Archive for category music information retrieval

Gender Specific Listening

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.

beats-enrollment

Selecting your favorite genres is part of the nifty user interview for Beat’s music

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:

Music_for_everyone__-_Spotify_-__Private_Browsing_

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

Rank All Male Female
1 Rihanna Eminem Rihanna
2 Bruno Mars Daft Punk Bruno Mars
3 Eminem Jay-Z Beyoncé
4 Katy Perry Bruno Mars Katy Perry
5 Justin Timberlake Drake P!nk

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:

click for full chart

click for full chart

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.

Rank Old Rank Artist
1 2 Bruno Mars
2 1 Rihanna
3 5 Justin Timberlake
4 4 Katy Perry
5 6 Drake
6 15 Chris Brown
7 3 Eminem
8 8 P!nk
9 11 David Guetta
10 14 Usher
11 17 Maroon 5
12 7 Jay-Z
13 13 Adele
14 9 Beyoncé
15 12 Lil Wayne
16 23 Lana Del Rey
17 25 Robin Thicke
18 24 Pitbull
19 27 The Black Eyed Peas
20 19 Lady Gaga
21 20 Michael Jackson
22 10 Daft Punk
23 18 Miley Cyrus
24 22 Macklemore & Ryan Lewis
25 28 Coldplay
26 16 Taylor Swift
27 26 Calvin Harris
28 21 Alicia Keys
29 29 Imagine Dragons
30 30 Britney Spears
31 44 Ellie Goulding
32 31 Kanye West
33 42 J. Cole
34 41 T.I.
35 52 LMFAO
36 32 Shakira
37 35 Bob Marley
38 54 will.i.am
39 36 Ke$ha
40 39 Wiz Khalifa

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
  • N.W.A
  • Jimi Hendrix
  • Limp Bizkit
  • Wu-Tang Clan
  • Xzibit
  • The Who
  • Moby
  • Alice in Chains
  • Soundgarden
  • Black Sabbath
  • Stone Temple Pilots
  • Mobb Deep
  • Queens of the Stone Age
  • Ice Cube
  • Kavinsky
  • Audioslave
  • Pantera

Top female-skewed artists:
artists that skew towards female fans

  • Danity Kane
  • Cody Simpson
  • Hannah Montana
  • Emily Osment
  • Playa LImbo
  • Vanessa Hudgens
  • Sandoval
  • Miranda Lambert
  • Sugarland
  • Aly & AJ
  • Christina Milian
  • Noel Schajris
  • Maria José
  • Jesse McCartney
  • Bridgit Mendler
  • Ashanti
  • Luis Fonsi
  • La Oreja de Van Gogh
  • Michelle Williams
  • Lindsay Lohan

Gender-skewed Genres

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:

  • Pop
  • Dance Pop
  • Contemporary  Hit Radio
  • Urban Contemporary
  • R&B
  • Hot Adult Contemporary
  • Latin Pop
  • Teen Pop
  • Neo soul
  • Latin
  • Pop rock
  • Contemporary country

Genres most skewed to male listeners:

  • Rock
  • Hip Hop
  • House
  • Album Rock
  • Rap
  • Pop Rap
  • Indie Rock
  • Funk Rock
  • Gangster Rap
  • Electro house
  • Classic rock
  • Nu metal

Summary

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’

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The Zero Button Music Player

Ever since the release of the Sony Walkman 35 years ago, the play button has been the primary way we interact with music. Now the play button stands as the last barrier between a listener and their music. Read on to find out how we got here and where we are going next.

imgresIn the last 100 years, technology has played a major role in how we listen to and experience music. For instance, when I was coming of age musically, the new music technology was the Sony Walkman. With the Walkman, you could take your music with you anywhere. You were no longer tied to your living room record player to listen to your music. You no longer had to wait and hope that the DJ would play your favorite song when you were on the road. You could put your favorite songs on a tape and bring them with you and listen to them whenever you wanted to no matter where your were. The Sony Walkman really changed how we listened to music. It popularized the cassette format, which opened the door to casual music sharing by music fans. Music fans began creating mix tapes and sharing music with their friends. The playlist was reborn, music listening changed. All because of that one device.

20111023_ipod_jpg__1337×1563_We are once again in the middle of music+technology revolution. It started a dozen years ago with the first iPod and it continues now with devices like the iPhone combined with a music subscription service like Spotify, Rdio, Rhapsody or Deezer. Today, a music listener armed with an iPhone and a ten dollar-a-month music subscription is a couple of taps away from being able to listen to almost any song that has ever been recorded. All of this music choice is great for the music listener, but of course it brings its own problems. When I was listening to music on my Sony Walkman, I had 20 songs to choose from, but now I have millions of songs to choose from. What should I listen to next? The choices are overwhelming. The folks that run music subscription services realize that all of this choice for their listeners can be problematic. That’s why they are all working hard to add radio features like Rdio’s You.FM Personalized Radio. Personalized Radio simplifies the listening experience – instead of having to pick every song to play, the listener only needs to select one or two songs or artists and they will be presented with an endless mix of music that fits well with initial seeds.

Helping listeners pick music is especially important when you consider that not all music listeners are alike, and that most listeners are, at best, only casual music fans. A study conducted in 2003 and again in 2006 by Emap (A UK-based Advertising agency), summarized here by David Jennings, identified four main types of music listeners. Jennings describes these four main listening types as:

  • Savants – for whom everything in life is tied up with music

  • Enthusiasts – Music is a key part of life but is balanced with other interests

  • Casuals – Music plays a welcoming role, but other things are far more important

  • Indifferents – Would not lose much sleep if music ceased to exist.

These four listener categories are an interesting way to organize music listeners, but of course, real life isn’t so cut and dried. Listener categories change as life circumstances change (have a baby and you’ll likely become a much more casual music listener) and can even change based on context (a casual listener preparing for a long road-trip may act like a savant for a few days while she builds her perfect road-trip playlist).

In 2006, the distribution of people across these 4 categories was as follows:

Untitled

This chart says a lot about the music world and why it works the way it does. For instance, it gives us a guide as to how much different segments of the listening world are willing to pay for music in a year. On the chart below, I’ve added my estimate of the amount of money each listener type will spend on music in a year.

money-14

Savants will spend a thousand dollars or more on vinyl, concerts, and music subscriptions. Enthusiasts will spend $100 a year on a music subscription or, perhaps, purchase a couple of new tracks per week. Casuals will spend $10 a year (maybe splurge and buy that new Beyoncé album), while Indifferents will spend nothing on music. This is why music services like Spotify and Rdio have been exploring the Fremium model. If they want to enroll the 72% of people who are Casual or Indifferent music listeners, they need a product that costs much less than the $100 a year Enthusiasts are willing to pay.

However, price isn’t the only challenge music services face in attracting the Casuals and the Indifferents. Different types of listeners have a different tolerance around the amount of time and effort it takes to play music that they want to listen to.

A music Savant – someone who lives, eats and breathes music – is happy spending hours a day poring through music blogs, forums and review sites to find new music, while the Indifferent music listener may not even make the simplest of efforts like turning the radio on or switching to a new station if they don’t like the current song. A simple metric for the time and effort spent is Interactions Per Listening Session. In this chart, I’ve added my estimate of the number of interactions, on average, a listener of a given type will tolerate to create a listening session.

Untitled-10

Interactions per Listening Session is an indication of how many times the listener controls their music player for a listening session. That music Savant may carefully handpick each song going into a playlist after reading a few music blogs and reviews about an artist on The Hype Machine, checking out the artist bio and previewing a few tracks. The music Enthusiast may grab a few top songs from a handful of their favorite artists to build a Spotify playlist. The casual listener may fire up Pandora, select an artist station and click play, while the Indifferent music listener may passively listen to the music that is playing on the radio or in the background at the local Starbucks.

Untitled_2-6

The above chart shows why a music service like Pandora has been so successful. With its simple interface, Pandora is able to better engage the Casual listeners who don’t want to spend time organizing their listening session. A Pandora listener need only pick a station, and Pandora does all the work from there. This is why music subscription services hoping to attract more users are working hard to add Pandora-like features. In order to make their service appeal to the Casuals, they need to make it incredibly easy to have a good listening experience.

But what about those Indifferents? If 40% of people are indifferent to music, is this a lost market for music services? Is it impossible to reach people who can’t even be bothered to queue up some music on Pandora? I don’t think so. Over the last 75 years, terrestrial radio has shown that even the most indifferent music fan can be coaxed into simple, “lean back” listening. Even with all of the media distractions in the world today, 92% of Americans age 12 or older listen to the radio at least weekly, much the same as it was back in 2003 (94%).

So what does it take to capture the ears of Indifferents? First, we have to drive the out-of-pocket costs to the listener to zero. This is already being done via the Freemium model – Ad supported Internet radio (non-on-demand) is becoming the standard entry point for music services. Next, and perhaps more difficult, we have to drive the number of interactions required to listen to music to zero.

Thus my current project – Zero UI – building a music player that minimizes the interactions necessary to get good music to play – a music player that can capture the attention of even the musically indifferent.

Implicit signals and context
Perhaps the biggest challenge in creating a Zero UI music player is how to get enough information about the listener to make good music choices. If a Casual or Indifferent listener can’t be bothered to explicitly tell us what kind of music they like, we have to try to figure it out based upon implicit signals. Luckily, a listener gives us all kinds of implicit signals that we can use to understand their music taste. Every time a listener adjusts the volume on the player, every time they skip a song, every time they search for an artist, or whenever they abandon a listening session, they are telling us a little bit about their music taste. In addition to the information we can glean from a listener’s implicit actions, there’s another source of data that we can use to help us understand a music listener. That’s the listener’s music listening device – i.e. their phone.

iphone_5s_-_Google_Search

The mobile phone is now and will continue to be the primary way for people to interact with and experience music. My phone is connected to a music service with 25 million songs. It ‘knows’ in great detail what music I like and what I don’t like. It knows some basic info about me such as my age and sex. It knows where I am, and what I am doing – whether I’m working, driving, doing chores or just waking up. It knows my context – the time of day, the day of the week, today’s weather, and my schedule. It knows that I’m late for my upcoming lunch meeting and it even might even know the favorite music of the people I’m having lunch with.

Current music interfaces use very little of the extra context provided by the phone to aid in music exploration and discovery. In the Zero UI project, I’ll explore how all of this contextual information provided by the latest devices (and near future devices) can be incorporated into the music listening experience to help music listeners organize, explore, discover and manage their music listening. The goal is to create a music player that knows the best next song to play for you given your current context. No button pressing required.

There are lots of really interesting areas to explore:

  • Can we glean enough signal from the set of minimal listener inputs?

  • Which context types (user activity, location, time-of-day, etc.) are most important for scheduling music? Will we suffer from the curse of dimensionality with too many contexts?

  • What user demographic info is most useful for avoiding the cold start problem (age, sex, zip code)?

  • How can existing social data (Facebook likes, Twitter follows, social tags, existing playlists) be used to improve the listening experience?

  • How can we use information from new wearable devices such as the Jawbone’s Up, the Fitbit,  and the Pebble Smart Watch to establish context?

  • How do we balance knowing enough about a listener to give them good music playlists and knowing so much about a listener that they are creeped out about their ‘stalker music player’?

Over the next few months I’ll be making regular posts about Zero-UI. I’ll share ideas, prototypes and maybe even some code. Feel free to follow along.

Conceptual zero-ui player that maps music listening onto user activity (as tracked by moves-app )

Conceptual zero-ui player that maps music listening onto user activity (as tracked by moves-app )

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How music recommendation works – and doesn’t work

Brian just posted  ‘How Music Recommendation works – and doesn’t work‘ over at his Variogr.am blog.  It is a must-read for anyone interested in the state of the art in music recommendation.  Here’s an excerpt:

 Try any hot new artist in Pandora and you’ll get the dreaded:

Pandora not knowing about YUS

This is Pandora showing its lack of scale. They won’t have any information for YUS  for some time and may never unless the artist sells well. This is bad news and should make you angry: why would you let a third party act as a filter on top of your very personal experiences with music? Why would you ever use something that “hid” things from you?

Grab a coffee, sit back and read Brian’s post. Highly recommended.

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Visualizing the Structure of Pop Music

The Infinite Jukebox generates plots of songs in which the most similar beats are connected by arcs. I call these plots cantograms. For instance, below is a labeled cantogram for the song Rolling in the Deep by Adele. The song starts at 3:00 on the circle and proceeds clockwise, beat by beat completely around the circle. I’ve labeled the plot so you can see how it aligns with the music. There’s an intro, a first verse, a chorus, a second verse, etc. until the outro and the end of the song.

Rolling in the Deep (labelled) by Adele

One thing that’s interesting is that most of the beat similarity connections occur between the beats in the three instances of the chorus. This certainly makes intuitive sense. The verses have different lyrics, so for the most part they won’t be too similar to each other, but the choruses have the same lyrics, the same harmony, the same instrumentation. They may even be, for all we know may even be exactly the same audio, that perfect performance, cut and pasted three times by the audio engineer to make the best sounding version of the song.

Now take a look at the cantogram for another popular song. The plot below shows the beat similarities for the song Tik Tok by Ke$ha. What strikes me the most about this plot is how similar it looks to the plot for Rolling in the Deep. It has the characteristic longer intro+first verse, some minor inter-verse similarities and the very strong similarities between the three choruses.

Tik Tok by Ke$ha

As we look at more plots for modern pop music we see the same pattern over and over again. In this plot for Lady Gag’s Paparazzi a cantogram we again see the same pattern.

Lady Gaga – Paparazzi

We see it in the plot for Justin Bieber’s Baby:

Justin Bieber – Baby

Taylor Swift’s Fearless has a two verses before the first chorus, shifting it further around the circle, but other than that the pattern holds:

Taylor Swift – Fearless

Now compare and contrast the pop cantograms with those from other styles of music. First up is Led Zeppelin’s Stairway to heaven. There’s no discernable repeating chorus, or global song repetition, the only real long-arc repetition occurs during the guitar solo for the last quarter of the song.

Led Zeppelin – Stairway to Heaven

Here’s another style of music. Deadmau5′s Raise your weapon. This is electronica (and maybe some dubstep). Clearly from the cantogram we can see that is is not a traditional pop song. Very little long arc repetition, with the densest cluster being the final dubstep break.

Deadmau5 – Raise your weapon

Dave Brubeck’s Take Five has a very different pattern, with lots of short term repetition during the first half of the song, while during the second half with Joe Morello’s drum solo there’s a very different pattern.

Dave Brubeck – Take Five

Green Grass and High Tides has yet a different pattern – no three choruses and out here. (By the way, the final guitar solo is well worth listening to in the Infinite Jukebox. It is the guitar solo that never ends).

Green Grass And High Tides by The Outlaws

The progressive rock anthem Roundabout doesn’t have the Pop Pattern

Yes – Roundabout

Nor does Yo-Yo Ma’s performance of the Cello suite No. 1.

01 Cello Suite No.1, 1. Prelude by Yo-Yo Ma

Looking at the pop plots one begins to understand that pop music really could be made in a factory. Each song is cut from the same mold. In fact, one of the most successful pop songs in recent years, was produced by a label with factory in its name. Looking at Rebecca Black’s Friday we can tell right away that it is a pop song:

Friday by Rebecca Black

Compare that plot to this years Youtube breakout, Thanksgiving by Nicole Westbrook, (another Ark Music Factory assembly):

Nicole Westbrook – It’s Thanksgiving (Official Video)

The plot has all the makings of the standard pop song for the 2010s.

In the music information retrieval research community there has been quite a bit of research into algorithmically extracting song structure, and visualizations are often part of this work. If you are interested in learning more about this research, I suggest looking at some of the publications by Meinard Müller and Craig Sapp.

Of course, not every pop song will follow the pattern that I’ve shown here. Nevertheless, I find it interesting that this very simple visualization is able to show us something about the structure of the modern pop song, and how similar this structure is across many of the top pop songs.

update: since publishing this post I’ve updated the layout algorithm in the Infinite Jukebox so that songs start and end at 12 Noon and not 3PM, so the plots you see in this post are rotated 90degrees clockwise from what you would see in the jukebox.

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Data Mining Music – a SXSW 2012 Panel Proposal

I’ve submitted a proposal for a SXSW 2012 panel called Data Mining Music.  The PanelPicker page for the talk is here:  Data Mining Music.  If you feel so inclined feel free to comment and/or vote for the talk. I promise to fill the talk  with all sorts of fun info that you can extract from datasets like the Million Song Dataset.

Here’s the abstract:

Data mining is the process of extracting patterns and knowledge from large data sets. It has already helped revolutionized fields as diverse as advertising and medicine. In this talk we dive into mega-scale music data such as the Million Song Dataset (a recently released, freely-available collection of detailed audio features and metadata for a million contemporary popular music tracks) to help us get a better understanding of the music and the artists that perform the music.

We explore how we can use music data mining for tasks such as automatic genre detection, song similarity for music recommendation, and data visualization for music exploration and discovery. We use these techniques to try to answers questions about music such as: Which drummers use click tracks to help set the tempo? or Is music really faster and louder than it used to be? Finally, we look at techniques and challenges in processing these extremely large datasets.

Questions answered:

  1. What large music datasets are available for data mining?
  2. What insights about music can we gain from mining acoustic music data?
  3. What can we learn from mining music listener behavior data?
  4. Who is a better drummer: Buddy Rich or Neil Peart?
  5. What are some of the challenges in processing these extremely large datasets?

Flickr photo CC by tristanf

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How do you spell ‘Britney Spears’?

I’ve been under the weather for the last couple of weeks, which has prevented me from doing most things, including blogging. Luckily, I had a blog post sitting in my drafts folder almost ready to go.  I spent a bit of time today finishing it up, and so here it is. A look at the fascinating world of spelling correction for artist names.

 
In today’s digital music world, you will often look for music by typing an artist name into a search box of your favorite music app.   However this becomes a problem if you don’t  know how to spell the name of the artist you are looking for. This is probably not much of a problem if you are  looking for U2, but it most definitely is a problem if you are looking for Röyksopp, Jamiroquai or  Britney Spears. To help solve this problem, we can try to identify common misspellings for artists and use these misspellings to help steer you to the artists that you are looking for.

A spelling corrector in 21 lines of code
A good place for us to start  is a post by  Peter Norvig (Director of Research at Google) called  ‘How to write a spelling corrector‘ which presents a fully operational spelling corrector in 21 lines of Python.  (It is a phenomenal bit of code, worth the time studying it).  At the core of Peter’s  algorithm is the concept of the edit distance  which is a way to represent the similarity of two strings by calculating the number of operations (inserts, deletes, replacements and transpositions) needed to transform one string into the other.  Peter cites literature that suggests that 80 to 95% of spelling errors are within an edit distance of 1 (meaning that  most misspellings are just one insert, delete, replacement or transposition away from the correct word).     Not being satisfied with that accuracy, Peter’s algorithm considers all words that are within an edit distance of 2 as candidates for his spelling corrector.  For Peter’s small test case (he wrote his system on a plane so he didn’t have lots of data nearby), his corrector covered 98.9% of his test cases.

Spell checking Britney
A few years ago, the smart folks at Google posted a list of Britney Spears spelling corrections that shows nearly 600 variants on Ms. Spears name collected in three months of Google searches.   Perusing the list, you’ll find all sorts of interesting variations such as ‘birtheny spears’ , ‘brinsley spears’ and ‘britain spears’.  I suspect that some these queries (like ‘Brandi Spears’) may actually not be for  the pop artist. One curiosity in the list is that although there are 600 variations on the spelling of ‘Britney’ there is exactly one way that ‘spears’ is spelled.  There’s no ‘speers’ or ‘spheres’, or ‘britany’s beers’ on this list.

One thing I did notice about Google’s list of Britneys is that there are many variations that seem to be further away from the correct spelling than an edit distance of two at the core of Peter’s algorithm.  This means that if you give these variants to Peter’s spelling corrector, it won’t find the proper spelling. Being an empiricist I tried it and found that of the 593  variants of ‘Britney Spears’,  200 were not within an edit distance of two of the proper spelling and would not be correctable.  This is not too surprising.  Names are traditionally hard to spell, there are many alternative spellings for the name ‘Britney’ that are real names, and many people searching for music artists for the first time may have only heard the name pronounced and have never seen it in its written form.

Making it better with an artist-oriented spell checker
A 33% miss rate for a popular artist’s name seems a bit high, so  I thought I’d see if I could improve on  this.  I have one big advantage that Peter didn’t. I work for a music data company so I can be pretty confident that all the search queries that I see are going to be related to music. Restricting the possible vocabulary to just artist names makes things a whole lot easier. The algorithm couldn’t be simpler. Collect the names of the top 100K most popular artists. For each artist name query,  find the artist name with the smallest edit distance to the query and return that name as the best candidate match.  This algorithm will let us find the closest matching artist even if it is has an edit distance of more than 2 as we see in Peter’s algorithm.  When I run this against the 593 Britney Spears misspellings, I only get one mismatch – ‘brandi spears’ is closer to the artist ‘burning spear’ than it is to ‘Britney Spears’.  Considering the naive implementation, the algorithm is fairly fast (40 ms per query on my 2.5 year old laptop, in python).

Looking at spelling variations
With this artist-oriented spelling checker in hand,  I decided to take a look at some real artist queries to see what interesting things I could find buried within.   I gathered some artist name search queries from the Echo Nest API logs and looked for some interesting patterns (since I’m doing this at home over the weekend, I only looked at the most recent logs which consists of only about 2 million artist name queries).

Artists with most spelling variations
Not surprisingly, very popular artists are the most frequently misspelled.  It seems that just about every permutation has been made in an attempt to spell these artists.

  • Michael Jackson - Variations: michael jackson,  micheal jackson,  michel jackson,  mickael jackson,  mickal jackson,  michael jacson,  mihceal jackson,  mickeljackson,  michel jakson,  micheal jaskcon,  michal jackson,  michael jackson by pbtone,  mical jachson,  micahle jackson,  machael jackson,  muickael jackson,  mikael jackson,  miechle jackson,  mickel jackson,  mickeal jackson,  michkeal jackson,  michele jakson,  micheal jaskson,  micheal jasckson,  micheal jakson,  micheal jackston,  micheal jackson just beat,  micheal jackson,  michal jakson,  michaeljackson,  michael joseph jackson,  michael jayston,  michael jakson,  michael jackson mania!,  michael jackson and friends,  michael jackaon,  micael jackson,  machel jackson,  jichael mackson
  • Justin BieberVariations: justin bieber,  justin beiber,  i just got bieber’ed by,  justin biber,  justin bieber baby,  justin beber,  justin bebbier,  justin beaber,  justien beiber,  sjustin beiber,  justinbieber,  justin_bieber,  justin. bieber,  justin bierber,  justin bieber<3 4 ever<3,  justin bieber x mstrkrft,  justin bieber x,  justin bieber and selens gomaz,  justin bieber and rascal flats,  justin bibar,  justin bever,  justin beiber baby,  justin beeber,  justin bebber,  justin bebar,  justien berbier,  justen bever,  justebibar,  jsustin bieber,  jastin bieber,  jastin beiber,  jasten biber,  jasten beber songs,  gestin bieber,  eiine mainie justin bieber,  baby justin bieber,
  • Red Hot Chili PeppersVariations: red hot chilli peppers,  the red hot chili peppers,  red hot chilli pipers,  red hot chilli pepers,  red hot chili,  red hot chilly peppers,  red hot chili pepers,  hot red chili pepers,  red hot chilli peppears,  redhotchillipeppers,  redhotchilipeppers,  redhotchilipepers,  redhot chili peppers,  redhot chili pepers,  red not chili peppers,  red hot chily papers,  red hot chilli peppers greatest hits,  red hot chilli pepper,  red hot chilli peepers,  red hot chilli pappers,  red hot chili pepper,  red hot chile peppers
  • Mumford and SonsVariations: mumford and sons,  mumford and sons cave,  mumford and son,  munford and sons,  mummford and sons,  mumford son,  momford and sons,  modfod and sons,  munfordandsons,  munford and son,  mumfrund and sons,  mumfors and sons,  mumford sons,  mumford ans sons,  mumford and sonns,  mumford and songs,  mumford and sona,  mumford and,  mumford &sons,  mumfird and sons,  mumfadeleord and sons
  • Katy Perry - Even an artist with a seemingly very simple name like Katy Perry has numerous variations:  katy perry,  katie perry,  kate perry,    kathy perry,  katy perry ft.kanye west,  katty perry,  katy perry i kissed a girl,  peacock katy perry,  katyperry,  katey parey,   kety perry,  kety peliy,  katy pwrry,  katy perry-firework,  katy perry x,  katy perry,  katy perris,  katy parry,  kati perry,  kathy pery,  katey perry,  katey perey,  katey peliy,  kata perry,  kaity perry

Some other most frequently misspelled artists:

  • Britney Spears
  • Linkin Park
  • Arctic Monkeys
  • Katy Perry
  • Guns N’ Roses
  • Nicki Minaj
Which artists are the easiest to spell?
Using the same techniques we can look through our search logs and find the popular artists that have the fewest misspelled queries. These are the easiest to spell artists. They include:
  • Muse
  • Weezer
  • U2
  • Oasis
  • Moby
  • Flyleaf
  • Seether
Most confused artists:
Artists are most easily confused with another include:
  • byran adams - ryan adams
  • Underworld – Uverworld
Wrapping up
Spelling correction for artist names is perhaps the least sexiest job in the music industry, nevertheless it is an important part of helping people connect with the music they are looking for.   There is a large body of research around context-sensitive spelling correction that can be used to help solve this problem, but even very simple techniques like those described here can go along way to helping you figure out what someone really wants when they search for ‘Jastan Beebar’.

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Do you do Music Information Retrieval?

We’re ramping up hiring at the Echo Nest. We’re looking for good MIR people at different experience levels to help us realize the company’s vision of knowing everything about all music automatically. I would guess that we are the closest analog to ISMIR in the industry– we only do music (audio and text), the base technology is straight out of our dissertations (brian, tristan)  and we’re active in conferences and universities. We work with an amazing amount of music data on a daily basis and we sell it to some great people and companies that are changing the face of music.

MIR-background candidates are especially encouraged to apply as long as you have relevant experience and want to work on implementation at a very fast growing startup. These are almost all full time positions in our offices near Boston, MA USA. Even if you’re not graduating for a while let us know if you’re interested now.

More info at: http://the.echonest.com/company/jobs/

Group coding session at The Echo Nest

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Upbeat and Quirky, With a Bit of a Build: Interpretive Repertoires in Creative Music Search

Upbeat and Quirky, With a Bit of a Build: Interpretive Repertoires in Creative Music Search
Charlie Inskip, Andy MacFarlane and Pauline Rafferty

ABSTRACT Pre-existing commercial music is widely used to accompany moving images in films, TV commercials and computer games. This process is known as music synchronisation. Professionals are employed by rights holders and film makers to perform creative music searches on large catalogues to find appropriate pieces of music for syn- chronisation. This paper discusses a Discourse Analysis of thirty interview texts related to the process. Coded examples are presented and discussed. Four interpretive re- pertoires are identified: the Musical Repertoire, the Soundtrack Repertoire, the Business Repertoire and the Cultural Repertoire. These ways of talking about music are adopted by all of the community regardless of their interest as Music Owner or Music User.

Music is shown to have multi-variate and sometimes conflicting meanings within this community which are dynamic and negotiated. This is related to a theoretical feedback model of communication and meaning making which proposes that Owners and Users employ their own and shared ways of talking and thinking about music and its context to determine musical meaning. The value to the music information retrieval community is to inform system design from a user information needs perspective.

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What Makes Beat Tracking Difficult? A Case Study on Chopin Mazurkas

What Makes Beat Tracking Difficult? A Case Study on Chopin Mazurkas
Peter Grosche, Meinard Müller and Craig Stuart Sapp

ABSTRACT – The automated extraction of tempo and beat information from music recordings is a challenging task. Especially in the case of expressive performances, current beat tracking approaches still have significant problems to accurately capture local tempo deviations and beat positions. In this paper, we introduce a novel evaluation framework for detecting critical passages in a piece of music that are prone to tracking errors. Our idea is to look for consistencies in the beat tracking results over multiple performances of the same underlying piece. As another contribution, we further classify the critical passages by specifying musical properties of certain beats that frequently evoke trac ing errors. Finally, considering three conceptually different beat tracking procedures, we conduct a case study on the basis of a challenging test set that consists of a variety of piano performances of Chopin Mazurkas. Our experimental results not only make the limitations of state-of-the-art beat trackers explicit but also deepens the understanding of the underlying music material.

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An Audio Processing Library for MIR Application Development in Flash

An Audio Processing Library for MIR Application Development in Flash
Jeffrey Scott, Raymond Migneco, Brandon Morton, Christian M. Hahn, Paul Diefenbach and Youngmoo E. Kim

The Audio processing Library for Flash affords music-IR researchers the opportunity to generate rich, interactive, real-time music-IR driven applications. The various lev-els of complexity and control as well as the capability to execute analysis and synthesis simultaneously provide a means to generate unique programs that integrate content based retrieval of audio features. We have demonstrated the versatility and usefulness of ALF through the variety of applications described in this paper. As interest in mu sic driven applications intensifies, it is our goal to enable the community of developers and researchers in music-IR and related fields to generate interactive web-based media.


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