Posts Tagged msd
Slides for my Data Mining Music talk
Posted by Paul in events, The Echo Nest on March 15, 2012
I recently gave a talk on Data Mining Music at SXSW. It was a standing room only session, with an enthusiastic audience that asked great questions. It was a really fun time for me. I’ve posted the slides to Slideshare, but be warned that there are no speaker notes so it may not always be clear what any particular slide is about. There was lots of music in the talk, but unfortunately, it is not in the Slideshare PDF. The links below should flesh out most of the details and have some audio examples.
- Have artist names been getting longer?
- The Passion Index – Find the bands that have the most passionate fans
- Six Degrees of Black Sabbath – Using artist relationship data to build a Six Degrees of Kevin Bacon for Music
- Frog-based playlisting – Building advanced playlists by finding paths through the artist space
- The Click Track Detector – Finding drummers that use a click track
- Looking for the Slow Build – Finding songs that have a gradual build
- Bohemian Rhapsichord – Turning a popular song into a musical instrument, with data.
- Midem Music Machine – Making a beautiful visualization of music
- The Swinger – Making any song swing
Thanks to everyone who attended.
The Music Matrix – Exploring tags in the Million Song Dataset
Posted by Paul in code, data, Music, The Echo Nest on November 27, 2011
Last month Last.fm contributed a massive set of tag data to the Million Song Data Set. The data set includes:
- 505,216 tracks with at least one tag
- 522,366 unique tags
- 8,598,630 (track – tag) pairs
A popular track like Led Zep’s Stairway to Heaven has dozens of unique tags applied hundreds of times.
There is no end to the number of interesting things you can do with these tags: Track similarity for recommendation and playlisting, faceted browsing of the music space, ground truth for training autotagging systems etc.
I think there’s quite a bit to be learned about music itself by looking at these tags. We live in a post-genre world where most music no longer fits into a nice tidy genre categories. There are hundreds of overlapping subgenres and styles. By looking at how the tags overlap we can get a sense for the structure of the new world of music. I took the set of tags and just looked at how the tags overlapped to get a measure of how often a pair of tags co-occur. Tags that have high co-occurrence represent overlapping genre space. For example, among the 500 thousand tracks the tags that co-occur the most are:
- rap co-occurs with hip hop 100% of the time
- alternative rock co-occurs with rock 76% of the time
- classic rock co-occurs with rock 76% of the time
- hard rock co-occurs with rock 72% of the time
- indie rock co-occurs with indie 71% of the time
- electronica co-occurs with electronic 69% of the time
- indie pop co-occurs with indie 69% of the time
- alternative rock co-occurs with alternative 68% of the time
- heavy metal co-occurs with metal 68% of the time
- alternative co-occurs with rock 67% of the time
- thrash metal co-occurs with metal 67% of the time
- synthpop co-occurs with electronic 66% of the time
- power metal co-occurs with metal 65% of the time
- punk rock co-occurs with punk 64% of the time
- new wave co-occurs with 80s 63% of the time
- emo co-occurs with rock 63% of the time
It is interesting to see how the subgenres like hard rock or synthpop overlaps with the main genre and how all rap overlaps with Hip Hop. Using simple overlap we can also see which tags are the least informative. These are tags that overlap the most with other tags, meaning that they are least descriptive of tags. Some of the least distinctive tags are: Rock, Pop, Alternative, Indie, Electronic and Favorites. So when you tell someone you like ‘rock’ or ‘alternative’ you are not really saying too much about your musical taste.
The Music Matrix
I thought it might be interesting to explore the world of music via overlapping tags, and so I built a little web app called The Music Matrix. The Music Matrix shows the overlapping tags for a tag neighborhood or an artist via a heat map. You can explore the matrix, looking at how tags overlap and listening to songs that fit the tags.
With this app you can enter a genre, style, mood or other type of tag. The app will then find the 24 tags with the highest overlap with the seed and show the confusion matrix. Hotter colors indicate high overlap. Mousing over a cell will show you the percentage overlap between the two corresponding tags and clicking on a cell will play a track that has high tag counts for the two tags. I find that I can learn a lot about a genre of music by looking at the 24 tag neighborhood for a genre and listening to examples. Some interesting neighborhoods to explore are:
You can also explore by moods:
If you are not sure what genre or style is for an artist, you can just start with the top tags for the artist like so:
Use the Music Matrix to explore a new genre of music or to find music that matches a set of styles. Find out how genres overlap. Listen to prototypical examples of different styles. Click on things, have fun. Check it out:
The code for the Music Matrix is on Github. Thanks to Thierry for creating the Million Song Data Set (the best research data set ever created) and thanks to Last.fm for contributing a very nice set of tag data to the data set.
Search for music by drawing a picture of it
I’ve spent the weekend hacking on a project at Music Hack Day Montreal. For my hack I created an application with the catchy title “Search for music by drawing a picture of it”. The hack lets you draw the loudness profile for a song and the app will search through the Million Song Data Set to find the closest match. You can then listen to the song in Spotify (if the song is in the Spotify collection).
Coding a project in 24 hours is all about compromise. I had some ideas that I wanted to explore to make the matching better (dynamic time warping) and the lookup faster (LSH). But since I actually wanted to finish my hack I’ve saved those improvements for another day. The simple matching approach (Euclidean distance between normalized vectors) works surprisingly well. The linear search through a million loudness vectors takes about 20 seconds, too long for a web app, this can be made palatable with a little Ajax .
The hack day has been great fun, kudos to the Montreal team for putting it all together.
Looking for the Slow Build
This is the second in a series of posts exploring the Million Song Dataset.
Every few months you’ll see a query like this on Reddit – someone is looking for songs that slowly build in intensity. It’s an interesting music query since it is primarily focused on what the music sounds like. Since we’ve analyzed the audio of millions and millions of tracks here at The Echo Nest we should be able to automate this type of query. One would expect that Slow Build songs will have a steady increase in volume over the course of a song, so lets look at the loudness data for a few Slow Build songs to confirm this intuition. First, here’s the canonical slow builder: Stairway to Heaven:
The green line is the raw loudness data, the blue line is a smoothed version of the data. Clearly we see a rise in the volume over the course of the song. Let’s look at another classic Slow Build – The Hall Of the Mountain King – again our intuition is confirmed:
Looking at a non-Slow Build song like Katy Perry’s California Gurls we see that the loudness curve is quite flat by comparison:
Of course there are other aspects beyond loudness that a musician may use to build a song to a climax – tempo, timbre and harmony are all useful, but to keep things simple I’m going to focus only on loudness.
Looking at these plots it is easy to see which songs have a Slow Build. To algorithmically identify songs that have a slow build, we can use a technique similar to the one I described in The Stairway Detector. It is a simple algorithm that compares the average loudness of the first half of the song to the average loudness of the second half of the song. Songs with the biggest increase in average loudness rank the highest. For example, take a look at a loudness plot for Stairway to Heaven. You can see that there is a distinct rise in scores from the first half to the second half of the song (the horizontal dashed lines show the average loudness for the first and second half of the song). Calculating the ramp factor we see that Stairway to Heaven scores an 11.36 meaning that there is an increase in average loudness of 11.36 decibels between the first and the second half of the song.
This algorithm has some flaws – for instance it will give very high scores to ‘hidden track’ songs. Artists will sometimes ‘hide’ a track at the end of a CD by padding the beginning of the track with a few minutes of silence. For example, this track by ‘Fudge Tunnel’ has about five minutes of silence before the band comes in.
Clearly this song isn’t a Slow Build, our simple algorithm is fooled. To fix this we need to introduce a measure of how straight the ramp is. One way to measure the straightness of a line is to calculate the Pearson correlation for the loudness data as a function of time. XY Data with correlation that approaches one (or negative one) is by definition, linear. This nifty wikipedia visualization of the correlation of different datasets shows the correlation for various datasets:
We can combine the correlation with our ramp factors to generate an overall score that takes into account the ramp of the song as well as the straightness of the ramp. The overall score serves as our Slow Build detector. Songs with a high score are Slow Build songs. I suspect that there are better algorithms for this so if you are a math-oriented reader who is cringing at my naivete please set me and my algorithm straight.
Armed with our Slow Build Detector, I built a little web app that lets you explore for Slow Build songs. The app – Looking For The Slow Build – looks like this:
The application lets you type in the name of your favorite song and will give you a plot of the loudness over the course of the song, and calculates the overall Slow Build score along with the ramp and correlation. If you find a song with an exceptionally high Slow Build score it will be added to the gallery. I challenge you to get at least one song in the gallery.
You may find that some songs that you think should get a high Slow Build score don’t score as high as you would expect. For instance, take the song Hoppipolla by Sigur Ros. It seems to have a good build, but it scores low:
It has an early build but after a minute it has reached it’s zenith. The ending is symmetrical with the beginning with a minute of fade. This explains the low score.
Another song that builds but has a low score is Weezer’s The Angel and the One.
This song has a 4 minute power ballad build – but fails to qualify a a slow build because the last 2 minutes of the song are nearly silent.
Finding Slow Build songs in the Million Song Dataset
Now that we have an algorithm that finds Slow Build songs, lets apply it to the Million Song Dataset. I can create a simple MapReduce job in Python that will go through all of the million tracks and calculate the Slow Build score for each of them to help us find the songs with the biggest Slow Build. I’m using the same framework that I described in the post “How to Process a Million Songs in 20 minutes“. I use the S3 hosted version of the Million Song Dataset and process it via Amazon’s Elastic MapReduce using mrjob – a Python MapReduce library. Here’s the mapper that does almost all of the work, the full code is on github in cramp.py:
def mapper(self, _, line): """ The mapper loads a track and yields its ramp factor """ t = track.load_track(line) if t and t['duration'] > 60 and len(t['segments']) > 20: segments = t['segments'] half_track = t['duration'] / 2 first_half = 0 second_half = 0 first_count = 0 second_count = 0 xdata =  ydata =  for i in xrange(len(segments)): seg = segments[i] seg_loudness = seg['loudness_max'] * seg['duration'] if seg['start'] + seg['duration'] <= half_track: seg_loudness = seg['loudness_max'] * seg['duration'] first_half += seg_loudness first_count += 1 elif seg['start'] < half_track and seg['start'] + seg['duration'] > half_track: # this is the nasty segment that spans the song midpoint. # apportion the loudness appropriately first_seg_loudness = seg['loudness_max'] * (half_track - seg['start']) first_half += first_seg_loudness first_count += 1 second_seg_loudness = seg['loudness_max'] * (seg['duration'] - (half_track - seg['start'])) second_half += second_seg_loudness second_count += 1 else: seg_loudness = seg['loudness_max'] * seg['duration'] second_half += seg_loudness second_count += 1 xdata.append( seg['start'] ) ydata.append( seg['loudness_max'] ) correlation = pearsonr(xdata, ydata) ramp_factor = second_half / half_track - first_half / half_track if YIELD_ALL or ramp_factor > 10 and correlation > .5: yield (t['artist_name'], t['title'], t['track_id'], correlation), ramp_factor
This code takes less than a half hour to run on 50 small EC2 instances and finds a bucketload of Slow Build songs. I’ve created a page of plots of the top 500 or so Slow Build songs found by this job. There are all sorts of hidden gems in there. Go check it out:
Looking for the Slow Build in the Million Song Dataset
The page has 500 plots all linked to Spotify so you can listen to any song that strikes your fancy. Here are some my favorite discoveries:
Respighi’s The Pines of the Appian Way
I remember playing this in the orchestra back in high school. It really is sublime. Click the plot to listen in Spotify.
Maria Friedman’s Play The Song Again
So very theatrical
Mandy Patinkin’s Rock-A-Bye Your Baby With A Dixie Melody
Another song that seems to be right off of Broadway – it has an awesome slow build.
- The Million Song Dataset – deep data about a million songs
- The Stairway Index – my first look at this stuff about 2 years ago
- How to process a million songs in 20 minutes – a blog post about how to process the MSD with mrjob and Elastic Map Reduce
- Looking for the Slow Build – a simple web app that calculates the Slow Build score and loudness plot for just about any song
- cramp.py – the MapReduce code for calculating Slow Build scores for the MSD
- Looking for the Slow Build in the Million Song Dataset – 500 loudness plots of the top Slow Builders
- Top Slow Build songs in the Million Song Dataset – the top 6K songs with a Slow Build score of 10 and above
- A Spotify collaborative playlist with a bunch of Slow Build songs in it. Feel free to add more.
How to process a million songs in 20 minutes
The recently released Million Song Dataset (MSD), a collaborative project between The Echo Nest and Columbia’s LabROSA is a fantastic resource for music researchers. It contains detailed acoustic and contextual data for a million songs. However, getting started with the dataset can be a bit daunting. First of all, the dataset is huge (around 300 gb) which is more than most people want to download. Second, it is such a big dataset that processing it in a traditional fashion, one track at a time, is going to take a long time. Even if you can process a track in 100 milliseconds, it is still going to take over a day to process all of the tracks in the dataset. Luckily there are some techniques such as Map/Reduce that make processing big data scalable over multiple CPUs. In this post I shall describe how we can use Amazon’s Elastic Map Reduce to easily process the million song dataset.
For this first experiment in processing the million song data set I want to do something fairly simple and yet still interesting. One easy calculation is to determine each song’s density – where the density is defined as the average number of notes or atomic sounds (called segments) per second in a song. To calculate the density we just divide the number of segments in a song by the song’s duration. The set of segments for a track is already calculated in the MSD. An onset detector is used to identify atomic units of sound such as individual notes, chords, drum sounds, etc. Each segment represents a rich and complex and usually short polyphonic sound. In the above graph the audio signal (in blue) is divided into about 18 segments (marked by the red lines). The resulting segments vary in duration. We should expect that high density songs will have lots of activity (as an Emperor once said “too many notes”), while low density songs won’t have very much going on. For this experiment I’ll calculate the density of all 1 million songs and find the most dense and the least dense songs.
A traditional approach to processing a set of tracks would be to iterate through each track, process the track, and report the result. This approach, although simple, will not scale very well as the number of tracks or the complexity of the per track calculation increases. Luckily, a number of scalable programming models have emerged in the last decade to make tackling this type of problem more tractable. One such approach is MapReduce.
MapReduce is a programming model developed by researchers at Google for processing and generating large data sets. With MapReduce you specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. There are a number of implementations of MapReduce including the popular open sourced Hadoop and Amazon’s Elastic MapReduce.
There’s a nifty MapReduce Python library developed by the folks at Yelp called mrjob. With mrjob you can write a MapReduce task in Python and run it as a standalone app while you test and debug it. When your mrjob is ready, you can then launch it on a Hadoop cluster (if you have one), or run the job on 10s or even 100s of CPUs using Amazon’s Elastic MapReduce. Writing an mrjob MapReduce task couldn’t be easier. Here’s the classic word counter example written with mrjob:
from mrjob.job import MRJob class MRWordCounter(MRJob): def mapper(self, key, line): for word in line.split(): yield word, 1 def reducer(self, word, occurrences): yield word, sum(occurrences) if __name__ == '__main__': MRWordCounter.run()
The input is presented to the mapper function, one line at a time. The mapper breaks the line into a set of words and emits a word count of 1 for each word that it finds. The reducer is called with a list of the emitted counts for each word, it sums up the counts and emits them.
When you run your job in standalone mode, it runs in a single thread, but when you run it on Hadoop or Amazon (which you can do by adding a few command-line switches), the job is spread out over all of the available CPUs.
MapReduce job to calculate density
We can calculate the density of each track with this very simple mrjob – in fact, we don’t even need a reducer step:
class MRDensity(MRJob): """ A map-reduce job that calculates the density """ def mapper(self, _, line): """ The mapper loads a track and yields its density """ t = track.load_track(line) if t: if t['tempo'] > 0: density = len(t['segments']) / t['duration'] yield (t['artist_name'], t['title'], t['song_id']), density
(see the full code on github)
The mapper loads a line and parses it into a track dictionary (more on this in a bit), and if we have a good track that has a tempo then we calculate the density by dividing the number of segments by the song’s duration.
Parsing the Million Song Dataset
We want to be able to process the MSD with code running on Amazon’s Elastic MapReduce. Since the easiest way to get data to Elastic MapReduce is via Amazon’s Simple Storage Service (S3), we’ve loaded the entire MSD into a single S3 bucket at http://tbmmsd.s3.amazonaws.com/. (The ‘tbm’ stands for Thierry Bertin-Mahieux, the man behind the MSD). This bucket contains around 300 files each with data on about 3,000 tracks. Each file is formatted with one track per line following the format described in the MSD field list. You can see a small subset of this data for just 20 tracks in this file on github: tiny.dat. I’ve written track.py that will parse this track data and return a dictionary containing all the data.
You are welcome to use this S3 version of the MSD for your Elastic MapReduce experiments. But note that we are making the S3 bucket containing the MSD available as an experiment. If you run your MapReduce jobs in the “US Standard Region” of Amazon, it should cost us little or no money to make this S3 data available. If you want to download the MSD, please don’t download it from the S3 bucket, instead go to one of the other sources of MSD data such as Infochimps. We’ll keep the S3 MSD data live as long as people don’t abuse it.
Running the Density MapReduce job
You can run the density MapReduce job on a local file to make sure that it works:
% python density.py tiny.dat
This creates output like this:
["Planet P Project", "Pink World", "SOIAZJW12AB01853F1"] 3.3800521773317689 ["Gleave", "Come With Me", "SOKBZHG12A81C21426"] 7.0173630509232234 ["Chokebore", "Popular Modern Themes", "SOGVJUR12A8C13485C"] 2.7012807851495166 ["Casual", "I Didn't Mean To", "SOMZWCG12A8C13C480"] 4.4351713380683542 ["Minni the Moocher", "Rosi_ das M\u00e4dchen aus dem Chat", "SODFMEL12AC4689D8C"] 3.7249476012698159 ["Rated R", "Keepin It Real (Skit)", "SOMJBYD12A6D4F8557"] 4.1905674943168156 ["F.L.Y. (Fast Life Yungstaz)", "Bands", "SOYKDDB12AB017EA7A"] 4.2953929132587785
Where each ‘yield’ from the mapper is represented by a single line in the output, showing the track ID info and the calculated density.
Running on Amazon’s Elastic MapReduce
When you are ready to run the job on a million songs, you can run it the on Elastic Map Reduce. First you will need to set up your AWS system. To get setup for Elastic MapReduce follow these steps:
- create an Amazon Web Services account: <http://aws.amazon.com/>
- sign up for Elastic MapReduce: <http://aws.amazon.com/elasticmapreduce/>
- Get your access and secret keys (go to <http://aws.amazon.com/account/> and click on “Security Credentials”)
- Set the environment variables $AWS_ACCESS_KEY_ID and $AWS_SECRET_ACCESS_KEY accordingly for mrjob.
Once you’ve set things up, you can run your job on Amazon using the entire MSD as input by adding a few command switches like so:
% python density.py --num-ec2-instances 100 --python-archive t.tar.gz -r emr 's3://tbmmsd/*.tsv.*' > out.dat
The ‘-r emr’ says to run the job on Elastic Map Reduce, and the ‘–num-ec2-instances 100’ says to run the job on 100 small EC2 instances. A small instance currently costs about ten cents an hour billed in one hour increments, so this job will cost about $10 to run if it finishes in less than an hour, and in fact this job takes about 20 minutes to run. If you run it on only 10 instances it will cost 1 or 2 dollars. Note that the t.tar.gz file simply contains any supporting python code needed to run the job. In this case it contains the file track.py. See the mrjob docs for all the details on running your job on EC2.
The output of this job is a million calculated densities, one for each track in the MSD. We can sort this data to find the most and least dense tracks in the dataset. Here are some high density examples:
Ichigo Ichie by Ryuji Takeuchi has a density of 9.2 segments/second
129 by Strojovna 07 has a density of 9.2 segments/second
The Feeding Circle by Makaton with a density of 9.1 segments per segment
Indeed, these pass the audio test, they are indeed high density tracks. Now lets look at some of the lowest density tracks.
Deviation by Biosphere with a density of .014 segments per second
The Wire IV by Alvin Lucier with a density of 0.014 segments per second
improvisiation_122904b by Richard Chartier with a density of .02 segments per second
The ‘density’ MapReduce task is about as simple a task for processing the MSD that you’ll find. Consider this the ‘hello, world’ of the MSD. Over the next few weeks, I’ll be creating some more complex and hopefully interesting tasks that show some of the really interesting knowledge about music that can be gleaned from the MSD.
(Thanks to Thierry Bertin-Mahieux for his work in creating the MSD and setting up the S3 buckets. Thanks to 7Digital for providing the audio samples)