Archive for category Spotify

How Students Listen

The Spotify Insights team took a deep dive into some of the listening data of college students to see if there were any differences in how students at different schools listen. We looked at a wide range of data including what artists were played, what songs were played and when, what playlists played, what genres were played and so on. We focused mostly on looking for distinctive listening patterns and behaviors at the different schools. The results were a set of infographic style visualizations that summarize the distinctive listening patterns for each school.

How_Students_Listen_and_weezerIt was a fun study to do and really shows how much we learn about listening behavior based upon music streaming behavior.  Read about the study on the Spotify Insights Blog:  Top 40 Musical Universities in America:How Students Listen

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More on “Where’s the Drama?”

My Music Hack Day Berlin hack was “Where’s the Drama?” – a web app that automatically identifies the most dramatic moment in any song and plays it for you. I’ve been having lots of fun playing with it … and even though (or perhaps because) I know how it works, I’m often surprised at how well it does at finding the most dramatic moments.  Here are some examples:

How does it work? The app grabs the detailed audio analysis for the song from The Echo Nest.  This includes a detailed loudness map of the song. This is the data I use to find the drama.  To do so, I look for the part of the song with the largest rise in volume over the course of a 30 second window (longer songs can have a bit of a longer dramatic window). I give extra weight to crescendos that culminate in louder peaks (so if there are two crescendos that are 20dB in range but one ends at 5dB louder, it will win). Once I identify the most dynamic part of a song, I pad it a bit (so we get to hear a bit of the drop after the build).

The rest is just UI – the song gets plotted as a heavily filtered loudness curve with the dramatic passage highlighted. I plot things with Highcharts, which is a pretty nifty javascript plotting and charting library. I recommend.

 Where_is_the_Drama_

Playing the music – I wanted to use Spotify to play the music, which was a bit problematic since there currently isn’t a way to play full streams with the Spotify Web API, so I did a couple of hacky hacks that got me pretty far. First of all, I discovered that you can add a time offset to a Spotify URI like so:

        spotify:track:2SHnUyZq0zwmvRIl4WY77G#1:05

When this URI is opened in Spotify (even when opened via a browser), Spotify will start to play the song a the 1:05 time offset.  

I still needed to be able to stop playing the track – and there’s no way to do that directly – so instead, I just open the URI:

      spotify:track:3nKrmorxtDSxnqvMgJudmV

which happens to be the URI for John Cage’s 4’33.  In other words, to stop playing one track, I just start playing another (that happens to be silent).  The awesome side effect of this is that I’ll be slowly turning anyone who uses “Where’s the Drama?” into experimental music listeners as the Spotify recommendation system responds to all of those John Cage ‘plays’. This should win some sort of ‘hackiest hack of the year’ award.

It was a fun hack to make, and great fun to demo. And now that I have the app, I am no longer wasting time listening to song intros and outros, I can just get to the bit of the song that matters the most. 

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Acrostify – make Spotify playlists with embedded secret messages

For my summer vacation early-morning coding for fun project I revamped my old Acrostic Playlist Maker to work with Spotify.  The app, called Acrostify, will generate acrostic playlists with the first letter of each song in the playlist spelling out a secret message.  With the app, you can create acrostic playlists and save them to Spotify.

 

Screen Shot 2014-08-07 at 6.38.41 AM

The app was built using The Echo Nest and Spotify APIs. The source is on github.

Give it a try at Acrostify.

 

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Outside Lands Recommendations

I am at Outside Hacks this weekend – A hackathon associated with the Outside Lands music festival. For this hack I thought it would be fun to try out the brand new Your Music Library endpoints in the Spotify Web API. These endpoints let you inspect and manipulate the tracks that a user has saved to their music. Since the hackathon is all about building apps for a music festival, it seems natural to create a web app that gives you festival artist recommendations based upon your Spotify saved tracks. The result is the Outside Lands Recommender:

 

Your_Outside_Lands_Festival_Recommendations

 

The Recommender works by pulling in all the saved tracks from your Spotify ‘Your Music’ collection, aggregating the artists and then using the Echo Nest Artist Similar API to find festival artists that match or are similar to those artists. The Spotify API is then used to retrieve artist images and audio samples for the recommendations where they are presented in all of their bootstrap glory.

This was a pretty straight forward app, which was good since I only had about half the normal hacking time for a weekend hackathon. I spent the other half of the time building a festival dataset for hackers to use (as well as answering lots of questions about both the Spotify and Echo Nest APIs).

It has been a very fun hackathon. It is extremely well organized, the Weebly location is fantastic, and the quality of hackers is very high. I’ve already seen some fantastic looking hacks and we are still a few hours from demo time.  Plus, this happened.

Screenshot_7_27_14__12_32_PM

Two more victims

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Fetching my saved tracks from Spotify

The @SpotifyPlatform team just pushed out an update to the Spotify Web API that lets a developer retrieve and manipulate the tracks that a user has saved in ‘Your Music’. To show off this new functionality I wrote a quick demo that shows how to fetch the saved tracks for a Spotify user via the nifty new API.  The demo will first solicit permission from the user, and if the user grants such permissions, the app will then retrieved the saved tracks and show them as a simple list.

My Saved Tracks

 

The demo is on line here: My Saved Tracks and the source is on github.

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Set Listener update – now listen to specific shows in Spotify

Last month I released the Set Listener –  a web app that lets you create a Spotify playlist for an artist’s most recent show.   A frequent request by users has been to allow the creation of a Spotify playlist for any show, not just the most recent one. However, I didn’t want to have to implement an elaborate event search and browse feature (this was supposed to be a quick hack) especially since SetList.fm already implements this browsing. So instead I did a bit of hack to implement this feature – in addition to accepting an artist name, the Set Listener will accept the url of any setlist.fm show. If you enter a setlist.fm show URL, the Set Listener will grab the songs for that show and generate the playlist. Here’s the flow:

Go to setlist.fm and browse until you find your show of interest. Grab the URL

God_Is_an_Astronaut_Concert_Setlist_at_Melnā_Piektdiena__Riga_on_May_19__2014___setlist_fm

 

Paste the URL into the Set Listener and you should be good to go:

The_Set_Listener

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Most Distinctive Fourth of July Songs

Independence day is just a few days away so I spent a bit of time over the weekend digging into the data to see what are the most listened to songs on the Fourth of July. Eliot wrote a great piece about the data for the Spotify Blog: The Most Distinctive Fourth of July Songs in the 50 U.S. States. Here I dig in a bit deeper.

From a musical perspective, The Fourth of July is a very interesting holiday. It’s a big summer outdoor party  – and the music reflects that. On the Fourth, people listen to patriotic songs,  BBQ songs, popular songs, party songs, songs about place and history. People listen to wide range of genres, from rock and pop to folk, country, marches,  and new weird america.   To see what music people listen to on the Fourth I went through about 5,000 playlists that people have created that have ‘fourth of july’ in the title.  I aggregated the songs across all of these playlists and created a playlist of the top 100 or songs.

As you can see, the playlist is a mix of US-centric music and summer party music – which is a pretty good reflection of what people actually listen to on the Fourth of July.  But I wanted to go a bit further and see what music was particularly distinctive for the Fourth of July – as compared to any other summer outdoor party playlist.  To do this, I collected the top most frequently appearing songs on the Fourth of July playlists and scored each song by calculating the ratio  of the play counts that occurred on July 4, 2013 in the U.S. vs. play counts for the song during the following weeks.  Songs that were played much more frequently on the Fourth than during the rest of July get a much higher score.  Ranking songs by this ratio yields the list of distinctive Fourth of July Songs:

I think it is a pretty good list of the music that we listen to more on the Fourth of July than on any other day. There’s a John Philip Sousa march, Woody Guthrie’s This Land is Your Land, Katy Perry’s Firework, lots and lots of country music. Some artists appear more frequently than others – Bruce Springsteen, John Mellencamp and Lee Greenwood are Fourth of July favorites.

I’m always interested in regional differences in how we listen to music, so I looked at the listening in each state in the U.S. to see which of the core Fourth of July songs was listened to more.  If you look just at the most popular Fourth of July song in each state the results are pretty boring – the most popular song in 46 out of 50 states for July 2013 was Party in the USA by Miley Cyrus – but if you look at the distinctive score (the ratio of plays on the fourth to plays during the following weeks), you get a more nuanced view of how people listen to music. Of course we represent this as a map:

fourth-map

So the real question is, are these playlists generated by data mining any good?  As actual playlists to listen to, I don’t think so. They are too incoherent, with jumps that take you from a sappy country song, to a teen pop song, to a march that will give you iPod whiplash. Compare the playlists above to a human-curated Fourth of July Playlist:

This is a much more listenable playlist. However, I think the data mined playlists do provide an excellent starting point – a  big pool of Fourth of July songs.  With a genre and popularity filter these could be turned into decent, listenable playlists. Similarly, give this pool of Fourth of July songs to a team of music curators and they can build some pretty fantastic Fourth of July playlists for every type of music fan.

 

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