Music is important but do we know how people listen to it? Does it depend on them or on the genre they are listening to? And the really important question - how many tracks does a DJ have to play to keep everyone happy?

Big data. It can answer important questions about health, welfare and how the nation is feeling, but what about music? Thomas Louail and Marc Barthelemy have used data from music on-demand services but they don't say which service(s) and the data isn't publicly available:

However, relatively little is known about how precisely we listen to recorded music on a daily basis. By how we refer here to some kind of detailed, quantified radioscopy of our contemporary listening practices of recorded music, an important aspect of the relation we entertain with music. Until recently, any empirical research willing to answer to questions pertaining to daily listening practices had to rely on surveys and interviews. The technological and societal evolutions have sustained the development of new mobile devices, online tools and listening possibilities, as well as new actors in the music industry. Music-on-demand services have quickly gained in popularity over the last few years, and for example, according to a recent report of the French national syndicate of phonographic publishing [29], more than three million of French residents (approx. 4% of the total population) were subscribing to an on-demand streaming music platform in 2016, and roughly 1/3 of the total French population regularly stream audio content. The data recorded by streaming platforms offer great possibilities to analyze and hopefully better understand individual and collective listening practices.

What they discovered is mostly what you might expect, but it is still fascinating:

Our analysis confirms a number of properties previously highlighted by research based on interviews and questionnaires, but also uncover new statistical patterns, both at the individual and collective levels. In particular, we show that individuals follow common listening rhythms characterized by the same fluctuations, alternating heavy and light listening periods, and can be classified in four groups of similar sizes according to their temporal habits - “early birds”, “working hours listeners”, “evening listeners” and “night owls”.

I wonder which one corresponds to the typical programmer? t is interesting to note that the only one that doesn't show a steep drop off at peak TV hours is the Night Owl. 

It isn't clear at all what this next observation means:

We show that different genres encourage different listening habits, from Classical or Jazz music with a more balanced listening among different songs, to Hip Hop and Dance with a more heterogeneous distribution of plays

Now to the all-important question: how many tracks does a DJ have to play to be reasonably sure that they play something that everyone knows. 

Finally, we provide measures of how distant people are from each other in terms of common songs. In particular, we show that the number of songs S a DJ should play to a random audience of size N such that everyone hears at least one song he/she currently listens to, is of the form S ∼ N ^α where the exponent depends on the music genre and is in the range [0.5, 0.8].

Perhaps most interesting of all is the conclusion:

More generally, our results show that the recent access to virtually infinite catalogs of songs does not promote exploration for novelty, but that most users favor repetition of the same songs.

So more is not necessarily an advantage and the repetition effect in music is as important as you might have guessed it was.  

If we are reluctant to explore how do we find new music?

For me the mechanism is that I listen to the radio - internet radio of course - but I have to listen to a lot of things I don't like before the miracle happens that I find something that I do like.

Surely there should be a recommendation system that could help? 

Random is good but algorithmic is better. 

More Information

Headphones on the wire

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