|By Euan Wilson, originally published in the July 2011 Socionomist|
Got a growing music collection? Looking for the perfect music for the perfect situation? Kieran Stafford of Sydney, Australia, could have a solution.
Stafford is seeking a patent for a program that examines music collections, then catalogs and displays songs’ signatures graphically. The program will enable users to find songs by signature and may one day help them “discover” music from different eras on the fly using socionomics.
Stafford notes that many consumers fail to catalog or tag their digital music files. They end up with “vast collections … without the means to sort through them.” Their only option is to create playlists, which over time can become almost as unmanageable as the collections themselves.
Businesses such as shops, restaurants and cocktail lounges, where music is an important part of daily operations, face a similar challenge:
Many businesses cannot dedicate the resources to build and catalogue a music collection or spend the time listening to music to determine its appropriateness for inclusion in their collection. Consequently, many businesses (and individuals) end up having a “safe” but limited collection, or rely on radio or a piped background music service.1
The data generated by Stafford’s program differs from Pandora’s and Amazon’s sources, which produce text-based, as opposed to graphical, displays. In contrast, users of Stafford’s program browse their collections based on the visual representations of the data and then select filters ranging from era to genre to lyrical structure to country of origin. We asked Stafford how else his system differs from Pandora or Amazon. He explained:
We have created a large database of music with a unique taxonomic classification system which allows this type of discovery. Think of it as a highly intelligent recommendation system. It differs vastly from Amazon or the iTunes recommendations systems, which are based on what other people bought. Most tracks on iTunes have never been purchased [and therefore go undiscovered when you’re looking for a recommendation].
Stafford’s program compares attributes of the songs themselves. Harmony, melody and style play a role in the recommendations. “We don’t expect much musical knowledge from the user—it’s about serendipitous discovery of similar music that the user had no prior knowledge of,” Stafford notes.
One of the most notable features of the program is its socionomic potential. Stafford explained in his patent application:
In the field of socionomics, music is considered a good indicator of collective social mood. … The [program] provides an indicator of market mood … it enables tracking of music trends over time and correlations to be drawn with other indicators, such as share market activity or real property prices. No known music library or other database can perform this function.1
Digitally comparing music to other indicators is a cool possibility. We postulate a further application: that tunes from different periods that show similar signatures are likely to correlate to music from other periods that have the same wave labels. The notes, tones, harmonies—even lyrical content and mood—should be comparable. If, for example, a user likes big band swing music from the 1920s, Stafford’s program might not only identify other bands that record swing but also bands that produce various kinds of music from similar mood periods—such as the 1990s. Sexy, theatrical pop music like that produced by Elvis Presley might find a mirror in late 90s boy bands. This approach is similar in intent, if not model, to that used by the video rental company Netflix and other similar services, which track and extrapolate the interests of a user to determine other selections that he/she might enjoy.
Because Stafford’s program shows data visually, it could provide socionomists powerful new evidence for the idea that Elliott waves—and the popular music they generate—have distinct personalities that they share with the same waves from different eras. When we broached this possibility, Stafford was cautiously optimistic: “At this stage, it’s a very long bow to draw, but we do think it’s possible given enough data to find underlying trends.”
Here’s to possibility.■
1Means for navigating data using a graphical interface. United States Patent Application 20110035380. Patents.com, Retrieved from http://patents.com/us-20110035380.html.
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