 Look
through someone's else's CD or MP3 collection and you'll probably find
it makes no 'sense' to you. How could this person possibly like this
and also this? How could she like this but not that?
The Internet is now replete with tools that purport to connect you with
music you will surely like based on information about what you have
already documented liking. For the most part, it bases these
recommendations, Amazon-style, on correlations in other people's
collections of preferred music. This, too, makes no 'sense'. It means
that no matter what you say you like, the recommender will tell you
that you will also like the Beatles, U2 and Madonna.
Some more recent tools, like Last.fm and Pandora,
try to be a bit more sophisticated in devising their recommendations.
Pandora is based on the Music Genome Project, which analyzes music
according to its attributes:
Taken
together [each song's] 'genes' capture its unique and magical musical
identity - everything from melody, harmony and rhythm, to
instrumentation, orchestration, arrangement, lyrics, and of course the
rich world of singing and vocal harmony. It's not about what a band
looks like, or what genre they supposedly belong to, or about who buys
their records - it's about what each individual song sounds like.
Pandora's
music analysts check off which of 400 attributes each individual song
has, and then, as you give thumbs up or down to various songs in its
library that you listen to, it recommends other songs that have the
same or similar attributes. Pandora is available only to Americans,
and I am not prepared to invent a US zip code to cheat the system
(though it would be easy to do so), so I can't really say how well it
works, but before it forced me to register (and then told me, as a
Canadian, I couldn't register)
its recommendations were just awful. What's worse, you can only give a
quick 'thumbs down' to so many songs before it forces you to listen to
some from start to finish, in accordance, apparently, with its
licenses. Extremely frustrating, hugely labour intensive (every song
they add has to be 'expertly' analyzed and tagged), and it fails to
recognize that we love songs as much for the context in which we first
and most often heard them as for their analytical attributes like "hard
rock features, acoustic rhythm piano, varying tempo and time
signatures, mixed acoustic and electric instrumentation and paired
vocal harmony".
Last.fm relies more on its members than
musicologists, and because it isn't limited to licensed music, it can
recommend artists and songs that your 'neighbours' (people whose song
lists correlate most closely with yours) like, even if they're obscure.
And it can be programmed to monitor your iTunes listening, so you don't
have to tell it what you like and what you're listening to -- if you
listen to more than half of a song, it assumes you like it, and logs
it. The sheer number of members and songs it correlates allows it to
use neural rather than analytical logic to make recommendations, but it
relies heavily on the frequency of listening to artists
rather than to specific songs, and most artists produce a wide variety
of songs. It does have an intriguing more popular/more obscure 'slider'
that you can tweak to get rid of the Beatles, U2 and Madonna.
So
far it has logged 1650 songs I have listened to, and recommended about
500 artists I might like. Some of these came from 'groups' I joined of
people who ostensibly tend to like the same types of music I do --
these recommendations were useless, zero for 150, to the point I
unjoined several groups so I wouldn't have to wade through any more.
The rest of the recommendations came from correlations either with
other members or with 'tags' -- members can tag the genres of music
they listen to, so the recommender can get a sense of what tags you
tend to prefer, and weight its recommendations towards artists
similarly tagged. These tags are autopoietic (folksonomic) -- they
evolve as members pick new tags and abandon old ones. So for example
last.fm tells me I tend to like music tagged as 'shoegaze' genre.
Of the 350 artists recommended to me so far using these neural heuristics, I have liked at least one song of sixteen of them:
- Female Singer-Songwriters: Imogen Heap (UK), Anna Nalick (US), KT Tunstall (UK), Inara George (US), Lucie Silvas (UK), Zero 7 (UK), K's Choice (Belgium), Nina Gordon (US), Alex Parks (UK), Lene Marlin (Norway), Nerina Pallot UK)
- Folk Singers: Nickel Creek (US)
- New Age/Instrumental/Acoustic/Soundtrack Artists: William Ellwood (US), Thomas Newman (US)
- 'Supergroups' that I didn't know about that include artists on my playlist: Frou Frou (UK) and The Wreckers (US)
The
recommendations also include a few African, Latin, and Classical
artists (all disproportionately represented on my playlist), but none
of them worth a second listen. None of these genres is well represented
in last.fm's massive database. Very few Canadian artists have been
recommended either, possibly because with 80 Canadian musicians (two
thirds of them women) in my playlist already, there may not be a lot
more out there to recommend -- though I'm dubious of this.
In
five of the 16 cases, last.fm has been able to play me 30-second cuts
or even (through its Recommendations Radio station selecting music
randomly from artists it has recommended to me) full-length samples of
these artists' work. For the other 11, I had to Google to find the
artists' home pages and listen to samples there, or use the more
extensive MySpace artist page samples, or (perfectly legal in Canada,
per our Supreme Court) file-sharing services, to hear these artists'
music.
For someone with tastes as peculiar and fussy as mine, 16
out of 350 is pretty good -- My MP3 player has only 800 songs by 300
different artists on it, spread over about 40 years of listening, so to
have found 16 new artists I like in just three months is remarkable,
despite the investment required in finding music of and listening to
334 crappy artists to find the 16. The recommender has 'learned' to
send me a heavy weekly dose of 'female singer-songwriters' (a tag
connected with much of my playlist), and a disproportionate number of
UK & European artists (perhaps because last.fm is UK-based and has
so many members there).
In contrast, the now-defunct Rock Chicks
Radio introduced me to only 4 new artists I really like, after a
similar investment in listening time. But I'll miss RCR -- they played
a better mix of music than any off-air, Internet or satellite radio
station out there, even though I knew almost all of the artists they
played.
Last.fm is far from perfect, however. I suspect because
most members are young, listening to about 20% 1960s music quickly
stereotyped me, and my identified 'neighbours' soon included lots of
Canadians in their 50s, even though most
of what I was listening to was contemporary. My 'neighbours' seem
mostly rooted in the past, and just a few oldies got me lumped in with
them, and produced recommendations of some truly dreadful bands of that
era. I've found it more fruitful to listen to 'tag radio' -- music
tagged with 'female singer-songwriters', 'acoustic', or even
'shoegaze'. I'm disappointed at the lack of depth of African, Latin,
Classical, Folk, New Age and Soundtrack selections and recommendations.
Yahoo Launchcast seems to be
pretty good at letting you listen to music of selected genres, though I
think it's outrageous it doesn't work with Firefox.
But none of
this gets at the Three M's that, I think, really determine whether you
will like a song or not: Melody, Memory and Mathematics. The composition
of a song goes far beyond 'attributes' -- just like a bird's songs,
each song is unique, and will appeal to different people, or not,
depending on how it resonates, emotionally and intellectually, with the
listener, and largely independent of who sings it or what instruments
are used to play it. I like both the Doors' original version and Jose
Feliciano's cover version of Light My Fire -- utterly different, but
still essentially the same song, the same composition. I think we are
'programmed' to just like
certain songs -- it's an evolutionary thing. I also think we are
'programmed' to like certain voices -- which doesn't mean we like everything we hear in those voices, but rather that we are predisposed to like songs by certain artists because the tonal quality resonates with something inside us.
Memory
also plays an enormous role in whether we like a song or not. A song is
essentially a story, and we will like songs that we remember in a
positive context (e.g. initially or most often heard while we were
doing something we loved) far beyond their musical merits. If the
lyrics, or even the name of a song or a singer, evoke a certain memory, our like or dislike of the song will be tainted by that memory.
And the mathematics of a song also speak to us, and are either consonant or dissonant with the mathematics of our brains and bodies. Certain chords and harmonies
(major sevenths, minor ninths, suspended seconds) strike me, for
example, as poignant, fraught with meaning, simply because of the way
the overtones of the notes hit my eardrum together and rumble along the
neural pathways to my brain. Likewise rhythms and the pacing
of songs (the staggeringly complex rhythms of African Zoukous music,
the soothing flow of samba, the teasing offbeat of merengue, or the
halting pace of the adagio of Ravel's Concerto in G) either fit, or
don't fit, with the syncopation of our souls, and that, rather than
intellectual discernment, will determine whether we love a song with
those 'time qualities' or loathe it.
So, composition, tonal
quality, memory, chords & harmonies, rhythm & pacing, all
determine how a song will be received by our musical 'taste buds'. And
how these things all 'work together', the result of effort of the
producer more than the artist (the song's 'production values') is also
important. It is all about the senses, and about chemistry. Whether we
like a song or not is not really our choice, as much as we would like
to credit or fault our intellectual appreciation for our judgement.
There are those who, like wine 'connoisseurs', will proclaim one
selection 'superior' to another, and tell you why, but they are merely
proclaiming their own filters, frames and prejudices, telling you not
about the singer or the song but about them, the critic. Sheer vanity.
This is all about complexity
-- there are too many variables to analyze or predict in any useful or
reliable way. Using neural approaches, as last.fm has done, is the
right way to try to tackle the problem, but it's like doing surgery
with a spatula: the instrument is really not up to the subtlety of the
task. I can sigh and say that finding 16 needles in a haystack of 350
is at least a vast improvement over the one-in-a-thousand I was batting
scanning through the radio spectrum. But, just as I know the cure for
some horrific diseases lies buried in some tiny plants in the dwindling
rainforest, so I know there are songs out there that would define me,
change me, transport me, pull me out of the darkness, or show me the
truth of the universe -- if only I could find them.
Image: Representation of a major seventh chord by Stewart Butterfield, co-founder of Flickr. |
MADE IN CANADA
trust your
instincts 
