Where do you go when you want to hear new music – a friend, or a computer?
Sounds like a trivial question, but it becomes rather a pressing one if you are a (friendless or otherwise) broadcast journalist looking for appropriate music to capture the mood of the piece you’re working on.
Large media organisations provide options – most significantly a library of production (and commercial) music, and often a librarian or researcher (or two) who will take the hassle out of finding appropriate music, using their knowledge and expertise.
As a freelancer or independent, you will find no shortage of production music out there, where you can browse and pay a nominal fee. And as for commercial music, if you want a quick and dirty solution, you can always piggy back on the marketing types at commercial breaks and beats (come on – we all know the first thing you think is ‘what was that track I heard last night on the latest Subaru advert???’).
But for those of you seeking a more personal touch, the options available in this most hit-and-miss of fields in which to recommend, are beginning to seriously open up.
At its most basic level, you can always start with the Listmania section in Amazon (whether or not you are convinced by your own ‘personalised’ suggestions, once logged in). Here people pull together lists of their favourite albums around genres, moods, time-spans, and many other more abstract nuances.
But these selections can be pretty convoluted (to say the least). They can be more of an intellectual than an instinctive means of gathering together music – people create lists by sitting back and thinking what would go well together.
If you want a more instinctive range of selections, then you would do well to set yourself up with a LastFM account. Once you’ve downloaded the software, the music you listen to on your MP3 player will be communicated to your account, which will be updated in the system.
You can then search LastFM for bands you like, and will be presented with lists of other users who have also recommended these bands.
Once you’ve clicked on a profile you will see what they have listened to most recently – and so get a more gut-driven sense of what people are actually listening to – and what’s out there.
I decided to blog this subject following a conversation with a BBC colleague of mine, Andy Aldridge, who is a bit of an ‘early adapter’ when it comes to the whole musical recommendations lark.
The difference between the way LastFM and The Filter recommend music is subtle – both use playlist recommendations using your favourites – albeit The Filter uses a statistical model (Bayesian inference) to filter results. As Andy points out though, both are essentially rooted in the ‘tyranny of the crowd’ approach you will find in the more basic Listmania suggestions:
The sign up forces you to rate artists which is likely to be as useful to them for working out what OTHERS like as is it is in working out what you like!
In a practical sense though, one reason why its worth persevering with LastFM over The Filter for now (despite their similarity), is because their full song licensing deals mean you can actually listen to the music you’ve been recommended within the same application.
Andy then pointed me in the direction of a radically different approach to anything already covered – the Music Genome Project (steady! Its still bubbling away in development, and ain’t ready yet). The recommendations proposed from this service will incorporate mathematical interpretations of what you like, rather than what others like.
So what are the pros and cons of these two different approaches?
Well, with the former (to which any scholar of 19th Century Utilitarianism will attest) you have a very blunt instrument when applied to human affairs. Those who shout the loudest stand a good chance of making the biggest impact – which is far from ideal (as it is far from idealism).
However, one practical advantage of external influence, is that it encourages you to re-try music you may have listened to in the past but dismissed. If left to your own devices, you might never return to it, but the badgering of the crowd may be more effective in getting you to give something another try.
With the Music Genome Project approach you risk getting trapped in the echo-chamber of your own tastes – it’s a potentially solipsistic approach to finding music, and is miles away from those traditional means of finding new music we are all familiar with, and which have served us well (radio, friends and family etc.). Indeed, if we can’t get computers to deal passably with creating and using spoken language with its structure and rules, how on earth can you expect a system to delve into your unconscious and unpick what you ‘like’ in music?
That said, you will at least evade the ME ME ME-type hysteria that can drive the crowd.
The exponential rise in music (both professional and amateur) available via social nets and other sources, is driving a need for ever more sophisticated means of filtering out what you like.
It will be interesting to see whether systems based on the tyranny of the crowd, or the solipsism of the self, succeed.