Archive for category code

50 Years Ago in Music

There’s a strong connection between music and memory. Whenever I here the song Lovin You by Minnie Riperton, I’m instantly transported back to 1975 when I spent the summer apprenticed to Tom, my future brother-in-law, fixing electronic organs. I was 15, Tom was 22 and super cool. He had a business (New Hampshire Organ Service) and he had a van with an 8-track player and an FM radio (a rarity in 1975). As we drove between repairs across rural New Hampshire we’d pass the time by listening to the radio.  Now, when I hear those radio songs from 1975 it is like I’m sitting in that van again.

Music can be like a time machine.  Transporting us to different times in our lives. I was interested in exploring this a bit more. Inspired by @realtimewwii which gives a day-by-day account of World War II, I created a set of dynamically updating Spotify playlists that follow the charts week-by-week.

For example there’s the 50 Years Ago in Music playlist that contains the top 100 or songs that were on the chart 50 years ago.  As I write this on April 12, 2015, this playlist is showing the top songs for the week of April 12, 1965.

The music on this playlist sends me back to when I was 5 years old listening to music on our AM radio in the kitchen in the morning while eating breakfast.

If you follow this playlist you’ll be able to re-create what it was like to listen to music 50 years ago.  If the mid-sixties doesn’t speak to you musically, there are some other playlists that you can try.

There’s 40 Years Ago in Music that brings me back to 1975 on the road with Tom.

There’s 30 years Ago in Music which is currently playing music from the mid-80s like Madonna and Phil Collins.

There’s 20 Years Ago in Music currently playing music from the mid-90s:

10 Years Ago in Music plays the music that was on the radio when Spotify was just a gleam in Daniel’s eye.

5 Years Ago in Music – the playlist of @echonest in its heyday.

All of the playlists update weekly on Monday. If you’d like a reminder about when they are updated you can follow @50yearmusic. And of course, the code is on github.

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The Playlist Miner

The Spotify Web API team pushed out a new feature recently that extends the search API to include playlist search. With this new feature it is now possible to search across all of the popular public playlists created by Spotify users. To try out the new search capability I created a new web app called The Playlist Miner.

The Playlist Miner is a web app that will create a Spotify playlist for you by finding the top songs in all of the playlists that match your criteria. Say, for example, that I want to create a dinner party playlist.  First, I find the top playlists that match ‘dinner party’ with The Playlist Miner:

The_Playlist_Miner

 

The Playlist Miner will find up to the top 1,000 most popular playlists that match dinner party. It shows them to me, giving me a chance to refine my query to focus in on the exact type of playlist that I am interested in.

 

The_Playlist_Miner

 

For this first try, I see lots of Christmas-oriented playlists (‘Tis the Season after all), but since I’m looking for music for a post-holiday dinner party, I’d rather not have holiday music in the playlist. So I refine my query to find non-Christmas oriented dinner party playlists like so:

 

The_Playlist_Miner

 

The resulting playlists are suitably non-Christmasy.

 

The_Playlist_Miner

I like the look of these playlists so I hit the Find Top Tracks button and The Playlist Miner will scour through all of the matching playlists (290 of them in this case) and find the most frequently appearing tracks.

The_Playlist_Miner

Once the top 100 tracks are found, I can save them to Spotify as my own playlist.

Selecting Prefer more distinctive labor and delivery tracks adjusts the track order for popularity so that tracks that are more distinctive to the particular playlist context will rise to the top. You can also use logical operators to focus in on the exact type of playlist you want to. You can search for “work out” OR workout NOT running  to find workout playlists without running in their titles/descriptions.

Under the hood – The Playlist Miner uses lots of bits of the Spotify API – user authentication, playlist search, playlist reading, playlist saving and more. The app is a an API calling beast – aggregating all the tracks from a thousand playlists requires 1,000 API calls. It’s a testament to the Spotify Web API that it doesn’t even blink under the load.  You can play with the code on github.

It’s fun to use The Playlist Miner to explore the quirkier aspects of how people listen to music. There are ironing playlists and sleeping baby playlists. There are playlists for getting psyched and playlists for Labor and Delivery.  With the Playlist Miner you can pull from all the playlists created for a particular purpose and build your own.  Give it a try.

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‘Tis the Season

‘Tis the season for artists to release Christmas music … and they release lots of it. In the last two weeks Spotify has added thousands of releases with ‘Christmas’ in the title.   I though it would be fun to build a little web app that lets you explore through all the releases.  Here it is: ‘Tis the Season.

_Tis_the_season

It shows you all the Christmas albums that have been released in the last few weeks, lets you listen to them and lets you open them in Spotify.

It makes use of the Spotify Web API – there’s a nifty search feature that lets you restrict album searches to albums that have just been recently release. That’s what makes this app possible.  Check out the app at ‘Tis the Season. The source is on github.

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Tracking play coverage in the Infinite Jukebox

Yesterday, I upgraded the Infinite Jukebox to make it less likely that it would get stuck in a section of the song. As part of this work, I needed an easy way to see the play coverage in the song. To do so, I updated the Infinite Jukebox visualization so that it directly shows play coverage. With this update, the height of any beat in the visualization is proportional to how often that beat has been played relative to the other beats in the song. Beats that have been played more have taller bars in the visualization.

This makes it easy to see if we’ve improved play coverage. For example, here’s the visualization of Radiohead’s Karma Police with the old play algorithm after about an hour of play:
Infinite_Jukebox_for_Karma_Police_by_Radiohead

As you can see, there’s quite a bit of bunching up of plays in the third quarter of the song (from about 7 o’clock to 10 o’clock). Now compare that to the visualization of the new algorithm:

Infinite_Jukebox_for_Karma_Police_by_Radiohead

With the new algorithm, there’s much less bunching of play. Play is much more evenly distributed across the whole song.

Here’s another example.  The song First of the Year (Equinox) by Skrillex played for about seven hours with the old algorithm:

Infinite_Jukebox_for_Equinox_by_Skrillex

As you can see, it has quite uneven coverage. Note the intro and outro of the song are almost always the least played of any song, since those parts of the song typically have very little similarity with the rest of the song.

Here’s the same song with the new algorithm:

Infinite_Jukebox_for_Equinox_by_Skrillex

Again, play coverage is much more even across all of the song outside of the intro and the outro.

I like this play coverage visualization so much that I’ve now made it part of the standard Infinite Jukebox. Now as you play a song in the Jukebox, you’ll get to see the song coverage map as well. Give it a try and let me know what you think.

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Infinite Jukebox improved

It has been over two years since the Infinite Jukebox was first released after Music Hack Day Boston 2012.  Since then millions of people have spent nearly a million hours listening to infinite versions of their favorite songs. It has been my most popular hack.

There has always been a problem with the Infinite Jukebox. Certain songs have sections with very dense interconnections. For these songs the Infinite Jukebox would sometimes get stuck playing the same section of the song for many minutes or hours before breaking free.  This morning I finally sat down and worked out a good way to deal with this problem. The Infinite Jukebox will now try to steer the song toward the beats that have been played the least. When the jukebox is deciding which beat to play next, it will search through all the possible future paths up to five beats into the future to find the path that brings the jukebox to the least played part of the song.  The result is that we exit out of the rats nest of connections rather quickly.  The code is quite succinct – just 20 lines in one recursive function.  Good payback for such a small amount of code.

Infinite_Jukebox_for_Turn_Down_by_Rittz

While I was in the codebase, I made a few other minor changes. I switched around the color palettes to favor more green and blue colors, and I use a different color to draw the beat connections when we make a jump.

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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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