twerkit featuring Nicki Minaj by Busta Rhymes
  5. From The Bottom Of My Broken Heart by Britney Spears
  6. Amigas Cheetahs by The Cheetah Girls
  7. Do Ya Think I’m Sexy by Paris Hilton
  8. Incredible by Clique Girlz
  9. No Ordinary Love by Jennifer Love Hewitt
  10. Mexican Wrestler by Emma Roberts
  11. I Don’t Think About It by Emily Osment
  12. A La Nanita Nana by The Cheetah Girls
  13. Don”t Let Me Be The Last To Know by Britney Spears
  14. Wild featuring Big Sean by Jessie J
  15. Heartbeat (Album Version) by Paris Hilton
  16. Love The Way You Love Me by The Pussycat Dolls
  17. When You Told Me You Loved Me by Jessica Simpson
  18. Jericho by Hilary Duff
  19. Strip by Brooke Hogan
  20. Pero Me Acuerdo De Tí by Christina Aguilera
  21. Bang Bang by Joachim Garraud
  22. Right Now featuring David Guetta (Sick Individuals Dub) by Rihanna
  23. Wilde Piraten by The Cool Kids
  24. Friend Lover by Electrik Red
  25. Betcha Can’t Do It Like Me by D4L
  26. Who’s That Girl by Hilary Duff
  27. Get In There, Frank! by Fun
  28. Hold It Don”t Drop It by Jennifer Lopez
  29. Sweet Sixteen by Hilary Duff
  30. Live It Up featuring Pitbull by Jennifer Lopez
  31. Freckles by Natasha Bedingfield
  32. I Want You by Paris Hilton
  33. Hold It Close by Fun
  34. Magic by The Pussycat Dolls
  35. How To Lose A Girl by Mitchel Musso
  36. Fairy Tales by JoJo
  37. Slow It Down featuring Fabolous (Album Version (Explicit)) by The-Dream
  38. Mr. Hamudah by Charles Hamilton
  39. Promise by Vanessa Hudgens
  40. Metamorphosis by Hilary Duff

How does PAPPA find the worst music in the world?  It looks through all the data that The Echo Nest is collecting about how people experience music online to find the songs that have been banned frequently. When a music listener says “ban this song” they are making a pretty strong statement about the song – essentially saying, “I do not ever want to hear that song again in my life”.  PAPPA finds these songs that have the highest banned-to-play ratio (i.e. the songs that have been proportionally banned the most when play count is taken into consideration) and adds them to the playlist. The result being a playlist filled with the most reviled music – with songs by Paris Hilton, Jennifer Love Hewitt and the great Emma Roberts. The perfect playlist to send your guests home.

At this moment, lets pause and listen to the song Mexican Wrestler by Emma Roberts:

[spotify http://open.spotify.com/track/48rQ4vyXnSTeqbBkZba5og]

What happens to all those carefully crafted text messages of songs sent by the guests?  No, there’s no Twilio app catching all those messages, parsing out songs and adding them to a play queue to be scheduled. They just go to my phone. That’s so if people are not leaving the party fast enough, I can use all the phone numbers of the guests to start to text them back and tell them they should go home.

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By the way, if you look at the songs that were texted to me during my two minute demo you’d realize how fruitless a YAPPA really is. There’s no possible way to make a party playlist that is going to satisfy everyone in the room. Tastes are too varied, and there’s always that guy who thinks he is clever by adding some Rick Astly to the party queue. Here’s what was texted to me during my two minute demo:

  1. Gregory Porter – be good
  2. Rebecca Black – It’s Friday
  3. Weird Al Yankovic – Fat
  4. Lady Gaga – Applause
  5. Weird Al Yankovic – Amish Paradise (from a different phone number from the other weird Al fan)
  6. Ghostbusters
  7. boss ass bitch
  8. Basement Jaxx raindrops
  9. John Mayer your body is a wonderland
  10. jay z holy grail
  11. Underworld spikee
  12. wake me up
  13. Britney Spears – Hit Me Baby One More Time
  14. Slayer War Ensemble
  15. Bieber baby
  16. KANYE
  17. Royksopp
  18. Ra Ra riot
  19. Rick Astley
  20. Mozart
  21. OSM
  22. Mikey Cyrus
  23. Hi paul
  24. Stevie wonder overjoyed

Imagine trying to build a party playlist based upon those 24 input songs. Admittedly, a hackathon demo session is not a real test case for a party playlister but I still think you’d end up with a terrible mix of songs that no smart algorithm, nor any smart human, could stitch together into a playlist that would be appropriate and pleasing for a party.  My guess is that if you did an A/B test for two parties, where one party played music based upon suggestions texted to a YAPPA and the other party played the top hotttest songs, the YAPPA party would always lose. I’d run this test, but that would mean I’d have to go to two parties. I hate parties, so this test will never happen. Its one of the flaws in our scientific method.

Who are the worst artists?
Looking at the PAPPA playlists I see a number of recurring artists – Britney Spears and Paris Hilton seem to be well represented. I thought it would be interesting to create a histogram of the top recurring artists in the most banned songs list.  Here’s the fascinating result:

PAPPA_-_Paul_s_Awesome_Party_Playlist_App

One thing I find notable about this list is the predominance of female artists.  Females outnumber males by a substantial amount.  Here’s some pie:

PAPPA_-_Paul_s_Awesome_Party_Playlist_App

80% of the most banned artists are female. A stunning result. There’s something going on here. Someone suggested that the act of banning a song is an aggressive act that may skew male, and many of these aggressively banning males don’t like to listen to female artists. More study is needed here. It may involve parties, so I’m out.

Wrapping it all up

I enjoyed creating my PAPPA YAPPA. Demoing it was really fun and the audience seemed to enjoy the twist ending.  The patterns in the data underlying the app are pretty interesting too. Why are so many banned songs by female artists?

If you are having your own party and want to use PAPPA to help enhance the party you can go to:

                     static.echonest.com/pappa/?phone=603+555+1212 

Just replace the phone number in the URL with your own and you are good to go.

PAPPA - Paul's Awesome Party Playlisting App - Ruining parties since 2013

PAPPA – Paul’s Awesome Party Playlisting App – Ruining parties since 2013

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Music Hack Day NYC

Music_Hack_Day_NYC_-_Splash

Music Hack Day NYC has just wrapped up, and what a great weekend it was!. Hosted at Spotify’s spiffy new headquarters in midtown, Music Hack Day NYC was the place to be if you are passionate music, technology and building stuff. During all night Friday and all day Saturday, hundreds of hackers used music APIs from companies like The Echo Nest, Soundcloud, Gracenote, Rdio and of course Spotify, to build next generation music apps.

spotify_entrance

It was a really fun event. The Spotify headquarters are perfect for hacking. Flawless and apparently limitless wifi/bandwidth, awesome A/V setup, and great sound for an unending social hacking playlist.

hackers-lg

Over the course of about 20 hours of hacking, 36 hacks were built and demoed.  There was quite a range of inventive hacks. Some of my favorites:

EERFY
The crowd favorite was Oscar Celma’s extremely clever EERFY – He solved the Music Industry Problem in 24 hours by turning it upside down with EERFY:

halfstep
Another favorite was Leo and Jason’s halfstep –  a chrome extension that motivates you to move more. How? If you only moved 20% of your movement goal yesterday, then halfstep will also let you listen to first 20% of any songs today. Check out the screencast of their hack.

Screenshot_10_21_13_12_27_PM

PartyOutlook
There were lots of great Echo Nest hacks including the Echo Nest prize winner partyOutlook by Matt Egan. PartyOutlook is a CocoaLibSpotify powered iPad jukebox that accepts tracks via the Twillio API and displays real-time information about the life of the party using Echo Nest song data. Allows an administrator to pause and play music, as well as skip tracks.

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TuneTravelr
Another really neat Echo Nest hack was TuneTravelr created by Chris Evans and Joshua Boggs. Tune Travelr  is a web app that  takes cities and locales a well as a time period and returns a  playlist of songs that were hot at the time. It uses the Echo Nest data to leverage some analytic querying, before pipping the results into Rdio’s web API for playback.

TuneTravelr

MoodVenue
MoodVenue used The Echo Nest API to help find out what’s going on tonight based on your mood and location.

Landing_Page___Mood_Venue

Repetition faceoff
Artists fight to the death, using only the sheer repetitive force of their music! By Brian McFee. Brian used the Echo Nest analysis data to build a custom metric for repetition for any song and used that to score songs by the artist for the face-off.

repfaceoff.png__1366×724_


Cheese Tray
A novel use of The Echo Nest API was Cheese Tray – A  Spotify app that takes selects randomly from among your Spotify playlists and analyzes it. It then adds to that playlist a song that best represents the average attributes of the playlist, as given by The Echo Nest API. Then, via SMS through the Twilio service, it sends a command to your Android device that changes its background to the album artwork for that average song.

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Songs About
Uses wikipedia articles titled ‘List of songs about…” to generate Spotify/Rdio playlists about different subjects/places. Songs about used The Echo Nest’s Rosetta Stone to get the song IDs for playing in Spotify and Rdio.

Topics_and_Edit_Post_‹_Music_Machinery_—_WordPress-2


Awesome Chart Explorer

This is my hack. A visualization that lets you explore and listen to 50 years of Billboard charts.
ss

ccRex
Upload a song, ccRex’ll fetch Creative Commons music to match using Echo Nest song attributes to determine the best match.

CCREX

Other nifty Echo Nest hacks:

  • opporTUNEcontext dependent playlisting based upon weather, activity, location, time of day and your favorite genresopporTUNE____Music_Hack_Day_NYC_2013____Hacker_League
  • crowdPlay – an SMS enabled party playlisterCrowdPlay_-2
  • BPM Reader –  an app that updates a playlist in real-time based on user keyboard input correlated to BPM. Tap in your beat, and generate a playlist of songs with that rhythm.facemelter.neocities.org
  • Moody calls – Get a phone call with a song that matches your moodMoodyCalls
  • Spotify V. Rdio -Pitting the two music platforms against each other using your listening history
    .
    Spotify_v._Rdio-2
  • Perl client for the Echo Nest – Ajax built a library that provides support for nearly all of The Echo Nest API features.
    trs-80_WWW-TheEchoNest

All in all, it was a great event with lots of awesome, innovative hacks and lots of smart people. A good time was had by all.  Thanks to Spotify and @mager for organizing the event. Well Done!

Andrew_Mager_-_SXSW_2010___Flickr_-_Photo_Sharing_

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What are your favorite Music Hack Day Hacks?

Photo by Thomas Bonte

Photo by Thomas Bonte

In a couple of weeks I’m heading out to Chicago to give a talk at the Chicago Music Summit about Music Hacking at Music Hack Days. I’ll have an hour to talk about hack days and show off lots of demos. Naturally, I’d like to highlight all the best hacks. However,  given that there have been over 30 music hack days, remembering the best of the 2000+ hacks is going to be a challenge.  I’m hoping you can help me remember the best hacks, either by adding a comment to this post or just tweeting with #bestmusichack. I prefer hacks that I can demo directly via the web or that have been captured on video. To get things started here are some of the most notable hacks that I can recall.

Whimsical Hacks

Hardware Hacks

Music Exploration and Discovery Hacks

Party playlisting

Of the many party playlisting hacks that have been created, which one is the best?

Hacks that have been turned into businesses

Music Remixing Hacks

Performance art

No need to be shy about suggesting your own hacks. As you can see, I have no qualms about adding my own hacks (Bonhamizer, Infinite Jukebox and Boil The Frog) to the list.

I anticipate your recommendations. Thanks in advance for your help!

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What is a Music Hack Day?

With 3 new Music Hack Days announced this week, it might be time for you to check out what goes on at a Music Hack Day.  Here are some videos that give a taste of what it’s like:

Music Hack Day Paris 2013

Music Hack Day Sydney 2012**

Music Hack Day 2012 Barcelona

Music Hack Day NYC 2011

For more info on what a Music Hack Day is like read: What happens at a Music Hack Day. I hope to see you all at one of the upcoming events.

**It is strange how a non-hacker made it onto the thumbnail for the Sydney video. Dude, It’s Sydney Australia, not Sydney Lawrence ;).

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Upcoming Music Hack Days – Chicago, Bologna and NYC

hackday.1.1.1.1

Photo by Thomas Bonte

Fall is traditional Music Hack Day season, and 2013 is shaping up to be the strongest yet. Three Music Hack Days have just been announced:

  • Chicago – September 21st and 22nd – this will be the first ever Music Hack Day in Chicago.
  • Bologna – October 5th and 6th – in collaboration with roBOt Festival 2013. The first Music Hack Day in Italy.
  • New York – October 18th and 19th – being held in Spotify’s nifty new offices.

There will no doubt be more hack days before the end of the year including the traditional Boston and London events.  You can check out the full schedule and sign up to be notified whenever at a new Music Hack Day is announced at MusicHackDay.org.

Music Hack Day is an international 24-hour event where programmers, designers and artists come together to conceptualize, build and demo the future of music. Software, hardware, mobile, web, instruments, art – anything goes as long as it’s music related.

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Top SXSW Music Panels for music exploration, discovery and interaction

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
Screenshot_5_22_13_11_01_AMAll 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)

Rolling in the Deep (labelled) by Adele

Rolling in the Deep (labelled) by Adele

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
hackday.1.1.1.1Started 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.

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Top SXSWi panels for music discovery and interaction

SXSW 2014 PanelPicker has opened up. I took a tour through the SXSW Interactive talk proposals to highlight the ones that are of most interest to me … typically technical panels about music discover and interaction. Here’s the best of the bunch. Tomorrow, I’ll take a tour through the SXSW Music proposals.

Algorithmic Music Discovery at Spotify
Spotify crunches hundreds of billions of streams to analyze user’s music taste and provide music recommendations for its users. We will discuss how the algorithms work, how they fit in within the products, what the problems are and where we think music discovery is going. The talk will be quite technical with a focus on the concepts and methods, mainly how we use large scale machine learning, but we will also some aspects of music discovery from a user perspective that greatly influenced the design decisions.

Delivering Music Recommendations to Millions
At its heart, presenting personalized data and experiences for users is simple. But transferring, delivering and serving this data at high scale can become quite challenging.
In this session, we will speak about the scalability lessons we learned building Spotify’s Discover system. This system generates terabytes of music recommendations that need to be delivered to tens of millions of users every day. We will focus on the problems encountered when big data needs to be replicated across the globe to power interactive media applications, and share strategies for coping with data at this scale.

Are Machines the DJ’s of Digital Music?
When it comes to music curation, has our technology exceeded our humanity? Fancy algorithms have done wonders for online dating. Can they match you with your new favorite music? Hear music editors from Rhapsody, Google Music, Sony Music and Echonest debate their changing role in curation and music discovery for streaming music services. Whether tuning into the perfect summer dance playlist or easily browsing recommended artists, finding and listening to music is the result of very intentional decisions made by editorial teams and algorithms. Are we sophisticated enough to no longer need the human touch on our music services? Or is that all that separates us from the machines?

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, including:

  • Musical Identity (MI) – who we are as music fans and how understanding MI is unlocking social music apps
  • If my friend uses Spotify & I use Rdio, can we still be friends? ID resolution & social sharing challenges
  • Discovery issue: finding like-minded fans & relevant expert music curators
  • A look at who’s actually building the future of social music

‘Man vs. Machine’ Is Dead, Long Live Man+Machine
A human on a bicycle is the most efficient land-traveller on planet Earth. Likewise, the most efficient advanced, accurate, helpful, and enjoyable music recommendation systems combine man and machine. This dual-pronged approach puts powerful, data-driven tools in the hands of thinking, feeling experts and end users. In other words, the debate over whether human experts or machines are better at recommending music is over. The answer is “both” — a hybrid between creative technology and creative curators. This panel will provide specific examples of this approach that are already taking place, while looking to the future to see where it’s all headed. 

Are Recommendation Engines Killing Discovery?
Are recommendation engines – like Yelp, Google, and Spotify – ruining the way we experience life? “Absolutely,” says Ned Lampert. The average person looks at their phone 150 times a day, and the majority of content they’re looking at is filtered through a network of friends, likes, and assumptions. Life is becoming prescriptive, opinions are increasingly polarized, and curiosity is being stifled. Recommendation engines leave no room for the unexpected. Craig Key says, “absolutely not.” The Web now has infinitely more data points than we did pre-Google. Not only is there more content, but there’s more data about you and me: our social graph, Netflix history (if you’re brave), our Tweets, and yes, our Spotify activity. Data is the new currency in digital experiences. While content remains king, it will be companies that can use data to sort and display that content in a meaningful way that will win. This session will explore these dueling perspectives.

Genre-Bending: Rise of Digital Eclecticism
The explosion in popularity of streaming music services has started to change the way we listen. But even beyond those always-on devices with unlimited access to millions of songs that we listen to on our morning commutes, while wending our way through paperwork at our desks or on our evening jogs, there is an even a more fundamental change going on. Unlimited access has unhinged musical taste to the point where eclecticism and tastemaking trump identifying with a scene. Listeners are becoming more adventurous, experiencing many more types of music than ever before. And artists are right there with them, blending styles and genres in ways that would be unimaginable even a decade ago. In his role as VP Product-Content Jon Maples has a front row seat to how music-listening behavior has evolved. He’ll share findings from a recent ethnographic study that reveals intimate details on how people live their musical lives.

Put It In Your Mouth: Startups as Tastemakers
Your life has been changed, at least once, by a startup in the last year. Don’t argue; it’s true. Think about it – how do you listen to music? How do you choose what movie to watch? How do you shop, track your fitness or share memories? Whoever you are, whatever your preferences, emerging technology has crept into your life and changed the way you do things on a daily basis. This group of innovators and tastemakers will take a highly entertaining look at how the apps, devices and online services in our lives are enhancing and molding our culture in fundamental ways. Be warned – a dance party might break out and your movie queue might expand exponentially.

And here’s a bit of self promotion … my proposed panel is all about new interfaces for music.

Beyond the Play Button – The Future of Listening
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.

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.

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Beyond the Play Button – My SXSW Proposal

It is SXSW Panel Picker season.   I’ve submitted a talk to both SXSW Interactive and SXSW Music.  The talk is called ‘Beyond the Play Button – the Future of Listening’ – the goal of the talk is to explore new interfaces for music listening, discovery and interaction.  I’ll show a bunch of my hacks and some nifty stuff I’ve been building in the lab. Here’s the illustrated abstract:

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.

If this talk looks interesting to you (and if you are a regular reader of my blog, it probably is), and you are going to SXSW, consider voting for the talk via the SXSW Panel Picker:

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Rock Steady – My Music Ed Hack

This weekend I’m at The Music Education Hack in New York City where educators and technologists are working together to transform music education in New York City.  My hack, Rock Steady,  is a drummer training app for the iPhone.  You use the app to measure how well you can keep a steady beat.  Here’s how it works:

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First you add songs from your iTunes collection. The app will then use The Echo Nest to analyze the song and map out all of the beats. Once the song is ready you enter Rock Steady training mode: The app will show you the current tempo of the song. Your goal then is to match the tempo by using your phone as a drumstick and tapping out the beat.  You are scored based upon how well you match the tempo.  There are three modes: matching mode  – in this easy-peesy mode you listen to the song and match the tempo.  A bit harder is silent mode –  you listen to the song for a few seconds and then try to maintain the tempo on your own. Finally there’s bonzo mode – here the music is playing, but instead of you matching the music, the music matches you. If you speed up, the music speeds up, if you slow down, the music slows down.  This is the trickiest mode – you have to keep a steady beat and not be fooled by the band that is following you.

This is my first iOS hack. I got to use lots of new stuff, such as Core Motion to detect the beats. I stole lots of code from the iOS version of the Infinite Jukebox (all the track upload  and analysis stuff).  It was a fun hack to build. If anyone thinks it is interesting I may try to finish it and put it in the app store.

 

Here’s a video:

[youtube http://www.youtube.com/watch?v=UsJ7RBkRAag]

 

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Two music hackathons in NYC next weekend ….

Next weekend, (starting friday, June 28th) there are two music-related hackathons in NYC.  First up, there’s The Hamr

Hacking Audio and Music Research (HAMR)

Organized by Colin Raffel is  HAMR: Hacking Music and Audio Research.  This hackathon is focused on music research with a goal of testing out new ideas rather than making a finished product. The focus of HAMR is on the development of new techniques for analyzing, processing, and synthesizing audio and music signals.  HAMR will be modeled after a traditional hack day in that it will involve a weekend of fast-paced work with an emphasis on trying something new rather than finishing something polished.  However, this event will deviate from the typical hack day in its focus on research (rather than commercial) applications.  In addition to HAMRing out work, the event will include presentations, discussions, and informal workshops.  Registration is free and researchers from any stage in their career are encouraged to participate.  Read more about Hamr

The other hacking event is Music Education Hack

Music Education Hack

music-ed-hackThe goal of the Music Education Hack is to explore how technology and help transform music education in NYC schools.  Hackers will have 24 hours to ideate, collaborate and innovate, before presenting their work to a panel of esteemed judges for a grand prize of $5,000. The Hacker teams will have access to New York City teachers as part of the  creation process as they focus on building products that incorporate music and technology into the education space.  For more info visit the Music Education Hack registration page.

 

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