The documentary "" aims to discover the science behind the perfect pop song. By analysing the history of pop music using innovative technology, we set out to identify the secret formula for chart success. Such a project required a unique combination of musical and scientific expertise, which the ΒιΆΉΤΌΕΔ's Research and Development department has in abundance.
Professor Armand Leroi
Researchers from R&D's worked with the presenter, , as well as academics from Queen Mary University of London and Oxford University to source and analyse five years worth of music and chart data.
There were several stages to this process - initially, we had to source chart data for the last five years, identify the songs referenced, and acquire audio for each track from the ΒιΆΉΤΌΕΔ's archive. from then on, we integrated with and built upon previous work analysing our music radio output; Using cuttting-edge signal processing techniques in order to distil each track down to a set of figures or features representing the most salient features of the music, in a form that our computer models could comprehend. These features represented many different aspects of the songs - some of them more easily interpretable in musical terms than others.
Want to work on these sorts of problems with us? weβre co-supervising, entitled β" along with Professor Leroi and , as part of the London Interdisciplinary Social Science partnership.
For instance, the tempo of each track in beats-per-minute, the key, and an estimate of the song structure (ie. the number and order of verses and choruses) - all easily recognisable as musical properties of a song. Alongside these, we extracted lower level acoustic features - some (such as RMS Energy - the average sound level over a window of the song) which are also easily interpretable by humans, as well as others whose significance is less obvious, such as , , , and (which are a recent innovation in this field by our collaborator Mi Tian, from the ), but which contain valuable information about the melody, harmony, rhythm, or timbre of the music.
Tim Cowlishaw
These features were computed over a sliding window on each track, and resulted in hundreds of thousands of measurements per track - a vast amount of data to contend with. To simplify the problem, each feature was summarised per track (approaches for this vary per feature, but for example we might take the mean and variance of a given feature per track), in order to produce a single summary vector per track.These vectors were still quite large (and the individual features making them up rather opaque in their musical meaning in some cases), so we performed principal component analysis on them in order to reduce their dimension. Once weβd done this, we ended up with a smaller set of figures per track, each of which was a combination of our original low-level features, and which were far more amenable to a musical interpretation. For instance, one principal component might be strongly correlated with electronic instrumentation, another with , and another with .
By looking at how these features varied in strength between songs, and over chart positions and time, the programme makers were able to identify the features of a song most closely correlated with chart success - the secret blueprint for perfect pop, as well as as well as producing a programme which presents the techniques and power of data science in a way thatβs engaging and informative for our audience.
, Professor Leroi teams up with music producer Trevor Horn and you can see what happens when he tries to use data to make a song by unsigned artist into a chart-topper. He also finds out whether he can identify - without listening to a single note - which tracks uploaded to are most likely to be a hit. on February 28th at 9pm on ΒιΆΉΤΌΕΔ Four.
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