The Secret Science of Pop


In The Secret Science of Pop, evolutionary biologist Professor Armand Leroi tells us that he sees pop music as a direct analogy for natural selection. And he salivates at the prospect of a huge, complete, historical data set that can be analysed in order to test his theories.

He starts off by bringing in experts in data analysis from some prestigious universities, and has them crunch the numbers on the past 50 years of chart music, analysing the audio data for numerous characteristics including “rhythmic intensity” and “agressiveness”. He plots a line on a giant computer monitor showing the rate of musical change based on an aggregate of these values. The line shows that the 60s were a time of revolution – although he claims that the Beatles were pretty average and “sat out” the revolution. Disco, and to a lesser extent punk, made the 70s a time of revolution but the 80s were not.

He is convinced that he is going to be able to use his findings to influence the production of new pop music. The results are not encouraging: no matter how he formulates his data he finds he cannot predict a song’s chart success with much better than random accuracy. The best correlation seems to be that a song’s closeness to a particular period’s “average” predicts high chart success. It is, he says, “statistically significant”.

Armed with this insight he takes on the role of producer and attempts to make a song (a ballad) being recorded at Trevor Horn’s studio as average as possible by, amongst other things, adjusting its tempo and adding some rap. It doesn’t really work, and when he measures the results with his computer, he finds that he has manoeuvred the song away from average with this manual intervention.

He then shifts his attention to trying to find the stars of tomorrow by picking out the most average song from 1200 tracks that have been sent into BBC Radio 1 Introducing. The computer picks out a particular band who seem to have a very danceable track, and in the world’s least scientific experiment ever, he demonstrates that a BBC Radio 1 producer thinks it’s OK, too.

His final conclusion: “We failed spectacularly this time, but I am sure the answer is somewhere in the data if we can just find it”.

My immediate thoughts on this programme:

-An entertaining, interesting programme.

-The rule still holds: science is not valid in the field of aesthetic judgement.

-If your system cannot predict the future stars of the past, it is very unlikely to be able to predict the stars of the future.

-The choice of which aspects of songs to measure is purely subjective, based on the scientist’s own assumptions about what humans like about music. The chances of the scientist not tweaking the algorithms in order to reflect their own intuitions are very remote. To claim that “The computer picked the song with no human intervention” is stretching it! (This applies to any ‘science’ whose main output is based on computer modelling).

-The lure of data is irresistible to scientists but, as anyone who has ever experimented with anything but the simplest, most controlled, pattern recognition will tell you, there is always too much, and at the same time never enough, training data. It slowly dawns on you that although theoretically there may be multidimensional functions that really could spot what you are looking for, you are never going to present the training data in such a way that you find a function with 100%, or at least ‘human’ levels of, reliability.

-Add to that the myriad paradoxes of human consciousness, and of humans modifying their tastes temporarily in response to novelty and fashion – even to the data itself (the charts) – and the reality is that it is a wild goose chase.

(very relevant to a post from a few months ago)


3 thoughts on “The Secret Science of Pop

  1. As usual I find myself agreeing with a rational audiophile.

    I think these machine learning researchers really underestimate the importance of chance in everyday events and processes. The example of pop music is, perhaps, one of the most extreme because the music itself is almost irrelevant where fashion, marketing and timing dominate the listeners’ overall experience. Add into this the power of positive feedback (i.e. that success breeds success AKA the Matthew Effect) and the researchers’ feature vectors fade into insignificance. Check out Leonard Mlodinow’s excellent 2008 book The Drunkard’s Walk for further examples and insight:'s_Walk


    1. Thanks for the tip – I’ll have a look at that book.

      Yes, I think the problem is all to do with randomness, dimensionality and the fickleness of the human brain.

      As you suggest, machine learning is only valid if the training data was generated by a consistent system (even though it may be very complex). Randomness throws a spanner in the works, as does human consciousness which is another variation on randomness.


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