Archive for category data
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 previous posts, we’ve seen that different regions of the country can have different listening preferences. So far we’ve looked at the distinctive artists in any particular region. Perhaps equally interesting is to look at artists that get much fewer listens in a particular region than you would expect. These are the regional anti-preferences, the artists that are generally popular across the United States, but get much less love in a particular part of the country.
To find these artists, we merely look for artists that drop the furthest in rank on the top-most-played chart for a region when compared to the whole U.S. For example, we can look at the top 50 artists in the United States, and find those artists of the 50 that drop furthest in rank on the New Hampshire chart. Try it yourself. Here are the results:
Artists listened to more in United States than they are in New Hampshire
R. Kelly is ranked the 42nd most popular artist in the U.S., but in New Hampshire he’s the 720th most popular, a drop of 678 positions on the chart making him the most ignored artist in New Hampshire.
We can do this for each of the states in the United States and of course we can put them on a map. Here’s a map that shows the most ignored artist of the U.S. top-50 artist in each state.
What can we do with this information? If we know where a music listener lives, but we know nothing else about them, we can potentially improve their listening experience by giving them music based upon their local charts instead of the global or national charts. We can also improve the listening even if we don’t know where the listener is from. As we can see from the map, certain artists are polarizing artists, liked in some circles and disliked in others. If we eliminate the polarizing artists for a listener that we know nothing about, we can reduce the risk of musically offending the listener. Of course, once we know a little bit about the music taste of a listener we can greatly improve their recommendations beyond what we can do based solely on demographic info such as the listener location.
Future work There are a few more experiments that I’d like to try with regard to exploring regional preferences. In particular I think it’d be fun to generate an artist similarity metric based solely on regional listening behaviors. In this world, Juicy J, the southern rapper, and Hillsong United, the worship band would be very similar since they both get lots of listens from people in Memphis. A few readers have suggested alternate scoring algorithms to try, and of course it would be interesting to repeat these experiments for other parts of the world. So much music data, so little time! However, this may be the last map I make for a while since the Internet must be getting sick of ‘artists on a map’ by now.
The data for the map 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.
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
Earlier this week we looked at how gender can affect music listening preferences. In this post, we continue the tour through demographic data and explore how the age of the listener tells us something about their music taste.
Where does the age data come from? As part of the enrollment process for most music services, the user is asked for a few pieces of demographic data, including gender and year-of-birth. As an example, here’s a typical user-enrollment screen from a popular music subscription service:
Is this age data any good? The first thing we need to do before we get too far with analyzing the age data is to get a feel for how accurate it is. If new users are entering random values for their date of birth then we won’t be able to use the listener’s age for anything useful. For this study, I looked at the age data submitted by several million listeners. This histogram shows the relative number of users by year of birth.
The first thing I notice is the curve has the shape one would expect. The number of listeners in each age bucket increases as the listener gets younger until around age 21 or so, at which points it drops off rapidly. The shape of the curve aligns with the data from this study by EMI in 2011 that shows the penetration of music streaming service by age demographic. This is a good indicator that our age data is an accurate representation of reality.
However, there are a few anomalies in the age data. There are unexpected peaks at each decade – likely due to people rounding their birth year to the nearest decade. A very small percentage (0.01 %) indicate that they are over 120 years old, which is quite unlikely. Despite this noise, the age data looks to be a valid and fairly accurate representation, in the aggregate, of the age of listeners. We should be able to use this data to understand how age impacts listening.
Does a 64-year-old listen to different music than a 13-year-old?
One would expect that people of different ages would have different music tastes. Let’s see if we can confirm this with our data. For starters, lets compare the average listening habits of 64-year-old listeners to that of the aggregate listening habits of the 13-year-old listener. For this experiment I selected 5,000 listeners in each age category, and aggregated their normalized artist plays to find the most-frequently-played artists. As expected, you can see that 64-year-old listeners have different tastes than 13-year-old listeners.
The top artists for the average 64-year-old listener include a mix of currently popular artists along with a number of artists from years gone by. While the top artists for the average 13-year-old includes only the most current artists. Still, there are seven artists (shown in bold) that overlap in the top 20 – an overlap rate of about 35%. This 35% overlap is consistent across all ranges of top artists for the two groups. No matter if we look at the top 100 or the top 1000 artists – there’s about a 35% overlap between the listening of 13- and 64-year-olds.
I suspect that 35% overlap is actually an overstatement of the real overlap between 13- and 64-year-olds. There are a few potential confounding effects:
- There’s a built-in popularity bias in music services. If you go to any popular music service you will see that they all feature a number of playlists filled with popular music. Playlists like The Billboard Top 100, The Viral 50, The Top Tracks, Popular New Releases etc. populate the home page or starting screen for most music services. This popularity bias inflates the apparent interest in popular music so, for instance, it may look like a 64-year-old is more interested in popular music than they really are because they are curious about what’s on all of those featured playlists.
- The age data isn’t perfect – for instance, there are certainly a number of people that we think are 64-years-old but are not. This will skew the results to artists that are more generally popular. We don’t really know how big this affect is, but it is certainly non-zero.
- People share listening accounts – this is perhaps the biggest confounding factor – that 64-year-old listener may be listening to music with their kids, their grand-kids, their neighbors and friends which means that not all of those plays should count as plays by a 64-year-old. Again, we don’t know how big this effect is, but it is certainly non-zero.
Let’s pause and have a listen to some music. First the favorite music of a typical 64-year-old listener:
And now the favorite music of a typical 13-year-old listener:
Finding the most distinctive artists
Perhaps more interesting than looking at how the two ages overlap in listening, is to look at how they differ – what are the artists that a 64-year-old will listen to that are rarely, if ever, listened to by a 13-year-old and vice versa. These are the most distinctive artists.
We can find the distinctive artists by identifying the artists in the top 100 of one group that fall the furthest in ranking in the other group. For example Skrillex is the 40th most listened to artist for the typical 13-year-old listener, but for 64-year-old listeners, Skrillex falls all the way to the 3,937 most listened to artist, making Skrillex one of the most distinguishing artist between the two groups of listeners. Likewise, Roy Orbison is the 42nd most listened to artist among 64-year-olds. He drops to position 4,673 among 13-year-olds making him one of the distinguishing artists that separate the 64-year-old from the 13-year-old.
We can use this technique to create playlists of artists that separate the 13-year-old from the 64-year-olds. Are you a 13-year-old, having a party and really wish that grandma would go to another room? Try this playlist:
Are you a 64-year-old and you want all of those 13 year-olds at the party to go home? Try this playlist:
We can also use this data to bring these two groups together. We can find the music that is liked the most among the two groups. We can do this by ordering artists by their worst ranking among the two groups. Artists like Skrillex and Roy Orbison fall to the bottom of the list since each is poorly ranked by one of the groups, while artists like Katy Perry and Bruno Mars rise to the top because they are favored by both groups.
Again, the confounding factors mentioned previously will bias the shared lists to more popular music. Nevertheless, if you are trying to make a playlist of music that will please both a 64-year-old and a 13-year-old, and you know nothing else about their music taste, this is probably your best bet.
Artists that are favored by both 64-year-old and 13-year-old listeners are: Bruno Mars, Taylor Swift, P!nk, Rihanna, Justin Timberlake, Katy Perry, Robin Thicke, Maroon 5, Lana Del Rey, Daft Punk, Beyoncé, Drake, Luke Bryan, Adele, Macklemore & Ryan Lewis, Miley Cyrus, David Guetta, Lorde, Jay-Z, Usher
We can sum up the differences between the two groups in this graphic:
Broadening our view
We’ve shown that, as expected, 13-year-olds and 64-year-olds have different listening preferences. We can apply the same techniques across the range of age demographics typically used by marketers. We can find the most distinctive artists for each demographic bucket. It is interesting to see the progression of music taste over time. For instance, it is clear that something happens to a music listener between the 25 to 34 and 35 to 44 age buckets. The typical listener goes from hipster (Lumineers, Vampire Weekend, The National), to old (Pearl Jam, U2, Bon Jovi).
It is interesting to look at the starting year for artists in each of these buckets to get a sense of how the artist’s own age relates to the age of their fans:
My take-way from this is that no matter how old you are, you don’t like the music from the 70s and the 80s so much.
Most homogenous Artists
We can also find the artists that are most acceptable across all demographics. These are the artists that are liked by more listeners in all of the groups. Like in the 13/64-year-old example, we can find these artists by ordering them by their worst ranking among all the demographic groups.
Most homogeneous artists: Bruno Mars, Rihanna, Katy Perry, Lana Del Rey, Beyoncé, P!nk, Jay-Z, Macklemore & Ryan Lewis, Daft Punk, Maroon 5, Justin Timberlake, Robin Thicke, David Guetta, Luke Bryan, Taylor Swift, Drake, Adele, Imagine Dragons, Miley Cyrus, Lorde
This is essentially the list of the most popular artists but with the most polarizing artists from any one demographic removed. If you don’t know the age of your listener, and you want to give the listener a low risk listening experience, these artists are a good place to start. And yes … this results in a somewhat bland, non-adventurous listening session – that’s the point. But as soon as you know a bit about the true listening preference of a new listener, you can pivot away from the bland and give them something much more in line with their music taste.
Rounding out the stats
There are a few more interesting bits of data we an pull out related to the age of the listener
Average number of artists in listening rotation
The typical 25- to 34-year old listener has more artists in active rotation than any other age group, while the 65+ listeners have the least.
Relative number of plays per user by age group
Likewise, the typical 25- to 34-year-old listener plays more music than any other category.
Tying it all up …
This quick tour through the ages confirms our thinking that the age of a listener plays a significant role in the type of music that they listen to. We can use this information to find music that is distinctive for a particular demographic. We can also use this information to help find artists that may be acceptable to a wide range of listeners. But we should be careful to consider how popularity bias may affect our view of the world. And perhaps most important of all, people don’t like music from the 70s or 80s so much.
One of the challenges faced by a music streaming service is to figure out what music to play for the brand-new listener. The first listening experience of a new listener can be critical to gaining that listener as a long time subscriber. However, figuring out what to play for that new listener is very difficult because often there’s absolutely no data available about what kind of music that listener likes. Some music services will interview the new listener to get an idea of their music tastes.
However, we’ve seen that for many listeners, especially the casual and indifferent listeners, this type of enrollment may be too complicated. Some listeners don’t know or care about the differences between Blues, R&B and Americana and thus won’t be able to tell you which they prefer. A listener whose only experience in starting a listening session is to turn on the radio may not be ready for a multi-screen interview about their music taste.
So what can a music service play for a listener when they have absolutely no data about that listener? A good place to start is to play music by the most popular artists. Given no other data, playing what’s popular is better than nothing. But perhaps we can do better than that. The key is in looking at the little bit of data that a new listener will give you.
For most music services, there’s a short user enrollment process that gets some basic info from the listener including their email address and some basic demographic information. Here’s the enrollment box for Spotify:
Included in this information is the date of birth and the gender of the listener. Perhaps we can use basic demographic data to generate a slightly more refined set of artists. For starters, lets consider gender. Let’s try to answer the question: If we know that a listener is male or female does that increase our understanding of what kind of music they might like? Let’s take a look.
Exploring Gender Differences in Listening
Do men listen to different music than women do? Anecdotally, we can think of lots of examples that point to yes – it seems like more of One Direction’s fans are female, while more heavy metal fans are male, but lets take a look at some data to see if this is really the case.
The Data – For this study, I looked at the recent listening of about 200 thousand randomly selected listeners that have self-identified as either male or female. From this set of listeners, I tallied up the number of male and female listeners for each artist and then simply ranked the artists in order or listeners. Here’s a quick look at the top 5 artists by gender.
Top 5 artists by gender
|2||Bruno Mars||Daft Punk||Bruno Mars|
|4||Katy Perry||Bruno Mars||Katy Perry|
Among the top 5 we see that the Male and Female listeners only share one artist in common:Bruno Mars. This trend continues as we look at the top 40 artists. Comparing lists by eye can be a bit difficult, so I created a slopegraph visualization to make it easier to compare. Click on this image to see the whole slopegraph:
Looking at the top 40 charts artists we see that more than a quarter of the artists are gender specific. Artists that top the female listener chart but are missing on the male listener chart include: Justin Bieber, Demi Lovato, Shakira, Britney Spears, One Direction, Christina Aguilera, Ke$ha, Ciara, Jennifer Lopez, Avril Lavigne and Nicki Minaj. Conversely, artists that top the male listener chart but are missing on the top 40 female listener chart include: Bob Marley, Kendrick Lamar, Wiz Khalifa, Avicii, T.I. Queen, J.Cole, Linkin Park, Kid Cudi and 50 Cent. While some artists seem to more easily cross gender lines like Rihanna, Justin Timberlake, Lana Del Rey and Robin Thicke.
No matter what size chart we look at – whether it is the top 40, top 200 or the top 1000 artists – about 30% of artists on a gender-specific chart don’t appear on the corresponding chart for the opposite gender. Similarly, about 15% of the artists that appear on a general chart of top artists will be of low relevance to a typical listener based on these gender-listening differences.
What does this all mean? If you don’t know anything about a listener except for their gender, you can reduce the listener WTFs by 15% for a typical listener by restricting plays to artists from the gender specific charts. But perhaps even more importantly, we can use this data to improve the listening experience for a listener even if we don’t know a listener’s gender at all. Looking at the data we see that there are a number of gender-polarizing artists on any chart. These are artists that are extremely popular for one gender, but not popular at all for the other. Chances are that if you play one of these polarizing artists for a listener that you know absolutely nothing about, 50% of the time you will get it wrong. Play One Direction and 50% of the time the listener won’t like it, just because 50% of the time the listener is male. This means that we can improve the listening experience for a listener, even if we don’t know their gender by eliminating the gender skewing artists and replacing them with more gender neutral artists.
Let’s see how this would affect our charts. Here are the new Top 40 artists when we account for gender differences.
|16||23||Lana Del Rey|
|19||27||The Black Eyed Peas|
|24||22||Macklemore & Ryan Lewis|
Artists promoted to the chart due to replace gender-skewed artists are in bold. Artists that were dropped from the top 40 are:
- Avicii – skews male
- Justin Bieber – skews female
- Christina Aguilera – skews female
- One Direction – skews female
- Demi Lovato – skews female
Who are the most gender skewed artists?
The Top 40 is a fairly narrow slice of music. It is much more interesting to look at how listening can skew across a much broader range of music. Here I look at the top 1,000 artists listened to by males and the top 1,000 artists listened to by females and find the artists that have the largest change in rank as they move from the male chart to the female chart. Artists that lose the most rank are artists that skew male the most, while artists that gain the most rank skew female.
Top male-skewed artists:
artists that skew towards male fans
- Iron Maiden
- Rage Against the Machine
- Van Halen
- Jimi Hendrix
- Limp Bizkit
- Wu-Tang Clan
- The Who
- Alice in Chains
- Black Sabbath
- Stone Temple Pilots
- Mobb Deep
- Queens of the Stone Age
- Ice Cube
Top female-skewed artists:
artists that skew towards female fans
- Danity Kane
- Cody Simpson
- Hannah Montana
- Emily Osment
- Playa LImbo
- Vanessa Hudgens
- Miranda Lambert
- Aly & AJ
- Christina Milian
- Noel Schajris
- Maria José
- Jesse McCartney
- Bridgit Mendler
- Luis Fonsi
- La Oreja de Van Gogh
- Michelle Williams
- Lindsay Lohan
By looking at the genres of the most gender skewed artists we can also get a sense of which genres are most gender skewed as well. Looking at the genres of the top 1000 artists listened to by male listeners and the top 1000 artists with female listeners we identify the most skewed genres:
Genres most skewed to female listeners:
- Dance Pop
- Contemporary Hit Radio
- Urban Contemporary
- Hot Adult Contemporary
- Latin Pop
- Teen Pop
- Neo soul
- Pop rock
- Contemporary country
Genres most skewed to male listeners:
- Hip Hop
- Album Rock
- Pop Rap
- Indie Rock
- Funk Rock
- Gangster Rap
- Electro house
- Classic rock
- Nu metal
This study confirms what we expected – that there are differences in gender listening. For mainstream listening about 30% of the artists in a typical male’s listening rotation won’t be found in a typical female listening rotation and vice versa. If we happen to know a listener’s gender and nothing else, we can improve their listening experience somewhat by replacing artists that skew to the opposite gender with more neutral artists. We can even improve the listening experience for a listener that we know absolutely nothing about – not even their gender – by replacing gender-polarized artists with artists that are more accepted by both genders.
Of course when we talk about gender differences in listening, we are talking about probabilities and statistics averaged over a large number of people. Yes, the typical One Direction fan is female, but that doesn’t mean that all One Direction fans are female. We can use gender to help us improve the listening experience for a brand new user, even if we don’t know the gender of that new user. But I suspect the benefits of using gender for music scheduling is limited to helping with the cold start problem. After a new user has listened to a dozen or so songs, we’ll have a much richer picture of the type of music they listen to – and we may discover that the new male listener really does like to listen to One Direction and Justin Bieber and that new female listener is a big classic rock fan that especially likes Jimi Hendrix.
update – 2/13 – commenter AW suggested that the word ‘bias’ was too loaded a term. I agree and have changed the post replacing ‘bias’ with ‘difference’
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.
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.
Yesterday, I wrote about who the Deepest Artists are. So naturally, today I’ll turn that on its head and take a look at who are the Shallowest Artists. I define a shallow artist as an artist that despite having a substantial number of released songs, has most listens concentrated in their top five tracks. These are the artists that are best known for just a small number of songs.
For each artist, I’ve calculated a Shallowness Score which is merely the percentage of an artist’s plays that occurs in an artist’s top 5 songs. A Shallowness Score of 71% means that 71% of all listens occur in the top 5 songs. Thus, 71% of all listens to Survivor (of Eye of the Tiger fame) are found in their top 5 songs.
Update: This post used to reference the Pitch Perfect Treblemakers, but Glenn points to an ambiguous artist issue with the Treblemakers where multiple artists were conflated. The Pitch Pefect Treblemakers only have 4 songs so they are no longer a candidate for this list.
Here are the top 15 Shallowest Artists. Click to see the full chart:
As you’d expect, there are plenty of new artists on the list, artists like Icona Pop, Avicii and Zedd that have had a few charting songs. Being tagged as a shallow artist isn’t necessarily bad, it just means that your music is dominated by a handful of hits. That’s why we find Adele and Jeff Buckley on the same list as Paris Hilton and Smash Mouth.
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
It has been nearly 10 years since Chris Anderson’s Wired article and subsequent book called The Long Tail. In the article, book, and subsequent blog posts Chris (and I can call him Chris because we once had a 3 minute conversation in Bilbao Spain, so we are friends) showed data about how music listening is changing as we move away from the physical constraints of CD shelves and replace them with the infinite virtual shelves of the online music store.
I thought it was time to look at the data again to see if the trends that my good friend Chris was seeing back in 2004 still persist today. In particular, in the blog post Latest Rhapsody data and more Chris showed how a substantial fraction of the music market is shifting away from the Walmart inventory of the top 50,000 tracks:
This chart shows that as Rhapsody’s collection size increased the amount of listener market share in the songs that were not in the top 50K grew from 26%, to 28% and then 30% over 3 years. Today’s music subscription services boast 10 million or more songs (but of course, those numbers start to get a bit meaningless beyond a certain point – it becomes filler). Let’s take a look to see if the fraction of listening that is not in the top 50K tracks has continued to grow. Here’s some pie:
This data shows that in 2013, with 10million+ tracks available, 42% of listens can be found in the long tail (i.e. beyond the top 50K tracks). So the trend has continued. More listening is taking place in less popular music.
In the same blog post, my pal Chris presents this Hitland vs. nicheland chart that shows what percentage of the music business is selling the top 100 artists. Back in 2006, 50% of Walmart’s music business was selling music by the top 100 artists, while for Rhapsody, about 25% of market share was in the top 100 artists.
Lets’s extend this chart using listening data from 2013:
As you can see, the trend continues, only 20% of listening is in the top 100 artists. It’s not a dramatic change, but it does show that the more music you make available, the deeper the catalog, the deeper the listening.
Here are a few more fun facts about today’s listening. 80% of all listening is concentrated in the top 5,000 artists. The top 1,000 songs account for about 13% of all listening, and 80% of all listening is spread over about 222,0000 songs.
My good buddy Chris’s Long Tail hypothesis has come under considerable scrutiny in the last few years, but by looking at the data we see that the trends Chris pointed out have continued. Our listening is less concentrated in the hits than ever before. Yes, hits are important and will alway be, but if you make more music available to listeners, they will indeed listen to it. So, remember my best friend’s three rules for the Long Tail:
- Make everything available
- Cut the price in half – now lower it
- Help me find it.
At The Echo Nest, we work hard to make rule 3 a reality – our mission is to help connect people with the best music, whether it be in the short head, or deep into that Long Tail.