Study Identifies World Music “Outliers”

Computers have a long and deep connection with music, and have from the beginning. This isn’t surprising to me because humans will make music with any tool they have (and with their body if they have no tools at hand).  People use digital computers and networks to make, perform, fiddle with, and share music, just as they have done music with every technology every invented.

Some people also like to use digital techniques to classify and otherwise analyze music (e.g., as “Music Information Retrieval”).  This applies widely used learning, classification, and searching schemes to digital representations of music.

I have never really been able to be excited about this topic, myself.

A recent study from Queen Mary University of London reports on “outliers in world music.”  Using large collection of digital recordings from around the world, the study looked for examples that are “different”, standing out from other music [1].

The research uses widely used data mining techniques, adapted to musical “objects”.  At bottom, this works from summary descriptions of each music sample. There are a huge, if not infinite, number of ways to describe music, so the researchers had to select a handful of characteristics.  (This is common practice, and it generally works OK.)

The overall goal is to find “outliers”, which they interpret as especially novel or creative examples.  If you have 100 songs, and one or two stand out statistically, there might be something really interesting about those two.

The research binned the music by country of origin.  This is a very approximate identification for the cultural tradition that the music might be associated with.  So, they found “outliers” within a given country, and also could report countries with relatively high numbers of outliers.  For example, in their collection, Botswana had large numbers of outliers.

This paper made me think a bit. The title and press release piqued my interest in “world music”, and made me wonder what an “outlier” would mean. But looking at the report, I see a lot of limitations.

I’m not sure what significance these “outliers” may have. The researchers imagine that these cases somehow represent innovation or creativity.  But the classification is such a blunt instrument that it’s not clear how “innovative” these examples may be, or whether other equally “creative” samples are not flagged, because they are different in ways that are not detected.

The methodology is “blunt” for many reasons. It’s a small and unsystematic sample. Yes, these are large databases, with enough data to do statistics.  But it is hard to know how representative these samples are.  The entire idea that there is some kind of Platonic ideal for, say “Brazilian music”, is lunacy of the first order.

This limited sample probably doesn’t matter too much, because the extracted features are probably obscuring them anyway.  The features used are only loosely justified, and there is not particular reason to think that they are specifically related to “creativity” or even to differences between musical traditions. Whatever is being classified here, it isn’t obvious that it has much to do with musical creativity, at least not everywhere and at all times.

(Ironically, the methodology is also “too sharp” in a crucial way.  The classification techniques are so powerful that they will find something.  They find outliers and groupings, whether such conclusions are meaningful or not.

The “world” part of the study is not exactly what I expected.  To me, “world music” means local music that is enjoyed lot’s of places other than home.  This study seems to define it as some kind of expression of aboriginal, pre-colonial, pre-mass-communication culture.  Taking this as the definition, it is certainly misleading to ‘bin’ music by country.  Countries are scarcely mono-cultural, and, by the way, minority “outliers” are often suppressed.  Finding “outliers” at the country level is interesting, but probably not indicative of “creativity” so much as stereotypes and the vagarities of the collection methods.

Finally, the entire notion that local folk music is somehow generated from a pure, unsullied local culture is highly questionable.  For centuries, musical cultures have been travelling and mixing around the world, and in the twentieth century mass communication has allowed music to spread nearly instantly nearly everywhere.

I would say that some of the most important “innovation” has been in the creative response to all these different sources.  At the very least, this means that an “outlier” in one country might be an import that would middle of the road at home, or that an imported hybrid in one country might be a shining outlier in the original country.  But these cases aren’t found by this study at all.

For example, consider American Jazz.  This music developed from many geographical roots, and now has spread throughout the world, influencing many musical styles.  So, everything that is influenced by jazz will be classified as somewhat similar, and less likely to be an “outlier”.  On the other hand, a pedestrian cover of a familiar standard might be flagged as an “outlier” compared to the rest of the “traditional” music of the country, less directly copied from overseas.  Either way, it misses the who point that Jazz has influenced and been influenced by many people, everywhere.

The point is, the methods of this study aren’t a very good way to find meaningful “outliers”.  And whatever this study is about, it probably isn’t finding anything interesting about “innovation” or “creativity”.  For that matter, it doesn’t really describe culture or music very well at all.

  1. Maria Panteli, Emmanouil Benetos, and Simon Dixon, A computational study on outliers in world music. PLOS ONE, 12 (12):e0189399, 2017.
  2. Queen Mary University of London, Computational study of world music outliers reveals countries with distinct recordings, in Queen Mary University of London – News. 2017.


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