The catalogs beginning to appear on the net in the last year are a step
in the right direction, but not all the way there. It's still a big
problem for a new act to get noticed amidst the ocean of competition.
This means that merit alone will not do the trick -- that must be
supported with other promotional methods and often some kind of mentoring
from inside the industry.
With some luck, and a sustained effort over a long time, enough
word-of-mouth can sometimes allow a new act to get recognition to the
point that they can "hit the big time." But, this is far from certain. It
is very important to understand that, in the current state of the music
business, hard work, dedication and "stick-to-it-iveness" guarantees
nothing. The myths that run the machine don't acknowledge this, but it is
the reality that underlies the music business.
What I would love to see, but seems a decade or two away, is an online
database that automatically (i.e., with well-defined algorithms
requiring no subjective judgments by any human beings) evaluates and
stores descriptions of music in a large database. A user might enter a
search spec for a piece of music that "sounds about half like A and about
half like B" (where A and B are particular pieces the user is familiar
with) and the system will automatically come up with the top-N choices,
ranked on closeness to the blend specified by the user.
There are selection systems in development (and maybe coming into actual
use) where users can respond to a questionnaire of some kind, as to what
music they like, and then the system can search for pairs of entries that
have higher-than-expected correlations (a simple statistical analysis).
For example, if fans of Bruce Springsteen are more than typically likely
to also like Don Henley, then that would show up when a Springsteen fan
asks the system for suggestions as to other music to look for.
But these systems rely on user feedback which has two serious drawbacks:
user subjectivity (why should I have to adhere to what you think?), and
lack of comprehensiveness (it still requires a certain minimum number of
users to have heard your music and included it on their feedback before
you start showing up on the suggestion lists). Basically, it moves from
whims of a few individual reviewers to the whims of a larger group of
people. But it's still a case of "majority rules" and an individual who
has unusual values will be less likely to find what they're looking for.
The obstacles facing a truly automatic system are twofold.
One: the process of transcribing a complex polyphonic sound sample into a symbolic
notation adequate to describe the variables that would be used to "score"
the music is not at hand. Eric
Scheirer, who is working on this task at the MIT Media Lab, predicts
one to two decades before this will be achieved (he did a
that involved a system that was able to fine-tune the nuances of a
performance of a piano piece for which the complete score was already
given and entered, but analyzing a multi-instrumental recording with no
score is well beyond any current algorithms).
Two: deciding what variables should be stored is not necessarily clear
(it would require understanding mathematically what the human ear
recognizes in complex sounds). The MIT ML folks are more optimistic about
this (they refer to an eigenvector analysis, sometimes known as a
"principle components" or "factor" analysis). However, until this kind of
research can be carried out in real life, it is impossible to know the
results of such investigations.
If anyone knows of a source of significant funding for such research, be sure to
email the webmaster!
-- Dan Krimm, 3/96