Posts Tagged fun
The Fresh 40
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.
The Fresh 40 was built on top of the increasingly marvelous Spotify Web API. Code is on github.
Fresh Faces on Spotify
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.
The Unfollower
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.
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.
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:
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.
More on “Where’s the Drama?”
Posted by Paul in code, events, Music, music hack day, Spotify, The Echo Nest on September 8, 2014
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).
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.
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.
Minimizing my Karaoke pain
Rumor has it from some of the Echo Nest gang that went to Stockholm last week for new employee orientation that there is some sort of mandatory Karaoke requirement. Now for some, I’m sure this is great fun, but for others, like myself, not so much. I thought it would be best to prepare for my own mandatory Karaoke by finding some very short songs in order to minimize my time on stage. To do this I went through a database of the top Billboard songs of the last 60 years to find the shortest songs. Here are some of the top shortest popular songs of the last 60 years:
| Length(Seconds) | Artist/Title | Date |
| 76 | Anna Kendrick Cups | 2013-01-14 |
| 78 | Zac Efron What I’ve Been Looking For (Reprise) | 2006-02-13 |
| 83 | Buchanan & Goodman Santa And The Satellite (Part I) | 1957-12-25 |
| 92 | Audrey Dear Elvis (Page 1) | 1956-09-24 |
| 96 | Fats Domino Whole Lotta Loving | 1958-11-19 |
| 98 | Glee Cast Isn’t She Lovely | 2011-05-30 |
| 99 | Maurice Williams & The Zodiacs Stay | 1960-10-05 |
| 101 | Swinging Blue Jeans, The Hippy Hippy Shake | 1964-03-09 |
| 103 | Peter, Paul & Mary Settle Down (Goin’ Down That Highway) | 1963-01-21 |
| 105 | Four Tops Ain’t That Love | 1965-08-02 |
| 105 | Fats Domino Shu Rah | 1961-03-22 |
| 105 | Chuck Berry Let It Rock | 1960-02-03 |
| 107 | Lucas Gabreel & Ashley Tisdale Bop To The Top | 2006-02-13 |
| 107 | Beach Boys, The Little Deuce Coupe | 1963-08-19 |
| 107 | Clyde McPhatter Lover Please | 1962-03-05 |
| 108 | Ventures, The Hawaii Five-O | 1969-03-10 |
| 110 | Glee Cast Sing! | 2010-11-01 |
| 110 | Glee Cast It’s My Life / Confessions Part II | 2009-10-26 |
| 110 | Ricky Nelson If You Can’t Rock Me | 1963-04-22 |
So it looks like my minimum possible karaoke pain will be 76 seconds if I go with Anna Kendrick’s Cups. Certainly better than Gun’s in Roses November Rain at 8:57 seconds or Don Mclean’s American Pie at 6:49. But better yet, I can go with Hawaii Five-O . That song is not only short, but has no vocals. With that song I’m sure to be pitch perfect!
What happens at a Music Hack Day?
So what is a Music Hack Day really like? Here’s a quick rundown of some of the things that will happen at the Music Hack Day.
9AM – 10AM Saturday morning – Registration – hackers start to arrive between 9AM and 10AM for registration.
Welcome to the Microsoft NERD
You get your badge, wander around the venue (which is graciously provided by Microsoft) have a muffin and a cup of coffee and meet up with old friends and / or make new friends.
Smiling faces greet you at MHD Registration
Informal hacking space at the NERD
This is a good time to start scoping around for hacking partners if you think you might need some help building a hack. If you want to get a head start in connecting with other hackers consider posting to the Ideas page on the Music Hack Day Boston Wiki.
Group hacking
Meet interesting people at Music Hack Day
10AM – Opening remarks – someone will walk you through the weekend, and give you an idea of what to expect, and tell you all the important info like where the bathrooms are.
Dave Haynes kicks of Music Hack Day London
10:15 – Lightning pitches – a big part of the Music Hack Day is the sharing of knowledge about various music-related technologies, whether it’s an API, a library, technique, hardware gizmo. During the lightning pitches, everyone who has a technology that they’d like to talk about gives a 60 second description of their technology.
Getting ready for the lightning rounds
11:00 Morning Workshops – The morning workshops are 15 to 20 minute overviews of a particular technology. There may be be 10 to 20 workshops run in a two hour period. Depending on the number workshop, there may be more than one track, so pay attention during the lightning pitches to figure out which workshops you’d like to attend.
Waiting for the workshops to start
Some companies offer prizes to the best hack that uses their technology. If you want to try to win something cool like a concert tickets, an iPad, or some cash, pay attention to the workshops to see who is giving out prizes.
The Echo Nest workshop
The Songkick workshop
1:00PM Lunch time – lunch is free – but don’t let it stop you from hacking.
Eat or hack?
2:00PM Hacking commences – after lunch – formal hacking begins. You have 24 hours to build something cool that is related to music. Hacking will finish up at 2:30PM on Sunday.
Hacking commences
2:00PM Afternoon workshops – In the afternoon, there are some in-depth workshops where you can learn how to build or do something. The workshops (as with everything at the Music Hack Day), is optional, you can chose to attend a workshop or find a quiet spot and work on your hack. Some of the workshops may be scheduled on an ad hoc basis (anyone can put on a workshop, just post a note on the ‘workshops board’). There are 3 hardware oriented workshops, at the Boston Music Hack Day that will give you an opportunity to build or interact with hardware (a rare thing for many of us software types).
Pen plotter & Chiplotle workshop Douglas Repetto and Brian Whitman will show you how to plot your beautiful music visualizations or whatever else on quite possibly the sexiest of all paper output mechanisms — early 1980s HPGL pen plotters! You’ll learn how to use Chiplotle, a Python bridge for live HPGL drawing control, and we’ll have a a few plotters on hand for everyone to use. Plus, there will be plotters available for hacking after the workshop.
More info:
- http://music.columbia.edu/cmc/chiplotle/
- http://www.flickr.com/groups/chiplotle/pool/
- http://www.youtube.com/watch?v=T20-KcCGokU
Electric Eels Workshop
“Electric Eels” workshop – by Noah Vawter – This project introduces a mobile platform for electronic music instruments. It encouragesplaying them more like traditional instruments. The new techniques this project introduces extend the role of electricity generation – Electrical energy for every note comes from its players’ movements.
Atari Punk Console workshop
Jimmie Rodgers will lead a workshop on building Atari Punk Consoles. The Atari Punk Console is a simple synthesizer with a wide range of sounds. It is so named because the sounds generated are similar to those of the early Atari consoles. In this workshop you will learn the basics of the timing circuits used to make these crazy sounds. You will also learn how to solder, and how to change the the sounds with your fingers as the contact, or even using light to make a simple light Theremin.
Jimmie's workshop
All parts and tools included in the costs. Additional parts will be available, so you will be able to customize your APC with lights, touch contacts, photo resistors, etc. You are encouraged to find a fun case for your APC and bring it (cigar box, mint tin, Mr. Potato Head other plastic toy, etc), anything hollow that can hold a deck of cards would do. If you do not have a case, then Altoid tins will be available at the workshop.
Hardware hacking
6PM – Dinner – At 6PM the pizzas arrive. Get a slice or two and some soda (the beer comes later), and get back to your hack. Remember, eating and sleeping is for the weak
Eating is for the weak
9:30 PM – Leg stretching time. – Hacking continues until around 9:30 PM when the Microsoft NERD will close. At that point hacking shifts to the Echo Nest (a few T stops away).
Onto the T to get to the Echo Nest
10:00 PM Hacking Continues at the Echo Nest
The Echo Nest offices
The venue for overnight hacking is at the Echo Nest in Davis Square – 4 stops away on the T from the main venue. At the Echo Nest there will be some beer, some live music provided by Javelin, and plenty of comfortable hacking space for the hardcore hackers.
Late night hacking
Overnight hacking is where all the magic happens.
Overnight hacking in London
Overnight hacking at the Echo Nest
8AM Sunday Morning – On Sunday morning, hacking finishes up at the Echo Nest and will return to the Microsoft NERD at 9AM, where you’ll find some more coffee and light breakfast. Hacking continues thorough lunch.
Remember, eating is for the weak.
2:30 PM Hacking finishes. By 2:30 PM you should have information about your hack posted to the wiki. Only hacks included on the wiki by 2:30 PM will be included in the final presentations.
Last minute hacking
3:00 PM – Final Demos – This is the time to show your stuff! Once all the hacks are complete, everyone will move to the big room for the hack demos. We’ll be joined by about 100 non-hackers who are here to see all the demos. During the demo time, everyone who managed to get a hack listed onto the hacks wiki by 2:30 will get 2 minutes to present their hack.
Giving a demo - Flight of the bumble bee on guitar
There will be people from the music industry, the press, and the tech world in attendance so it doesn’t hurt to try to make your 2 minutes in the spotlight memorable. We should have two projectors setup so each hack presenter should have 2 minutes to setup and then 2 minutes to present.
Demoing the big piano
Ben demos the roomba recon
Giving good demo is fun
We will be ruthless with the timing. When your 2 minutes are up, we’ll start the next demo, so make sure you get to the point quickly
5:00 PM – final awards – At 5PM the panel of judges will gather to pick the winners of the prizes, and present the awards. But of course, everyone is a winner.
Announcing the prize winners
6:00PM – After all is done, we will find a local pub to get some refreshment.
Time for a beer
It is sure to be a good time.
Photos from flickr photographers: Elizabeth Thomsen, Dave Haynes, Thomas Bonte, Ben Reyes, Brad Searles, aroscoe, habber, David Noël, imelda, James Wheare, narq,
The Name Dropper
Posted by Paul in data, fun, Music, The Echo Nest, web services on July 10, 2010
[tweetmeme source= ‘plamere’ only_single=false]
TL;DR; I built a game called Name Dropper that tests your knowledge of music artists.
One bit of data that we provide via our web APIs is Artist Familiarity. This is a number between 0 and 1 that indicates how likely it is that someone has heard of that artists. There’s no absolute right answer of course – who can really tell if Lady Gaga is more well known than Barbara Streisand or whether Elvis is more well known than Madonna. But we can certainly say that The Beatles are more well known, in general, than Justin Bieber.
To make sure our familiarity scores are good, we have a Q/A process where a person knowledgeable in music ranks our familiarity score by scanning through a list of artists ordered in descending familiarity until they start finding artists that they don’t recognize. The further they get into the list, the better the list is. We can use this scoring technique to rank multiple different familiarity algorithms quickly and accurately.
One thing I noticed, is that not only could we tell how good our familiarity score was with this technique, this also gives a good indication of how well the tester knows music. The further a tester gets into a list before they can’t recognize artists, the more they tend to know about music. This insight led me to create a new game: The Name Dropper.
The Name Dropper is a simple game. You are presented with a list of dozen artist names. One name is a fake, the rest are real.
If you find the fake, you go onto the next round, but if you get fooled, the game is over. At first, it is pretty easy to spot the fakes, but each round gets a little harder, and sooner or later you’ll reach the point where you are not sure, and you’ll have to guess. I think a person’s score is fairly representative of how broad their knowledge of music artists are.
The biggest technical challenge in building the application was coming up with a credible fake artist name generator. I could have used Brian’s list of fake names – but it was more fun trying to build one myself. I think it works pretty well. I really can’t share how it works since that could give folks a hint as to what a fake name might look like and skew scores (I’m sure it helps boost my own scores by a few points). The really nifty thing about this game is it is a game-with-a-purpose. With this game I can collect all sorts of data about artist familiarity and use the data to help improve our algorithms.
So go ahead, give the Name Dropper a try and see if you can push me out of the top spot on the leaderboard:
Play the Name Dropper
Music Machinery 