Archive for category tags

Are these the angriest tracks on the web?

I built a playlist of songs that most frequently appear in playlists with the words angry or mad with the Smart Playlist Builder. These are arguably some of the angriest tracks on the web.

It is interesting to compare these angry tracks to the top tracks tagged with angry at


I can’t decide whether the list derived from angry playlists is better or worse than the list driven by social tags. I’d love to hear your opinion. Take a look at these two lists and tell me which list is a better list of angry tracks and why.

yep, this is totally unscientific poll, but I’m still interested in what you think.


Map of Music Styles

I spent this weekend at Rethink Music Hackers’ Weekend building a music hack called Map of Music Styles (aka MOMS).  This hack presents a visualization of over 1000 music styles. You can pan and zoom through the music space just like you can with Google maps.  When you see an interesting style of music you can click on it to hear some samples of music of that style.

It is fun to explore all the different neighborhoods of music styles. Here’s the Asian corner:

Here’s the Hip-Hop neighborhood:

And a mega-cluster of ambient/chill-out music:

To build the app, I collected the top 2,000 or so terms via The Echo Nest API. For each term I calculated the most similar terms based upon artist overlap (for instance, the term ‘metal’ and ‘heavy metal’ are often applied to the same artists and so can be considered similar, where as ‘metal’ and ‘new age’ are rarely applied to the same artist and are, therefore, not similar).  To layout the graph I used  Gephi (Its like Photoshop for graphs)  and exported the graph to SVG.  After that it was just a bit of Javascript, HTML, and CSS to create the web page that will let you pan and zoom. When you click on a term, I fetch audio  that matches the style via the Echo Nest and 7Digital APIs.

There are a few non-styles that snuck through – the occasional band name, or mood, but they don’t hurt anything so I let them hang out with the real styles.   The app works best in Chrome. There’s a bug in the Firefox version that I need to work out.

Give it a try and let me know how you like it:    Map of Music Styles

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The Labyrinth of Genre

I’m fascinated with how music genres relate to each other, especially how one can use different genres as stepping stones as a guide through the vast complexities of music.   There are thousands of genres, some like rock or pop represent thousands of artists, while some like Celtic Metal or Humppa may represent only a handful of artists.   Building a map by hand that represents the relationships of all of these genres is a challenge.  Is Thrash Metal more closely related to Speed Metal or to Power Metal?  To sort this all out I’ve built a Labyrinth of Genre that lets you explore the many genres.  The Labyrinth lets you wander though about a 1000 genres, listening to samples from representative artists.

The Labyrinth of Genre

Click on a genre and  the labyrinth will be expanded to show similar half a dozen similar genres and you’ll hear songs in the genre.

I built the labyrinth by analyzing a large collection of tags.  I used the cosine distance of  tf-idf weighted tagged artists as a distance metric for tags. When you click on a node, I attach the six closest tags that haven’t already been attached to the graph. I then use the Echo Nest APIs to get all the media.

Even though it’s a pretty simple algorithm, it is quite effective in grouping similar genre. If you are interested in wandering around a maze of music, give the Labyrinth of Genre a try.


A Genre Map

Inspired by an email exchange with Samuel Richardson, creator of ‘Know your genre‘  I created a genre map that might serve as a basis for a visual music explorer (perhaps something to build at one of the upcoming music hack days).  The map is  big and beautiful (in a geeky way).  Here’s an excerpt, click on it to see the whole thing.

Update – I’ve made an interactive exploration tool that lets you wander through the genre graph. See the Labyrinth of Genre

The Labyrinth of Genre


Update 2 – Colin asked the question “What’s the longest path between two genres?” – If I build the graph by using the 12 nearest neighbors to each genre, find the minimum spanning tree for that graph and then find the longest path, I find this 31 step wonder:


Of course there are lots of ways to skin this cat – if I build the graph with just the nearest 6 neighbors, and don’t extract the minimum spanning tree, the longest path through the graph is 10 steps:



A few years back I created a data set of social tags from RJ at graciously gave permission for me to distribute the dataset for research use.  I hosted the dataset on the media server at Sun Labs. However, with the Oracle acquisition, the media server is no longer serving up the data, so I thought I would post the data elsewhere.

The dataset is now available for download here: Lastfm-ArtistTags2007

Here are the details as told in the README file:

The LastFM-ArtistTags2007 Data set
Version 1.0
June 2008

What is this?

    This is a set of artist tag data collected from using
    the Audioscrobbler webservice during the spring of 2007.

    The data consists of the raw tag counts for the 100 most
    frequently occuring tags that listeners have applied
    to over 20,000 artists.

    An undocumented (and deprecated) option of the audioscrobbler
    web service was used to bypass the normalization of tag
    counts.  This data set provides raw tag counts.

Data Format:

  The data is formatted one entry per line as follows:



    11eabe0c-2638-4808-92f9-1dbd9c453429<sep>Deerhoof<sep>art punk<sep>21
    11eabe0c-2638-4808-92f9-1dbd9c453429<sep>Deerhoof<sep>art rock<sep>18

Data Statistics:

    Total Lines:      952810
    Unique Artists:    20907
    Unique Tags:      100784
    Total Tags:      7178442


    Some minor filtering has been applied to the tag data. will
    report tag with counts of zero or less on occasion. These tags have
    been removed.

    Artists with no tags have not been included in this data set.
    Of the nearly quarter million artists that were inspected, 20,907
    artists had 1 or more tags.


    ArtistTags.dat  - the tag data
    README.txt      - this file
    artists.txt     - artists ordered by tag count
    tags.txt        - tags ordered by tag count


    The data in LastFM-ArtistTags2007 is distributed with permission of  The data is made available for non-commercial use only under
    the Creative Commons Attribution-NonCommercial-ShareAlike UK License.
    Those interested in using the data or web services in a commercial
    context should contact partners at last dot fm. For more information


    Thanks to for providing the access to this tag data via their
    web services


    This data was collected, filtered and by Paul Lamere of The Echo Nest. Send
    questions or comments to



1 Comment

Social Tags and Music Information Retrieval

It is paper writing season with the ISMIR submission deadline just four days away.  In the last few days a couple of researchers have asked me for a copy of the article I wrote for the Journal of New Music Research on social tags.    My copyright agreement with the JNMR lets me post a pre-press version of the article – so here’s a version that is close to what appeared in the journal.

Social Tagging and Music Information Retrieval

Social tags are free text labels that are applied to items such as artists, albums and songs.  Captured in these tags is a great deal of information that is highly relevant to Music Information Retrieval (MIR) researchers including information about genre, mood, instrumentation, and quality. Unfortunately there is also a great deal of irrelevant information and noise in the tags.
Imperfect as they may be, social tags are a source of human-generated contextual knowledge about music that may become an essential part of the solution to many MIR problems. In this article, we describe the state of the art in commercial and research social tagging systems for music.   We describe how tags are collected and used in current systems.  We explore some of the issues that are encountered when using tags, and we suggest possible areas of exploration for future research.

Here’s the reference:

Paul Lamere. Social tagging and music information retrieval. Journal of  New Music Research, 37(2):101–114.

1 Comment’s new player pushed out a new web-based music player that has some nifty new features including an artist slideshow, multi-tag radio and multi-artist radio.  It is pretty nice.


I like the new artist slide show (it is very Snapp Radio like), but they seem to run out of unique artist images rather quickly – and what’s with the grid?  It looks like I am  looking at the artists through a screen window.

I really like the multi-tag radio, but it is not 100% clear to me whether it is finding music that has been tagged with all the tags or whether it just alternates between the tags.  Hopefully it is the former. Update: It is the former.


It is nice to see Multi-tag radio come out of the playground and into the main player.  It is a great way to get a much more fined-tuned listening experience.  I do worry that is de-emphasizing tags though.  They only show a couple of tags in the player and it is hard to tell whether these are artist, album or track tags.’s biggest treasure trove is their tag data, so  they should be very careful to avoid any interface tweaks that may reduce the number of tags they collect.