Alphabetization Is Not Fit for Music Libraries

Wikipedia’s arti­cle on alpha­bet­i­za­tion explains:

Advan­tages of sort­ed lists include:

  • one can eas­i­ly find the first n ele­ments (e.g. the 5 small­est coun­tries) and the last n ele­ments (e.g. the 3 largest coun­tries)
  • one can eas­i­ly find the ele­ments in a giv­en range (e.g. coun­tries with an area between .. and .. square km)
  • one can eas­i­ly search for an ele­ment, and con­clude whether it is in the list

The first two advan­tages are things you almost nev­er need to do with music libraries. And the third has been sup­plant­ed by now-ubiq­ui­tous search box­es: if you know what you’re look­ing for, you search; and if you don’t, an alpha­bet­ized list is not the way to find it.

Web vision­ary Ted Nel­son (<mst3k>Dr. Ted Nelson?</mst3k>) has been para­phrased as point­ing out that “elec­tron­ic doc­u­ments have been designed to mim­ic their paper antecedents,” and that “this is where every­thing went wrong: elec­tron­ic doc­u­ments could and should behave entire­ly dif­fer­ent­ly from paper ones.” If the fold­er metaphor is inad­e­quate for dig­i­tal doc­u­ments, no won­der it’s so piti­ful at han­dling music. The prox­im­i­ty between pieces of music in a library should least of all be based on the first let­ter in a band’s name – it’s as arbi­trary as sort­ing them by the vocalist’s month of birth – yet this is how it’s uni­ver­sal­ly done.

Music library orga­ni­za­tion needs to be re-thought from the ground up. We need to con­sid­er how it is that peo­ple used to lis­ten to music before it was all on their iTunes. How are your CDs orga­nized (or dis­or­ga­nized) on your shelf? How are they orga­nized in your head? What is it that prompts you to lis­ten to what you lis­ten to when you lis­ten to it? And how can we use com­put­ers to adopt and enhance these ways of think­ing, rather than forc­ing us to think like com­put­ers? Con­tin­ue →

Intelligent browsing in foobar

Col­lect­ing my thoughts here…

foobarSo, iron­i­cal­ly, music is becom­ing increas­ing­ly dif­fi­cult for me to lis­ten to. As though wor­ry­ing about an exten­sive gaunt­let of tag­ging pro­ce­dures isn’t enough, I just have too much damn music. Brows­ing alpha­bet­i­cal­ly through upwards of 500 artists is not the best way to go look­ing for some­thing when you have no idea what you want to hear.

I’ve audi­tioned var­i­ous meth­ods of tweak­ing foo­bar to ‘deliv­er’ music to me more or less auto­mat­i­cal­ly, and I’m close to hav­ing some­thing ide­al. The playlist tree com­po­nent allows for dynam­ic tree struc­tures (which, unfor­tu­nate­ly, can only be rebuilt man­u­al­ly or every time a new song begins); using the title­for­mat­ting lan­guage, I’ve gen­er­at­ed five queries whose pur­pose it is to ‘coax’ cer­tain albums to stark­er vis­i­bil­i­ty from the fea­ture­less and indif­fer­ent music library, to greater or less­er suc­cess.

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AllMusic’s Tone Intersections

In a pre­vi­ous post about A Flat Hier­ar­chy for Sub­jec­tive mp3 Tags, I described the ardu­ous and mar­gin­al­ly reward­ing task of tag­ging my entire library with as many ‘tones’ tags as All­Mu­sic was able to pro­vide. With foobar2000 0.9 final now less than a week away, these tags may prove use­ful soon enough. But a few weeks ago, impa­tient and curi­ous, I decid­ed to put them to anoth­er use:

tones intersection chart

By cre­at­ing a tones/tones tree struc­ture in foo­bar, I was able to count how often each ‘tone’ inter­sects with every oth­er ‘tone.’ What you see above is the begin­ning of that data col­lec­tion, which I ulti­mate­ly planned to ana­lyze in…some way.

After Googling around for ideas on tag clus­ter­ing, I came across gCLU­TO, a free piece of soft­ware that would, mirac­u­lous­ly, do exact­ly what I need­ed — name­ly, mag­i­cal­ly fig­ure out how best to clus­ter each tag with relat­ed tags. I fig­ured four clus­ters would be a com­fort­able num­ber, based on ear­li­er read­ing I had done on a two-axis the­o­ry of musi­cal emo­tion (intense/relaxed and positive/negative).

topographical cluster visualization

Unfor­tu­nate­ly, my com­put­er sim­ply couldn’t han­dle even con­struct­ing and decon­struct­ing the foo­bar tree with­out freez­ing up for about 45 min­utes each time. Plus, col­lect­ing all this data would have meant hours and hours of work, for a goal whose ben­e­fits weren’t very clear to me at all, as well as a halt in incor­po­rat­ing new down­loads into my library. It was a pret­ty excit­ing cou­ple days while it last­ed though.

A Flat Hierarchy for Subjective mp3 Tags

I’ve always been anal about the way my mp3s are tagged. Before the iPod, Audio­scrob­bler, and foobar2000, it was an irra­tional obses­sion, since I keep my music well-sort­ed on my hard dri­ve. But there’s some­thing so “offi­cial” about mp3 tags that I find appeal­ing.

A few years ago this fix­a­tion extend­ed to a pro­gram called Mood­Log­ic, which applies a user-main­tained data­base of real­ly spe­cif­ic infor­ma­tion about songs to con­struct playlists to match par­tic­u­lar moods. In the end it proved more work than it was worth for me, so I aban­doned it, but I’ve always wished for a sim­i­lar­ly intu­itive method of music brows­ing and playlist cre­ation (come on, alpha­bet­i­cal­ly?).

The genre tag has always been the most elu­sive. The sub­jec­tive if not total­ly base­less dis­tinc­tions between “Pop/Rock,” “Rock,” and “Pop” are enough to aggra­vate even the mildest case of OCD. I nev­er both­ered with this kind of cat­e­go­riza­tion until recent­ly when I real­ized that foobar2000 can han­dle mul­ti­ple val­ues for one tag field. Inter­est­ing…

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