Posts Tagged code
Every week, thousands of artists release albums on Spotify. Sifting through all this new music to find good stuff to listen to can be hard. Luckily, there are lots of tools from New Music Tuesday playlists to the Spotify Viral 50 to help us find the needles in the proverbial haystack of new music. However, most of these tools tend to surface up new music by artists that have been around for a while. For instance, the top artist on Spotify Viral 50 as I write this is Jeremih who has been on the charts for five years. The top of New Music Tuesday right now is Mumford & Sons who’ve been recording for at least eight years.
I’m interested in finding music by the freshest artists – artists that are at the very beginning of their recording careers. To that end, I’ve built a new chart called ‘The Fresh 40’ that shows the top albums by the freshest artists. To build The Fresh 40 I scour through all of the albums that have been released in the last two weeks on Spotify (on average that’s about 30 thousand albums), and find the albums that are the very first album release for its artist. I then rank each album by a weighted combination of the number of followers the artist has on Spotify and the popularity of the artist and album (which is related to Spotify track plays). The result is a chart of the top 40 most popular fresh artists.
The Fresh 40 updates every day and shows all the salient info including the rank, yesterday’s rank, the overall score, artist followers, artist popularity, album popularity and the number of days that the album has been on the chart. Since an album can only be on the chart for 15 days, there’s quite a bit of change from day to day.
If you are interested in finding music by the very newest artists on Spotify, you might be interested in The Fresh 40. Give the chart a look.
My weekend programming project this week was to explore a new feature of the Spotify Web API that allows you to find albums that have been released in the last two weeks. The result is a web app called Fresh Faces. This app goes through all of the recent releases and finds those that are the very first release for the artist. If you are looking for new music, there’s no fresher place to start than this app – it finds the newest music by the freshest artists – artists that are barely two weeks into their recording career.
Fresh Faces lets you sort the results based on artist popularity, album popularity, artist followers or release date. You can click on an album to hear a sample, find more info about the album or open it on Spotify.
How many new releases are there?
I was curious about how many releases there are in a two week period, and when releases tend to happen, so I added a chart at the bottom of the Fresh Faces app that shows the distribution of fresh and recurring releases and the dates when releases happen. You can see that the shift of releasing music from Tuesday to Friday is ongoing.
In the past two weeks about 32,000 albums have been released – about 5,200 of these are the first release for the artist. That’s a whole lot of fresh music.
Give Fresh Faces a try and let me know what you think.
One of the problems with working at a company like Spotify is that my Spotify account gets filled up with all sorts of work-related playlists. Over the last few years I’ve built lots of apps that create playlists. When I test these apps I end up generating lots of playlists that I will never ever listen to. If I were a tidy soul, I’d clean up my playlists after ever project, but, alas, that is something I never do. The result is that after working at Spotify for a year (and using Spotify for 8 years), I’ve accumulated many hundreds of garbage playlists. Now I could go into the Spotify desktop client and clean these up, but in the current client there’s no good way to bulk delete playlists. Each playlist delete takes at least 3 clicks. The prospect of doing this hundreds of times to clean up the playlist garbage is a bit overwhelming.
I had a few hours to kill in a coffeeshop yesterday so I decided to deal with my playlist mess. I wrote a little Spotify web app called The Unfollower that lets you unfollow any of your playlists with a single click. If you change your mind, you can re-follow any playlist that you unfollow.
The Unfollower uses the Spotify Web API to make it all happen. In particular it relies on the Follow/Unfollow API that was recently added by the API team.
If you are like me and have lots of dead playlists clogging up your Spotify, and you are looking for a streamlined way of cleaning them up, give The Unfollower a try.
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.
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:
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:
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:
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:
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.
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:
- When will the Bass Drop – Lonely Island
- Stairway to Heaven – Led Zeppelin
- Doomsday – Nero
- November Rain – Guns N Roses
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).
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:
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:
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.
The Echo Nest engineering team just pushed out a new feature giving you more control over the artist makeup in playlists. There is a new parameter to the playlist/static API called distribution that can be set to wandering or focused. When the distribution is set to wandering the artists will appear with approximately equal distribution in the playlist. If the distribution is set to focused artists that are more similar to the seed artists will appear more frequently. When combined with the variety parameter, you have excellent control over the number and distribution of artists in a playlist. If you want to create a playlist suitable for music discovery, create a playlist with high variety and a wandering distribution. If you want to create a playlist that more closely mimics the radio experience choose a low variety and a focused distribution.
I’ve put together a little demo that lets you create playlists with different levels of variety and distribution settings. The demo will create a playlist given a seed artist and show you the artist distribution for the playlist. Here’s the output of the demo with distribution set to focused:
You can see from the artist histogram that the playlist draws more from artists that are very similar to the seed artist (Weezer). Compare to these results from a wandering playlist with the same seed and variety:
You can see that there is flatter distribution of artists in the playlist. You can use variety and distribution to tailor playlists to the listener. For instance, you can give the Classic Rock Radio experience to a listener by setting variety to relatively low, setting the distribution to focused and seeding with a classic rock artist like Led Zeppelin. Here’s the artist distribution for the resulting playlist:
That looks like the artist rotation for my local classic rock radio.
Give the demo a try to see how you can use variety and distribution to match playlists to your listener’s taste. Then read the playlist API docs to see how to use the API to start incorporating these attributes into your apps.