Which music services are growing, which are shrinking

Here’s a quick tour of google trends output for a number of music services with an eye for identifying which are growing and which are shrinking. Google trends tracks search interest.  The number 100 represents the peak search interest in these graphs.

Updated (1) (2) (3) – added a number of new charts. Updated (4) – added a summary list

Here’s a quick summary:
Rising:
Spotify, Soundcloud, Rdio, Songza, SiriusXM, iheartradio, 8tracks, bandcamp, Google Music, Mixcloud, Shazam Muve, Ex.fm, Radionomy, Music Unlimited
Steady:
Amazon Mp3, Beatport, iTunes, Pandora, Youtube
Slight decline:
Slacker, Jango, Soundhound, xbox music
Falling:
Rhapsody, Deezer, Grooveshark, Turntable.fm,MOG, Hype Machine, Playlist.com, Walmart, Yahoo Music, Myspace Music, Facebook Music, Zune, Last.fm, Twitter Music, radio.com

iTunes – ITunes looks relatively flat since 2010. Perhaps things will change with their Pandora competitor to be launched this month.

Google_Trends_-_Web_Search_interest__itunes_-_Worldwide__2004_-_present

last.fm – Peaked in 2009, has now fallen back to where it was in 2006. The golden age of last.fm is over, sad to say.

Google_Trends_-_Web_Search_interest__last.fm_-_Worldwide__2004_-_present

Spotify – steady growth since launch in 2009

Google_Trends_-_Web_Search_interest__spotify_-_Worldwide__2004_-_present

Pandora – steady growth since 2006. Perhaps leveling off.

Google_Trends_-_Web_Search_interest__pandora_-_Worldwide__2004_-_present

Rhapsody  – slow but steady shrinking interest

Google_Trends_-_Web_Search_interest__rhapsody_-_Worldwide__2004_-_present

Rdio – steady growth since 2011 launch, steep growth in the last year

Google_Trends_-_Web_Search_interest__rdio_-_Worldwide__2004_-_present

Deezer – steady shrinkage since 2009

Google_Trends_-_Web_Search_interest__deezer_-_Worldwide__2004_-_present

Grooveshark – peaked in 2012, now shrinking

Google_Trends_-_Web_Search_interest__grooveshark_-_Worldwide__2004_-_present

siriusxm – strong growth since 2011

Google_Trends_-_Web_Search_interest__siriusxm_-_Worldwide__2004_-_present-2

iheartradio – strong growth since 2011

Google_Trends_-_Web_Search_interest__iheartradio_-_Worldwide__2004_-_present

Google Music – slow steady growth

Google_Trends_-_Web_Search_interest__google_music_-_Worldwide__2004_-_present

Slacker  – slight decline in interest since its peak in 2009

Google_Trends_-_Web_Search_interest__slacker_-_Worldwide__2004_-_present

Soundcloud – strong increase since 2009

Google_Trends_-_Web_Search_interest__soundcloud_-_Worldwide__2004_-_present

Youtube  – Youtube has always been one of the most popular destinations for music listeners

Google_Trends_-_Web_Search_interest__youtube_music_-_Worldwide__2004_-_present

Songza – After a pivot in 2011, very strong growth

Google_Trends_-_Web_Search_interest__songza_-_Worldwide__2004_-_present

8tracks – strong growth since 2011

Google_Trends_-_Web_Search_interest__8tracks_-_Worldwide__2004_-_present

Bandcamp

Google_Trends_-_Web_Search_interest__bandcamp_-_Worldwide__2004_-_present

Turntable – after the initial buzz, interest in turntable has declined dramatically.

Google_Trends_-_Web_Search_interest__turntable.fm_-_Worldwide__2004_-_present

Mixcloud – strong steady growth since 2009

Google_Trends_-_Web_Search_interest__mixcloud_-_Worldwide__2004_-_present

MOG – peaked in 2012

Google_Trends_-_Web_Search_interest__mog_music_-_Worldwide__2004_-_present

Jango – peaked in January of this year, but have since dropped to 2010 interest levels

Google_Trends_-_Web_Search_interest__jango_-_Worldwide__2004_-_present

Playlist.com – peaked in 2009, now at its lowest interest since 2007.

Google_Trends_-_Web_Search_interest__playlist.com_-_Worldwide__2004_-_present

soundhound – slightly off from its 2012 peak interest.

Google_Trends_-_Web_Search_interest__soundhound_-_Worldwide__2004_-_present

shazam – strong steady, rising interest

Google_Trends_-_Web_Search_interest__shazam_-_Worldwide__2004_-_present

Beatport – holding steady at 2009 levels

Google_Trends_-_Web_Search_interest__beatport_-_Worldwide__2004_-_present

Muve – steady growth since 2011

Google_Trends_-_Web_Search_interest__muve_-_Worldwide__2004_-_present

The Hype Machine – six years of decline

Google_Trends_-_Web_Search_interest__hypemachine__hype_machine__-_Worldwide__2004_-_present

ex.fm – a jagged two year climb

Google_Trends_-_Web_Search_interest__ex.fm_exfm_-_Worldwide__2004_-_present

Amazon MP3 – growing until 2011, when it flattens out, and perhaps drops a bit.

Google_Trends_-_Web_Search_interest___amazon_mp3__-_Worldwide__2004_-_present

Walmart Music – at its lowest point ever

Google_Trends_-_Web_Search_interest__walmart_music_-_Worldwide__2004_-_present

Yahoo Music – Once the biggest destination on the web, now at its lowest point.

Google_Trends_-_Web_Search_interest__yahoo_music_-_Worldwide__2004_-_present

Myspace Music – steady decline until there’s nothing left

Google_Trends_-_Web_Search_interest___myspace_music__-_Worldwide__2004_-_present

Facebook Music – the only service where the downward trend started before the product was announced.

Google_Trends_-_Web_Search_interest___facebook_music__-_Worldwide__2004_-_present

Twitter Music –  perhaps the strangest graph at all. Lots of excitement at launch and then, almost instantly … meh.

Google_Trends_-_Web_Search_interest___twitter_music__-_Worldwide__2004_-_present-2

Zune – bursts of activity with every Zune update, but a steady decline to irrelevance.

Google_Trends_-_Web_Search_interest__zune_-_Worldwide__2004_-_present

xbox music –  modest decline since the October 2012 release, but too early to tell.

Google_Trends_-_Web_Search_interest___xbox_music__-_Worldwide__2004_-_present

Radionomy – I’d never heard of them before, but they are gaining interest, especially in France.

Google_Trends_-_Web_Search_interest__radionomy_-_Worldwide__2004_-_present

Sony’s Music Unlimited – growing since 2010

Google_Trends_-_Web_Search_interest__music_unlimited_-_Worldwide__2004_-_present

Radio.com – Waning interest since 2009

Google_Trends_-_Web_Search_interest__radio.com_-_Worldwide__2004_-_present

Of course, these search trends are not the same as having an actual measure of activity. Millions of people play music on Spotify or iTunes every day without performing a search. However, until we can get raw user numbers from every music service, this is probably about the closest we can get to understanding which services are growing and which are shrinking.

Leave a comment if you think there are some music listening services that I’ve missed that I should include.

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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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Github repositories of music tech

There are a lot of music tech companies working to create new ways for people to engage with music. Lots of these companies are also giving back to the world by making their source code available.  Here are the top music tech companies who have made significant open source contributions (in alphabetical order). Criteria to be on this list: The organization must be primarily a music company (sorry, google and twitter) that has participated in a Music Hack Day and must have at least three 10-star or more github projects. If I’ve missed anyone, please let me know.

Last.fm – 23 public repos. Top Projects:

  • lastfm-deskiop – 166 stars – The official Last.fm desktop application suite
  • Fingerprinter – 160 stars –  the official repository for the last.fm fingerprint library.
  • libmoost – 122 stars – Last.fm’s collection of C++ utility libraries

Rdio – 31 public repos. Top projects:

  • Vernacular  – 95 stars –  a localization tool for developers. It currently is focused on providing a unified localization system for MonoTouchMono for Android, and Windows Phone.
  • Bujagali – 90 stars – Bujagali is a flexible template system that is a thin layer on top of JavaScript which makes it easier to write HTML (or any templated text) using JavaScript
  • rdio-simple – 83 stars – a set of simple clients libraries for Rdio’s web API.

SongKick – 52 public repos – Top Projects

  • oauth2-provider –  334 stars – Simple OAuth 2.0 provider toolkit
  • transport – 40 stars –  A transport layer abstraction for talking to service APIs
  • aspec – 10 stars – a testing language for API external surfaces.

SoundCloud – 123 public repos – Top projects:

Spotify – 28 public repos – Top projects:

  • luigi – 682 stars –  Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
  • cocoalibspotify – 425 stars – A Cocoa wrapper for libpotify
  • sparkey – 161 stars – Sparkey is a simple constant key/value storage library.

The Echo Nest – 42 public repos. Top Projects:

  • Echoprint-codegen – 323 stars – Echoprint is an open source music fingerprint and resolving framework powered by the The Echo Nest.
  • pyechonest – 258 stars – Pyechonest is an open source Python library for the Echo Nest API. With Pyechonest you have Python access to the entire set of API methods.
  • Echoprint-server – 212 stars – the server component for Echoprint – an open source music fingerprint and resolving framework powered by the The Echo Nest.

Misc:
A few companies / organizations have only one frequently starred repos, but since it is their entire source code, it seems worth mentioning.

  • MuseScore – 135 stars – MuseScore is a open source and free music notation software
  • Tomahawk-player – 445 stars – Tomahawk, the social music player app

Criteria to be on this list: The organization must be primarily a music company (sorry, google and twitter) that has participated in a Music Hack Day and must have at least three 10-star or more github projects. If I’ve missed anyone, please let me know.

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Are these the angriest tracks on the web?

I built a playlist of songs that most frequently appear in playlists with the words angry or mad with the Smart Playlist Builder. These are arguably some of the angriest tracks on the web.

http://www.rdio.com/people/plamere/playlists/5779446/Top_angry_songs_created_with_SPB/

It is interesting to compare these angry tracks to the top tracks tagged with angry at Last.fm.

Top_Tracks_tagged_as_‘angry’_–_Music_at_Last.fm

I can’t decide whether the list derived from angry playlists is better or worse than the list driven by social tags. I’d love to hear your opinion. Take a look at these two lists and tell me which list is a better list of angry tracks and why.

yep, this is totally unscientific poll, but I’m still interested in what you think.

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Top cover songs

I’ve always been a big fan of cover songs. They provide a great way to experience old music in a new way. They can help you discover a new artist or a new genre – by combining the familiar with the novel.  To build the ultimate cover song playlist I used the Smart Playlist Builder to create a playlist of covers that most frequently appear in cover songs playlists. These are the essential covers.  Have a listen:

http://www.rdio.com/people/plamere/playlists/5775766/Top_covers_songs_via_SPB/

You can read more about the Smart Playlist Builder and create your own wisdom-of-the-crowds playlists.

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Using the wisdom of the crowds to build better playlists

At music sites like Rdio and Spotify, music fans have been creating and sharing music playlists for years. Sometimes these playlists are carefully crafted sets of songs for particular contexts like gaming or sleep and sometimes they are just random collections of songs.  If I am looking for music for a particular context, it is easy to just search for a playlist that matches that context.  For instance, if I am going on roadtrip there are hundreds of roadtrip playlists on Rdio for me to chose from. Similarly, if I am going for a run, there’s no shortage of running playlists to chose from.  However, if I am going for a run, I will need to pick one of those hundreds of playlists, and I don’t really know if the one I pick is going to be of the carefully crafted variety or if it was thrown together haphazardly, leaving me with a lousy playlist for my run.   Thus I have a problem –  What is the best way to pick a playlist for a particular context?

Naturally, we can solve this problem with data.  We can take a wisdom of the crowds approach to solving this problem. To create a running playlist, instead of relying on a single person to create the playlist, we can enlist the collective opinion of everyone who has ever created a running playlist to create a better list.

I’ve built a web app to do just this. It lets you search through Rdio playlists for keywords. It will then aggregate all of the songs in the matching playlists and surface up the songs that appear in the most playlists.  So if Kanye West’s  Stronger appears in more running playlists than any other song, it will appear first in the resulting playlist.  Thus songs, that the collective agree are good songs for running get pushed to the top of the list.  It’s a simple idea that works quite well. Here are some example playlists created with this approach:

Best Running Songs

http://www.rdio.com/people/plamere/playlists/5773579/Top_best_running_songs_via_SPB/

Coding

http://www.rdio.com/people/plamere/playlists/5773559/Top_coding_songs_via_SPB/

Sad Love Songs

http://www.rdio.com/people/plamere/playlists/5773508/Top_sad_love_songs_songs_via_SPB/

Chillout

http://www.rdio.com/people/plamere/playlists/5773867/Top_chillout_songs_via_SPB/

Date Night

http://www.rdio.com/people/plamere/playlists/5773474/Top_date_night_songs_via_SPB/

Sexy Time

http://www.rdio.com/people/plamere/playlists/5773535/Top_sexytime_songs_via_SPB/

This wisdom of the crowds approach to playlisting isn’t limited to contexts like running or coding, you can also use it to give you an introduction to a genre or artist as well.

Country

http://www.rdio.com/people/plamere/playlists/5773544/Top_country_songs_via_SPB/

Post Rock

http://www.rdio.com/people/plamere/playlists/5773642/Top_post_rock_songs_via_SPB/

Weezer

http://www.rdio.com/people/plamere/playlists/5773606/Top_weezer_songs_via_SPB/

The Smart Playlist Builder

The app that builds these nifty playlists is called The Smart Playlist Builder.  You type in a few keywords and it will search Rdio for all the matching playlists.  It will show you the matching playlists, giving you a chance to refine your query.  You can search for words, phrases and you can exclude terms as well. The query sad “love songs” -country will search for playlists with the word sad,  and the phrase love songs in the title, but will exclude any that have the word country.

Smart_Playlist_Builder

When you are happy with your query you can aggregate the tracks from the matching playlists. This will give you a list of the top 100 songs that appeared in the matching playlists.

Smart_Playlist_Builder

If you are happy with the resulting playlist, you can save it to Rdio, where you can do all the fine tuning of the playlist such as re-ordering, adding and deleting songs.

Top_sad_love_songs_-country_songs_via_SPB_–_Rdio

The Smart Playlist Builder uses the really nifty Rdio API. The Rdio folks have done a fantastic job of giving developers access to their music and data. Well done Rdio team!

Go ahead and give The Smart Playlist Builder a try to see how the wisdom of the crowds can help you make playlists.

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The Most Replayed Songs

rocklobsterI 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)

  1. Robin Thicke — Blurred Lines featuring T.I., Pharrell
  2. Jay-Z — Holy Grail featuring Justin Timberlake
  3. Miley Cyrus — We Can’t Stop
  4. Imagine Dragons — Radioactive
  5. 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.

click to open an interactive version of this chart.

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

  1. 91% replays   White Noise For Baby Sleep — Ocean Waves
  2. 86% replays   Eric West — Reckless (From Playing for Keeps)
  3. 86% replays   Soundtracks For The Masters — Les Contes D’hoffmann: Barcarole
  4. 83% replays   White Noise For Baby Sleep — Warm Rain
  5. 83% replays   Rain Sounds — Relax Ocean Waves
  6. 82% replays   Dennis Wilson — Friday Night
  7. 81% replays   Sleep — Ocean Waves for Sleep – White Noise
  8. 74% replays   White Noise Sleep Relaxation White Noise Relaxation: Ocean Waves 7hz
  9. 74% replays   Ween — Ocean Man
  10. 73% replays   Children’s Songs Music — Whole World In His Hands
  11. 71% replays   Glee Cast — Friday (Glee Cast Version)
  12. 63% replays   Rain Sounds — Rain On the Window
  13. 63% replays   Rihanna — Cheers (Drink To That)
  14. 60% replays   Group 1 Crew — He Said (feat. Chris August)
  15. 59% replays   Karsten Glück Simone Sommerland — Schlaflied für Anne
  16. 56% replays   Monica — With You
  17. 54% replays   Jessie Ware — Wildest Moments
  18. 53% replays   Tim McGraw — I Like It, I Love It
  19. 53% replays   Rain Sounds — Morning Rain In Sedona
  20. 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:

  1. 31% replays   Miley Cyrus — The Climb
  2. 16% replays   August Alsina — I Luv This sh*t featuring Trinidad James
  3. 15% replays   Brad Paisley — Whiskey Lullaby
  4. 14% replays   Tamar Braxton — The One
  5. 14% replays   Chris Brown — Love More
  6. 14% replays   Anna Kendrick — Cups (Pitch Perfect’s “When I’m Gone”)
  7. 13% replays   Avenged Sevenfold — Hail to the King
  8. 13% replays   Jay-Z — Big Pimpin’
  9. 13% replays   Labrinth — Beneath Your Beautiful
  10. 13% replays   Karmin — Acapella
  11. 12% replays   Lana Del Rey — Summertime Sadness [Lana Del Rey vs. Cedric Gervais]
  12. 12% replays   MGMT — Electric Feel
  13. 12% replays   One Direction — Best Song Ever
  14. 12% replays   Big Sean — Beware featuring Lil Wayne, Jhené Aiko
  15. 12% replays   Chris Brown — Don’t Think They Know
  16. 11% replays   Justin Bieber — Boyfriend
  17. 11% replays   Avicii — Wake Me Up
  18. 11% replays   2 Chainz — Feds Watching featuring Pharrell
  19. 10% replays   Paramore — Still Into You
  20. 10% replays   Alicia Keys — Fire We Make
  21. 10% replays   Lorde — Royals
  22. 10% replays   Miley Cyrus — We Can’t Stop
  23. 10% replays   Ciara — Body Party
  24.   9% replays   Marc Anthony — Vivir Mi Vida
  25.   9% replays   Ellie Goulding — Burn
  26.   9% replays   Fantasia — Without Me
  27.   9% replays   Rich Homie Quan — Type of Way
  28.   9% replays   The Weeknd — Wicked Games (Explicit)
  29.   9% replays   A$AP Ferg — Work REMIX
  30.   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:

http://www.rdio.com/people/plamere/playlists/5733386/Most_replayed/

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:  

  1. Body Party by Ciara
  2. Still Into You by Paramore
  3. Tapout featuring Lil Wayne, Birdman, Mack Maine, Nicki Minaj, Future by Rich Gang
  4. Part II (On The Run) featuring Beyoncé by Jay-Z
  5. Feds Watching featuring Pharrell by 2 Chainz
  6. Royals by Lorde
  7. V.S.O.P. by K. Michelle
  8. Just Give Me A Reason by Pink
  9. Don’t Think They Know by Chris Brown
  10. Wake Me Up by Avicii

There’s an Rdio playlist of these songs: Most Manually Replayed

So what?
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:

http://www.rdio.com/artist/Fifth_Harmony/album/Miss_Movin%27_On/track/Miss_Movin%27_On/

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…

http://www.rdio.com/artist/The_B-52%27s/album/Rock_Lobster_/_6060-842_(Digital_45)/track/Rock_Lobster/

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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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