Archive for category fun
Last week at the SXSW Music Hack Championship hackathon I built The Autocanonizer. An app that tries to turn any song into a canon by playing it against a copy of itself. In this post, I explain how it works.
At the core of The Autocanonizer are three functions – (1) Build simultaneous audio streams for the two voices of the canon (2) Play them back simultaneously, (3) Supply a visualization that gives the listener an idea of what is happening under the hood. Let’s look at each of these 3 functions:
(1A) Build simultaneous audio streams – finding similar sounding beats
The goal of the Autocanonizer is to fold a song in on itself in such a way that the result still sounds musical. To do this, we use The Echo Nest analyzer and the jremix library to do much of the heavy lifting. First we use the analyzer to break the song down into beats. Each beat is associated with a timestamp, a duration, a confidence and a set of overlapping audio segments. An audio segment contains a detailed description of a single audio event in the song. It includes harmonic data (i.e. the pitch content), timbral data (the texture of the sound) and a loudness profile. Using this info we can create a Beat Distance Function (BDF) that will return a value that represents the relative distance between any two beats in the audio space. Beats that are close together in this space sound very similar, beats that are far apart sound very different. The BDF works by calculating the average distance between overlapping segments of the two beats where the distance between any two segments is a weighted combination of the euclidean distance between the pitch, timbral, loudness, duration and confidence vectors. The weights control which part of the sound takes more precedence in determining beat distance. For instance we can give more weight to the harmonic content of a beat, or the timbral quality of the beat. There’s no hard science for selecting the weights, I just picked some weights to start with and tweaked them a few times based on how well it worked. I started with the same weights that I used when creating the Infinite Jukebox (which also relies on beat similarity), but ultimately gave more weight to the harmonic component since good harmony is so important to The Autocanonizer.
(1B) Build simultaneous audio streams - building the canon
The next challenge, and perhaps biggest challenge of the whole app, is to build the canon – that is - given the Beat Distance Function, create two audio streams, one beat at a time, that sound good when played simultaneously. The first stream is easy, we’ll just play the beats in normal beat order. It’s the second stream, the canon stream that we have to worry about. The challenge: put the beats in the canon stream in an order such that (1) the beats are in a different order than the main stream, and (2) they sound good when played with the main stream.
The first thing we can try is to make each beat in the canon stream be the most similar sounding beat to the corresponding beat in the main stream. If we do that we end up with something that looks like this:
It’s a rat’s nest of connections, very little structure is evident. You can listen to what it sounds like by clicking here: Experimental Rat’s Nest version of Someone Like You (autocanonized). It’s worth a listen to get a sense of where we start from. So why does this bounce all over the place like this? There are lots of reasons: First, there’s lots of repetition in music – so if I’m in the first chorus, the most similar beat may be in the second or third chorus – both may sound very similar and it is practically a roll of the dice which one will win leading to much bouncing between the two choruses. Second – since we have to find a similar beat for every beat, even beats that have no near neighbors have to be forced into the graph which turns it into spaghetti. Finally, the underlying beat distance function relies on weights that are hard to generalize for all songs leading to more noise. The bottom line is that this simple approach leads to a chaotic and mostly non-musical canon with head-jarring transitions on the canon channel. We need to do better.
There are glimmers of musicality in this version though. Every once in a while, the canon channel will remaining on a single sequential set of beats for a little while. When this happens, it sounds much more musical. If we can make this happen more often, then we may end up with a better sounding canon. The challenge then is to find a way to identify long consecutive strands of beats that fit well with the main stream. One approach is to break down the main stream into a set of musically coherent phrases and align each of those phrases with a similar sounding coherent phrase. This will help us avoid the many head-jarring transitions that occur in the previous rat’s nest example. But how do we break a song down into coherent phrases? Luckily, it is already done for us. The Echo Nest analysis includes a breakdown of a song into sections – high level musically coherent phrases in the song – exactly what we are looking for. We can use the sections to drive the mapping. Note that breaking a song down into sections is still an open research problem – there’s no perfect algorithm for it yet, but The Echo Nest algorithm is a state-of-the-art algorithm that is probably as good as it gets. Luckily, for this task, we don’t need a perfect algorithm. In the above visualization you can see the sections. Here’s a blown up version – each of the horizontal colored rectangles represents one section:
You can see that this song has 11 sections. Our goal then is to get a sequence of beats for the canon stream that aligns well with the beats of each section. To make things at little easier to see, lets focus in on a single section. The following visualization shows the similar beat graph for a single section (section 3) in the song:
You can see bundles of edges leaving section 3 bound for section 5 and 6. We could use these bundles to find most similar sections and simply overlap these sections. However, given that sections are rarely the same length nor are they likely to be aligned to similar sounding musical events, it is unlikely that this would give a good listening experience. However, we can still use this bundling to our advantage. Remember, our goal is to find a good coherent sequence of beats for the canon stream. We can make a simplifying rule that we will select a single sequence of beats for the canon stream to align with each section. The challenge, then, is to simply pick the best sequence for each section. We can use these edge bundles to help us do this. For each beat in the main stream section we calculate the distance to its most similar sounding beat. We aggregate these counts and find the most commonly occurring distance. For example, there are 64 beats in Section 3. The most common occurring jump distance to a sibling beat is 184 beats away. There are ten beats in the section with a similar beat at this distance. We then use this most common distance of 184 to generate the canon stream for the entire section. For each beat of this section in the main stream, we add a beat in the canon stream that is 184 beats away from the main stream beat. Thus for each main stream section we find the most similar matching stream of beats for the canon stream. This visualizing shows the corresponding canon beat for each beat in the main stream.
This has a number of good properties. First, the segments don’t need to be perfectly aligned with each other. Note, in the above visualization that the similar beats to section 3 span across section 5 and 6. If there are two separate chorus segments that should overlap, it is no problem if they don’t start at the exactly the same point in the chorus. The inter-beat distance will sort it all out. Second, we get good behavior even for sections that have no strong complimentary section. For instance, the second section is mostly self-similar, so this approach aligns the section with a copy of itself offset by a few beats leading to a very nice call and answer.
That’s the core mechanism of the autocanonizer - for each section in the song, find the most commonly occurring distance to a sibling beat, and then generate the canon stream by assembling beats using that most commonly occurring distance. The algorithm is not perfect. It fails badly on some songs, but for many songs it generates a really good cannon affect. The gallery has 20 or so of my favorites.
(2) Play the streams back simultaneously
When I first released my hack, to actually render the two streams as audio, I played each beat of the two streams simultaneously using the web audio API. This was the easiest thing to do, but for many songs this results in some very noticeable audio artifacts at the beat boundaries. Any time there’s an interruption in the audio stream there’s likely to be a click or a pop. For this to be a viable hack that I want to show people I really needed to get rid of those artifacts. To do this I take advantage of the fact that for the most part we are playing longer sequences of continuous beats. So instead of playing a single beat at a time, I queue up the remaining beats in the song, as a single queued buffer. When it is time to play the next beat, I check to see if is the same that would naturally play if I let the currently playing queue continue. If it is I ‘let it ride’ so to speak. The next beat plays seamlessly without any audio artifacts. I can do this for both the main stream and the canon stream. This virtually elimianates all the pops and clicks. However, there’s a complicating factor. A song can vary in tempo throughout, so the canon stream and the main stream can easily get out of sync. To remedy this, at every beat we calculate the accumulated timing error between the two streams. If this error exceeds a certain threshold (currently 50ms), the canon stream is resync’d starting from the current beat. Thus, we can keep both streams in sync with each other while minimizing the need to explicitly queue beats that results in the audio artifacts. The result is an audio stream that is nearly click free.
(3) Supply a visualization that gives the listener an idea of how the app works
I’ve found with many of these remixing apps, giving the listener a visualization that helps them understand what is happening under the hood is a key part of the hack. The first visualization that accompanied my hack was rather lame:
It showed the beats lined up in a row, colored by the timbral data. The two playback streams were represented by two ‘tape heads’ – the red tape head playing the main stream and the green head showing the canon stream. You could click on beats to play different parts of the song, but it didn’t really give you an idea what was going on under the hood. In the few days since the hackathon, I’ve spent a few hours upgrading the visualization to be something better. I did four things: Reveal more about the song structure, show the song sections, show, the canon graph and animate the transitions.
Reveal more about the song
The colored bars don’t really tell you too much about the song. With a good song visualization you should be able to tell the difference between two songs that you know just by looking at the visualization. In addition to the timbral coloring, showing the loudness at each beat should reveal some of the song structure. Here’s a plot that shows the beat-by-beat loudness for the song stairway to heaven.
You can see the steady build in volume over the course of the song. But it is still less than an ideal plot. First of all, one would expect the build for a song like Stairway to Heaven to be more dramatic than this slight incline shows. This is because the loudness scale is a logarithmic scale. We can get back some of the dynamic range by converting to a linear scale like so:
This is much better, but the noise still dominates the plot. We can smooth out the noise by taking a windowed average of the loudness for each beat. Unfortunately, that also softens the sharp edges so that short events, like ‘the drop’ could get lost. We want to be able to preserve the edges for significant edges while still eliminating much of the noise. A good way to do this is to use a median filter instead of a mean filter. When we apply such a filter we get a plot that looks like this:
The noise is gone, but we still have all the nice sharp edges. Now there’s enough info to help us distinguish between two well known songs. See if you can tell which of the following songs is ‘A day in the life’ by The Beatles and which one is ‘Hey Jude’ by The Beatles.
Show the song sections
As part of the visualization upgrades I wanted to show the song sections to help show where the canon phrase boundaries are. To do this I created a the simple set of colored blocks along the baseline. Each one aligns with a section. The colors are assigned randomly.
Show the canon graph and animate the transitions.
To help the listener understand how the canon is structured, I show the canon transitions as arcs along the bottom of the graph. When the song is playing, the green cursor, representing the canon stream animates along the graph giving the listener a dynamic view of what is going on. The animations were fun to do. They weren’t built into Raphael, instead I got to do them manually. I’m pretty pleased with how they came out.
All in all I think the visualization is pretty neat now compared to where it was after the hack. It is colorful and eye catching. It tells you quite a bit about the structure and make up of a song (including detailed loudness, timbral and section info). It shows how the song will be represented as a canon, and when the song is playing it animates to show you exactly what parts of the song are being played against each other. You can interact with the vizualization by clicking on it to listen to any particular part of the canon.
Wrapping up – this was a fun hack and the results are pretty unique. I don’t know of any other auto-canonizing apps out there. It was pretty fun to demo that hack at the SXSW Music Hack Championships too. People really seemed to like it and even applauded spontaneously in the middle of my demo. The updates I’ve made since then – such as fixing the audio glitches and giving the visualization a face lift make it ready for the world to come and visit. Now I just need to wait for them to come.
In my recent regional listening preferences post I published a map that showed the distinctive artists by state. The map was rather popular, but unfortunately was a source of confusion for some who thought that the map was showing the favorite artist by state. A few folks have asked what the map of favorite artists per state would look like and how it would compare to the distinctive map. Here are the two maps for comparison.
Favorite Artists by State
This map shows the most played artist in each state over the last year. It is interesting to see the regional differences in favorite artists and how just a handful of artists dominates the listening of wide areas of the country.
Most Distinctive Artists by State
This is the previously published map that shows the artists that are listened to proportionally more frequently in a particular state than they are in all of the United States.
The data for both maps is drawn from an aggregation of data across a wide range of music services powered by The Echo Nest and is based on the listening behavior of a quarter million online music listeners.
It is interesting to see that even when we consider just the most popular artists, we can see regionalisms in listening preferences. I’ve highlighted the regions with color on this version of the map:
Favorite Artist Regions
This past weekend was the fourth annual MIDEM Music Hack Day held in Cannes. During about 48 hours 2 dozen or so hackers collected in a beautiful hacking space at the top of the Palais Des Festivals to build something cool with music and technology.
The MIDEM Music Hack Day is no ordinary Music Hack Day. It has a very limited enrollment so only hackers that have demonstrated the ability to create music hacks are invited. Add to that, the fact that there is about 50% more time to build hacks and the result is a set of very high quality hacks.
Martyn Davies, master Music Hack day coordinator, kicked off the event with a talk to the MIDEM attendees about what Music Hack Day is all about. Martyn talked about the things that drive the hackers to spend their weekends hacking on code – in particular how the Music Hack Day is a chance to combined their love for music and technology, be creative and to build something new and cool during the weekend. Martyn demonstrated two representative hacks built at previous Music Hack Days. First he showed the demo given by master hacker @sydlawrence of a hack called Disco Disco Tech. The excitement in Syd’s voice is worth the price of admission alone.
Next he showed one of my favorite Music Hack Day hacks of all time, Johnny Cash is Everywhere by @iainmullen
One of the special features of the MIDEM Music Hack Day is that non-hackers get to pitch their hacking ideas to the hackers about what apps they’d really like to see created over the weekend. There were a number of pitches ranging from a proposal for an artist-centric tool for organizing a creative music production team to a whimsical request to show what the music on the Internet sounds like when it decays. (Here’s one answer). All of the idea pitches were interesting, but here’s the secret. The hackers are not going to build your idea. It’s not because they don’t like your idea, it is because they already have tons of good ideas. The hackers are a very creative bunch, each with a long list of ideas waiting to be built. What the hackers usually lack is a solid block of time to implement their own ideas and so a hackathon is the perfect time to take that best idea on the list and work for a solid 24 hour to get it done. It is rare for a hacker to get excited about building someone else’s idea, when they have so many of their own. As they say: “ideas are cheap, execution is everything“.
Once the opening talks concluded we hackers made their way up to the top of the Palais des Festivals (the heart of MIDEM) to our hacking space. It’s a great space with lots of natural light, a terrace that overlooks the French Riviera, and it is some distance away from the main conference so we were not bothered by stray walk-ons.
To kick things off, we went around the room introducing ourselves, briefly talking about our background skills, and ideas, and almost immediately got to hacking. Since all the hackers were experienced hackers there was no need for the typical API workshops or learning sessions. Everyone knew, for instance, that I was from The Echo Nest and was ready to answer any questions about the Echo Nest API that should arise.
The next 46 hours was a blur of coding, punctuated by food delivery, the sound of the espresso maker and the occasional wandering pigeon.
Hacker Self Portraits
There’s a math error lurking in there …
Early morning coding on the French Riveria
After 48 hours, we gathered in the Innovation Hall to demonstrate what we built. Each hacking team had about three minutes to show their stuff. Eighteen hacks were built. Here are some of my very favorites:
DJ Spotify – built by Yuli Levtov – This is a real hacker’s hack. Yuli had a problem. He wanted to use Spotify when he DJed, but Spotify won’t let you beat match and cross fade songs. In fact, Spotify won’t even let you play two streams at once. So Yuli got to work to make it happen. Along the way Yuli augmented his DJ playlists with BPM and key information from The Echo Nest (using a very clever growl hack). One of the highlights of my MIDEM week was listening to Yuli try to explain what OS virtualization is and how Soundflower works to a room full of Music Biz types. Yuli has a detailed blog post that describes how his hack works. Yuli’s hack was voted the best hack by the hackers. Well done Yuli!
That One Song - by Matt Ogle and Hannah Donovan – For this hack, Matt added a feature to his super popular This Is My Jam site. Type in any band and let the Jam community tell you the one song you should hear first. Plus: playback options, commentary, and an alternative “B-side” song recommendation for each artist.
Skrillex Invader 20 – by Vivien Barousse - Imagine Guitar Hero meets Space Invaders meets Skrillex meets a Piano Keyboard. Skrillex Invader 20 is a small game designed to help you improve your skills on a piano keyboard.
Scratchy Record - by Alastair Porter - Playing music from mp3s today has no soul. Scratchy Record reproduces the joy that can be had listening to music on vinyl. From the dirty needle causing extra noise, to the pops and skips that we all love, to the need to get up half way through the album and turn it over. Scratchy Record has it all.
HappyClappy - by Peck, ankit and mager – an IOS app that lets you queue up songs by clapping the rhythm. Uses The Echo Nest to search for songs by BPM.
PhatStats - Syd Lawerence – Syd tried to build a sustainable subscription business during the hack with PhatStats - A new tool to discover up and coming talent across the social web, and to monitor your videos and their engagement levels across the social web.
This is your tour - Sam Phippen - Going on tour is hard. You’ve got to find someone to tour with. You’ve got to pick cities and venues. You’ve got to book hotels, find places to eat and drink. All of this takes far too much time.
Nikantas – Sabrina Leandro – a clever app that helps you learn a new language through music lyrics. Fill in the blanks in lyrics of your favourite English, Spanish, Portuguese, French, German, or Italian artists. Can you recognise a word in a song? A word will be displayed on the screen, press the space key when the artist sings it
neoScores meets Deezer - Bob Hamblok – HTML5 sheet music score following inside Deezer.
Seevl hipster - by Alexandre Passant - Be a real hipster. Impress your friends with obscure music tastes. Do you want to impress your friend who’s into electro-folk, or that other one who only listens to avant-garde metal? Now you can! With seevl hipster, find obscure artists that match your friend tastes, and show-off on their Facebook wall.
Playlist Plus - Iain Mullan - Playlist Plus allows you to create a richer interactive version of a playlist. Add notes and comments to each track, to share with friends, or distribute an in-depth album review. Like a particular lyric? Bookmark it at the exact timestamp in the song. Think a track has a heavy Zeppelin influence? Link to the song/album it reminds you of. Cover version? Link to the original and let the reader stream it instantly from within your PlaylistPlus!
ScapeList - Mohammed Sordo - How does a landscape sound like? You take a picture of, let’s say, the Grand Canyon in Colorado, a la Instagram, but you also want to attach a song to it, a song that makes sense to you while you were taking that picture. Now imagine that other people went to the same place, took another picture of it but picked a different song. You end up with a playlist of songs related to that landscape, a ScapeList, curated by the users themselves, which you can listen to.
VideoFairy - by Suzie Blackman - A radio-style music discovery app designed for smart TV! It’s a bit like channel hopping, but for music videos. VideoFairy finds music videos from artists you’ll like with a simple interface that works with a remote control (use arrow keys and ‘enter’ on a keyboard).Designed for ‘lean back’ TV viewing with minimum interaction, you can sit back and watch new music recommended from your last.fm profile. Skip skip anything you don’t like with a simple tap of the remote.
Cannes Burn - my hack – a music visualizaton of Ellie Goulding’s Burn
I was unusually nervous and quite tired when I gave my demo, so I fell for a newbie demo mistake and had trouble getting my desktop to display properly. But when I finally did, my demo went off smoothly. I only had to say a few words and hit the play button, so despite the nerves, it was a pretty easy demo to give. Here’s my view of the audience while giving the demo:
After we presented the hacks the hackers themselves voted for the best hack which went to Yuli for his amazing DJ Spotify. Yuli is quite the gracious and humble winner, making sure everyone got a glass of his winning champagne.
After all the hacking the exhausted hackers took some time to kick back, have a good dinner, a few drinks and long conversations into the night about life as an international music hacker.
Halfway through the MIDEM Music Hack Day I paused to take stock. Here I was, on the other side of the world sitting at the top of the Palais des Festival, overlooking the French Riviera, surrounded by friends and writing code. It was a great place to be, and I felt very fortunate to be there. This was all possible because the music biz folks realize that we hackers have lots of ideas that will advance the state-of-the-art in music tech, and even more importantly we have the ability to actually turn those ideas into reality. And so, they treat us very well. It is good to be a music hacker.
My hack at the MIDEM Music Hack Day this year is what I’d call a Creative Hack. I built it, not because it answered any business use case or because it demonstrated some advanced capability of some platform or music tech ecosystem, I built it because I was feeling creative and I wanted to express my creativity in the best way that I can which is to write a computer program. The result is something I’m particularly proud of. It’s a dynamic visualization of the song Burn by Ellie Goulding. Here’s a short, low-res excerpt, but I strongly suggest that you go and watch the full version here: Cannes Burn
Unlike all of the other hacks that I’ve built, this one feels really personal to me. I wasn’t just trying to solve a technical problem. I was trying to capture the essence of the song in code, trying to tell its story and maybe even touch the viewer. The challenge wasn’t in the coding it was in the feeling.
After every hack day, I’m usually feeling a little depressed. I call it post-hacking depression. It is partially caused by being sleep deprived for 48 hours, but the biggest component is that I’ve put my all into something for 48 hours and then it is just over. The demo is done, the code is checked into github, the app is deployed online and people are visiting it (or not). The thing that just totally and completely took over my life for two days is completely gone. It is easy to reflect back on the weekend and wonder if all that time and energy was worth it.
Monday night after the MIDEM hack day was over I was in the midst of my post-hack depression sitting in a little pub called Le Crillon when a guy came up to me and said “I saw your hack. It made me feel something. Your hack moved me.”
Cannes Burn won’t be my post popular hack, nor is it my most challenging hack, but it may be my favorite hack because I was able to write some code and make somebody that I didn’t know feel something.
For my Christmas vacation programming project this year, I revisited an old hack: Six Degrees of Black Sabbath. I wrote the original, way back in 2010 at the very first San Francisco Music Hack Day. That version is still up and running, and getting regular visits, but it is getting a bit long in the tooth and so I’ve given it a total rewrite from the ground up. The result is the new Six Degrees of Black Sabbath:
Six Degrees of Black Sabbath is like the Oracle of Bacon but for music. It lets you find connections to just about any two artists based upon their collaborations. Type in the name of two artists, and 6dobs will give you a pathway showing the connections that will get you from one artist to another. For instance, if you enter ‘The Beatles’ and ‘Norah Jones’ you’ll get a path like:
- We start with The Beatles
- The Beatles had member George Harrison
- George Harrison performed with Ravi Shankar on the song Bangla Dhun and 26 others.
- Ravi Shankar was parent of Norah Jones
If you don’t like a particular connection, you can bypass it generating a new path. For instance, if we bypass Ravi Shankar, it will take us eight steps to get to Norah Jones from the Beatles:
The Beatles -> Paul McCartney -> The Fireman -> Youth -> Pigface
-> Mike Dillon ->Garage A Trois -> Charlie Hunter -> Norah Jones
Not all connections are created equal. Mick Jagger and Keith Richards have been playing together for over 50 years in the Rolling Stones. That’s a much stronger connection than the one between Mick Jagger and Fergie for performing a single song together at the Rock and Roll Hall of Fame. We take these connection strengths into account when finding paths between artists. Preference is given to stronger connections, even if those stronger connections will yield a longer path.
The new version of Six Degrees of Black Sabbath has a number of new features:
Video - Each step in a path is represented by a Youtube video – often with a video by the two artists that represent that step. I’m quite pleased at how well the video works for establishing the connection between two artists. Youtube seems to have it all.
Live stats - The app tracks and reports all sorts of things such as the longest path discovered so far, the most frequently occurring artists on paths, the most connected artists, most searched for artists and so on.
Larger database of connections - the database has about a quarter million artists and 2.5 million artist-to-artist connections.
Autocomplete for artist names - no need to try to remember how to spell ‘Britney Spears‘ – just start typing the parts you know and it will sort it out.
Spiffier looking UI - It still looks like it was designed by an engineer, but at least it looks like it was designed in this decade by an engineer.
Path finding improvements - faster and better paths throughout.
Revisiting this app after 4 years was a lot of fun. I got to dive deep into a bunch of tech that was new to me including Redis, Bootstrap 3, and the YouTube video search API. I spent many hours untangling the various connections in the new Musicbrainz schema. I took a tour through a number of Pythonic network graph libraries (Networkx, igraph and graph-tool), I learned a lot about Python garbage collection when you have a 2.5gb heap.
Give the app a try and let me know what you think.
This weekend, I’ve been in London, attending the London Music Hack Day. For this weekend’s hack, I was inspired by daughter’s music listening behavior – when she listens to music, she is good for the first verse or two and the chorus, but after that, she’s on to the next song. She probably has never heard a bridge. So for my daughter, and folks like her with short attention spans, I’ve built Attention Deficit Radio. ADR creates a Pandora-like radio station based upon a seed artist, but doesn’t bother you by playing whole songs. Instead, after about 30 seconds or so, it is on to the next song. The nifty bit is that ADR will try to beat-match and crossfade between the songs giving you a (hopefully) seamless listening experience as you fly through the playlist. Of course those with short attention spans need something to look at while listening, so ADR has lots of gauges that show the radio status – it shows the current beat, the status of the cross-fade, tempo and song loading status.
There may be a few rough edges, and the paint is not yet dry, but give Attention Deficit Radio a try if you have a short listening attention span.
Our playlists our filled with One Hit Wonders like My Sharona, Tainted Love and Final Countdown. One Hit wonders are the non-nutritious food of the music world – they are Twinkie’s, the Ho Hos and the Yodels of our musical diet. But what should we listen to when we want a full and nutritious musical meal? We should look for music by artists that have deeper catalogs – artists where the fans spend substantial time listening to the non-hits. These are the Deep Artists, the opposite of the One Hit Wonders – the artists that you can spend months or years listening to and exploring their collection.
Unfortunately, there’s no master list of Deep Artists – but I have lots of music listener data, so I figured I could build one. Here’s what I did. First I restricted my results to somewhat familiar artists with at least 100 songs in their catalog. I then scored each artist by the percentage of song plays that occur in the deep catalog versus the total plays for the artist – where deep catalog means a song that is not in the top ten for that artist. This gives each artist a Deepness Score that I could then use to sort artists to give us a list of the Deepest Artists. Here are the top ten:
Not surprising to see Johann Sebastian Bach at number two. Bach has no real ‘hits’ – and indeed has an incredibly deep catalog. 90% of all Bach plays occur in Bach’s non-top 10. The number one deep artist is Vitamin String Quartet – they have 3500 covers of songs with no clear hits among them.
Looking at the full list we see jam bands like Phish and Grateful Dead, AOR staples like Pink Floyd and David Bowie.
I’ve built a list of a little over 500 of the Deepest Artists. These are artists that have a deepness score of 50% or greater – meaning that at least 50% of all listens for the artist is in the deeper cuts. This Thanksgiving if you are looking for some more nutritious music, stay away from Alice’s Restaurant and other One Hit Wonders and listen to music by artists on this Deep Artists list.
Update: Glenn looked at these results and felt that a nutritious music meal shouldn’t include Vitamin String Quartet (it’s the ‘artificially-fortified sugar-coated cereal of music’ according to Glenn), so Glenn took a different approach with different results. Glenn calls his results boring, but I think they are quite interesting. Read his post: Good Boring results
I’ve been in Helsinki this weekend (which is not in Sweden btw) for the Helsinki Music Hack Day. I wanted to try my hand at a DJ app that will allow you to dynamically and interactively mix two songs. I started with Girl Talk in a Box, ripped out the innards and made a whole bunch of neat changes:
- You can load more than one song at a time. Each song will appear as its own block of music tiles.
- You can seamlessly play tiles from either song.
- You can setup branch points to let you jump from an point in one song to any point in another (or the same) song.
- And the killer feature – you can have two active play heads allowing you to dynamically interact with two separate audio streams. The two play heads are always beat matched (the first play head is the master that sets the tempo for everyone else). You can cross-fade between the two audio streams – letting you move different parts of the song into the foreground and the background.
All the regular features of Girl Talk in a Box are retained – bookmarks, arrow key control, w/a/s/d navigation and so on. See the help for more details on the controls.
You can try the app here: Swap the Drop
A Music Hack Day is unlike most other hackathons. There are no mega-prizes for the best hacks. There are no VCs wandering the hacker hallways trolling for the next startup. There are no briefs that describe the types of apps that you should build. Hackers don’t go to a Music Hack Day to win big prizes, or to launch their startup. Hackers go to Music Hack Days because they love music and they love to build stuff. At a Music Hack Day these passionate builders get to apply their talents to music, surrounded by like-minded peers and build their version of the future of music. The currency at a Music Hack Day is not money or VC attention, the currency is creativity. The Music Hack Day prize is knowing that you’ve built something cool enough to delight other music hackers.
So what happens at a Music Hack Day? How does it all work? What kind of hacks do people build? Read on to see exactly what happened at the Boston Music Hack Day 2013, held this last weekend.
Boston Music Hack Day
This weekend, hundreds of folks who are passionate about music and technology got together in Cambridge MA for Boston Music Hack Day 2013. The event was hosted at the Microsoft NERD - a wonderful facility that Microsoft makes available for all sorts of programmer events. Registration started at 9AM and by 10AM hackers were breakfasted and ready to go.
The event started off with some opening remarks by your truly, describing how a Music Hack Day works and how to have a successful event (meet other people, learn new stuff, build something, make sure you finish it, demo it and have fun).
Short technology presentations
Next up, organizations that had some sort of music technology such as an API or new gizmo that might be interesting to music hackers spent a few minutes talking about their technology. For many hackers, this was their first exposure to the music ecosystem – they don’t know what APIs are available for building apps so learning about music streaming APIs from companies like SoundCloud, Rdio and Spotify, and learning about all the music data available from APIs like The Echo Nest and the Free Music Archive is really important.
There were a few interesting devices available for hackers at the event. Techogym brought a high tech treadmill with its own API hoping that music hackers would build music-related exercise apps. Muzik brought a set of headphones that are instrumented with accelerometers and other sensors allowing for apps to adapt to the actions of the listener.
Sometimes hackers come to a Music Hack Day with ideas ready to go. Sometimes hackers come with their skills but no ideas. At the Project Pitch session, hackers had a minute to pitch their idea or to offer their skills. About 20 hackers braved the front of the room describing their idea or their skill set. One hacker described his project as help me with my homework (and yes, this hacker did find a teammate and they ultimately built a nifty hardware hack that satisfied the homework requirement too).
Tech Deep Dives
Next up on the schedule were the Tech Deep dives. Organizations had a half-hour to give a deeper view of what their technology is capable of. Some hackers want to know more about how to do particular things with an API or technology. This is their opportunity to find out all about the nuts and bolts, to ask questions from the experts. The Tech Deep Dives are strictly optional – many hackers skip them and instead start forming teams, sharing ideas, initializing their repos and writing code.
After all the preliminaries are over it was finally time to start hacking.
Hackers formed teams, large and small.
The competition for the best vest was fierce:
They staked out a comfortable workspace in chairs …
Or on the floor …
There were some very creative hardware hacks:
Plenty of good food
And lots of fun
The Microsoft NERD was only available until 9PM – after that we moved over to hack/reduce – a wonderful hacking space a five minute walk away. There we were greeted by a perfect hacking space with lots of great wifi, great hacker lighting, and lots of beer. Hacking continued all night. Some hackers did try to get some sleep (either at the hack, or back at home), but some hardcore hackers stayed the whole night.
By 9AM on Sunday morning, the hackers were back at the NERD, for lots of coffee, some breakfast and then more hacking.
At 2:30, hacking was officially over, and teams submitted their projects to Hacker League. Sixty hacks were submitted.
By 3PM the 200 hackers had all gathered back into the big room joined by a hundred folks who had come just to see the demo session. Hackers had two minutes to show their stuff. It is a hard demo to give. You are giving a demo of software that you’ve just finished building. It might have some bugs. The WiFi is a little flaky, you haven’t slept in 24 hours, your hands are shaking from too much coffee and too much nervousness, you have to type while holding a microphone and your laptop just won’t sync with the projector just right, and the audio isn’t coming out of the speakers, and the colors look all wrong on the screen. All in front of 300 people. I’ve done it dozens of times and it still is a really scary demo to give. But it is incredibly exhilarating too – to take nothing but an idea and turn it into something that can amaze or amuse a room full of tech elite in 24 hours. It is quite a rush.
There were two A/V setups so while one team was presenting, the next team was setting up. This allowed us to get through 60 demos in just over two hours. There was a very low incidence of demo fail. And only two inappropriate demos (one was a 2 minute powerpoint presentation with no tech built, the other was a 2 minute tech commercial for a product). I was worried that we might have a #titstare moment with one hack that seemed to contain questionable content but that hacker apparently decided not to present.
The 60 hacks represented a wide range of domains. There were games, music learning tools, programs designed to create, manipulate, remix and even destroy music. I’d love to cover them all, but there are just too many.
The full list is on hacker league. Here are some of my absolute favorites:
String Theory - A musical instrument and sound sculpture build from yearn and stretch sensors and powered by an Arduino.
The Lone Arranger - a terminal app that allows you to easily rearrange your audio. By a father and son hacking team.
The Secret History of Music - combs biographies, lyrics, and commentary from song meanings from two artists, combines them into one fictional artist, and uses Markov chain magic to generate a 50K novel about this new fictional band.
LED Soundsystem - this hack attempts to generate a light show synchronized to the music. It has a special place in my heart because the hacking team was working on the same problem I was working on for my abandoned hack – i.e. automatically finding the ‘drop’ in a song. Unfortunately, this team had a demo fail – but they are smart guys and I expect to see good stuff from them at the next hack day.
eHarmonica – an electronic harmonica!
Enter the dragon - In today’s world, everyone deserves a spectacular entrance. And we intend to give it to them. Enter the Dragon uses bluetooth technology to detect when a user enters the room and plays their personalized entrance music.
Dadabots - Dadabots are bot accounts on creative websites that make procedural creations or remixes of other creations
ios SoundPuzzle - A simple iOS ear training game built programmatically from the free music archive and the echonest remix api.
danceomatic – totally awesome automatic choreography from an mp3 and a web based stick figure performance.
Jotunnslayer – Never again listen to power metal without slaying ice giants. Die in battle. Earn your place in Valhalla.
Give Me Liberty or Give Me Death Metal - Political representation via musical exploration
Echos – Choose your favorite song and shoot to the beat! Fight enemies that respond to and are controlled by the music! Listen closely and experience unparalleled power as you get into the groove! Enjoy addictive arcade-style game-play in this twist on a classic formula.
TweetTones - a native iOS application that generates synthesized music from tweets in real-time.
Short-Attention Span Playlist Scanner - Glenn made a radio scanner that find and plays just the choruses.
ionian Eclipse - A web-based multiplayer top-down space shooter with procedurally generated enemies and interactions driven by music events.
ColorMe - Ever wondered what your music looked like? Now you can look at songs by your favorite artist with this super fun web app, powered by the Echo Nest.
How Repetitive - measures how often audio segments repeat themselves within in a given song.
Jason’s music visualizer - an html/css visualizer on steroids.
And last, but by no means least, Jonathan’s awesome MIDI Digester that converts audio to MIDI and back, over and over to generate some very strange sounds. The very essence of the music.
There are so many excellent hacks, I’m sure I’ve missed many notables. Luckily, Evolver.fm covered the event, so expect to see Eliot’s writups on all the best hacks on Evolver.fm.
At the end of the mega demo session, there’s a brief prize awarding ceremony where a half-dozen organizations give out modest prizes for hackers that made cool stuff using their tech.
Finally we adjourned to the local pub for some food, beer and hacking recaps.
Special thanks to the organizers of the event. The Music Hack Day would not happen without Elissa and Matt. They do all the hard work. Finding the venue, wrangling the sponsors and volunteers, making a mega Costco food run, dealing with the A/V, running the registration, selecting and hiring the caterers, designing t-shirts and so much more. There’s a huge amount of work that goes into planning the event, much more than meets the eye. Elissa and Matt are the unsung heroes of Music Hack Day. We should make a music hack to sing their song.
Thanks also to the event sponsors: Rdio, Spotify, Microsoft, hack/reduce, Free Music Archive, SoundCloud, Mailchimp and The Echo Nest, and the many volunteers who came and helped us run the whole show.
More Music Hack Days
Interested in going to a Music Hack Day? Check out the Music Hack Day calendar for upcoming events. There’s one in Helsinki this weekend, and there’s one in London in just a few weeks. More events are rumored to be in the planning stages for 2014.
(Photos mostly by Michelle Ackerman, a few by me)
For my Boston Music Hack Day hack I built Yet Another Party Playlisting App (YAPPA), because the world needed another party playlister – but really, I built it because I needed another hack, because 15 hours into the 24 hour hackathon I realized that my first hack just wasn’t going to work (more on that in another post). And so, with 9 hours left in the hack day, I thought I would try my hand at the party playlisting app.
The YAPPA is a frequently built app. In some sense one can look at the act of building a YAPPA as a hacking exercise. Just as a still life painter will practice by painting a bowl of fruit, or a pianist will practice scales, a music hacker can build their hacking muscle by creating a YAPPA.
The essential features of a YAPPA are straightforward – create a listening experience for a party based upon the tastes of the guests. Allow guests to suggest music for the party, apply some rules to select music that satisfies all the guests, and keep the music flowing.
With those features in mind, I created my party playlisting app. The interface is dead simple – guests can add music to the party via the master web interface or text the artist and song from the mobile phones to the party phone number. Once the party has started, PAPPA will keep the music flowing.
The key technology of PAPPA is how it picks the music to play next. Most YAPPAs will try to schedule music based on fairness so that everyone’s music taste is considered. Some YAPPAs also use song attributes such as song hotttnesss, song energy and danceability to make sure that the music matches the vibe of the party. PAPPA takes a very different approach to scheduling music. That’s because PAPPA takes a very different approach to parties. PAPPA doesn’t like parties. PAPPA wants everyone to go home. So PAPPA takes all of these songs that have been carefully texted to the party phone number, along with all the artist and song suggestions submitted via the web and throws them away. It doesn’t care about the music taste of the guests at the party. In fact it despises their taste (and the guests as well). Instead, PAPPA selects and plays the absolute worst music it can find. It gives the listener an endless string of the most horrible (but popular) music. Here’s a sample (the first 3 songs are bait to lure in the unwitting party guests):
- Royals by Lorde
- Levels by Avicii
- Blurred Lines by Robin Thicke
- #Twerkit featuring Nicki Minaj by Busta Rhymes
- From The Bottom Of My Broken Heart by Britney Spears
- Amigas Cheetahs by The Cheetah Girls
- Do Ya Think I’m Sexy by Paris Hilton
- Incredible by Clique Girlz
- No Ordinary Love by Jennifer Love Hewitt
- Mexican Wrestler by Emma Roberts
- I Don’t Think About It by Emily Osment
- A La Nanita Nana by The Cheetah Girls
- Don”t Let Me Be The Last To Know by Britney Spears
- Wild featuring Big Sean by Jessie J
- Heartbeat (Album Version) by Paris Hilton
- Love The Way You Love Me by The Pussycat Dolls
- When You Told Me You Loved Me by Jessica Simpson
- Jericho by Hilary Duff
- Strip by Brooke Hogan
- Pero Me Acuerdo De Tí by Christina Aguilera
- Bang Bang by Joachim Garraud
- Right Now featuring David Guetta (Sick Individuals Dub) by Rihanna
- Wilde Piraten by The Cool Kids
- Friend Lover by Electrik Red
- Betcha Can’t Do It Like Me by D4L
- Who’s That Girl by Hilary Duff
- Get In There, Frank! by Fun
- Hold It Don”t Drop It by Jennifer Lopez
- Sweet Sixteen by Hilary Duff
- Live It Up featuring Pitbull by Jennifer Lopez
- Freckles by Natasha Bedingfield
- I Want You by Paris Hilton
- Hold It Close by Fun
- Magic by The Pussycat Dolls
- How To Lose A Girl by Mitchel Musso
- Fairy Tales by JoJo
- Slow It Down featuring Fabolous (Album Version (Explicit)) by The-Dream
- Mr. Hamudah by Charles Hamilton
- Promise by Vanessa Hudgens
- Metamorphosis by Hilary Duff
How does PAPPA find the worst music in the world? It looks through all the data that The Echo Nest is collecting about how people experience music online to find the songs that have been banned frequently. When a music listener says “ban this song” they are making a pretty strong statement about the song – essentially saying, “I do not ever want to hear that song again in my life”. PAPPA finds these songs that have the highest banned-to-play ratio (i.e. the songs that have been proportionally banned the most when play count is taken into consideration) and adds them to the playlist. The result being a playlist filled with the most reviled music – with songs by Paris Hilton, Jennifer Love Hewitt and the great Emma Roberts. The perfect playlist to send your guests home.
At this moment, lets pause and listen to the song Mexican Wrestler by Emma Roberts:
What happens to all those carefully crafted text messages of songs sent by the guests? No, there’s no Twilio app catching all those messages, parsing out songs and adding them to a play queue to be scheduled. They just go to my phone. That’s so if people are not leaving the party fast enough, I can use all the phone numbers of the guests to start to text them back and tell them they should go home.
By the way, if you look at the songs that were texted to me during my two minute demo you’d realize how fruitless a YAPPA really is. There’s no possible way to make a party playlist that is going to satisfy everyone in the room. Tastes are too varied, and there’s always that guy who thinks he is clever by adding some Rick Astly to the party queue. Here’s what was texted to me during my two minute demo:
- Gregory Porter – be good
- Rebecca Black – It’s Friday
- Weird Al Yankovic – Fat
- Lady Gaga – Applause
- Weird Al Yankovic – Amish Paradise (from a different phone number from the other weird Al fan)
- boss ass bitch
- Basement Jaxx raindrops
- John Mayer your body is a wonderland
- jay z holy grail
- Underworld spikee
- wake me up
- Britney Spears – Hit Me Baby One More Time
- Slayer War Ensemble
- Bieber baby
- Ra Ra riot
- Rick Astley
- Mikey Cyrus
- Hi paul
- Stevie wonder overjoyed
Imagine trying to build a party playlist based upon those 24 input songs. Admittedly, a hackathon demo session is not a real test case for a party playlister but I still think you’d end up with a terrible mix of songs that no smart algorithm, nor any smart human, could stitch together into a playlist that would be appropriate and pleasing for a party. My guess is that if you did an A/B test for two parties, where one party played music based upon suggestions texted to a YAPPA and the other party played the top hotttest songs, the YAPPA party would always lose. I’d run this test, but that would mean I’d have to go to two parties. I hate parties, so this test will never happen. Its one of the flaws in our scientific method.
Who are the worst artists?
Looking at the PAPPA playlists I see a number of recurring artists – Britney Spears and Paris Hilton seem to be well represented. I thought it would be interesting to create a histogram of the top recurring artists in the most banned songs list. Here’s the fascinating result:
One thing I find notable about this list is the predominance of female artists. Females outnumber males by a substantial amount. Here’s some pie:
80% of the most banned artists are female. A stunning result. There’s something going on here. Someone suggested that the act of banning a song is an aggressive act that may skew male, and many of these aggressively banning males don’t like to listen to female artists. More study is needed here. It may involve parties, so I’m out.
Wrapping it all up
I enjoyed creating my PAPPA YAPPA. Demoing it was really fun and the audience seemed to enjoy the twist ending. The patterns in the data underlying the app are pretty interesting too. Why are so many banned songs by female artists?
If you are having your own party and want to use PAPPA to help enhance the party you can go to:
Just replace the phone number in the URL with your own and you are good to go.