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?

The most natural method for organizing music (if you can escape alphabetical thinking for a moment) is by similarity. Last.fm does this, and it is invaluable. When you are at an artist’s page at Last.fm, you feel that you are in that artist’s “neighborhood,” with links to similar bands, tags, listeners, and related groups. The Last.fm architecture was designed to manifest organic, bottom-up communities around bands and genres. This is an experience that cannot currently be replicated in any music player, at least not easily (with the possible exception of Amarok). But because Last.fm’s data is extraordinarily accessible, there are virtually no obstacles to incorporating this sense of “musical neighborhoods” into a piece of software.

If you don’t already have an artist in mind whose neighborhood you’d like to browse, you probably at least have some idea of the kind of mood you’re after, and there are several approaches here. One (perhaps the least viable) is using Last.fm’s tags. These are actually less often concerned with mood than they are with genre, a taxonomy well-known as being inconsistent and, I would argue, misguided: When I want to listen to Lullatone, it’s not because I want to listen to electronic music; it’s because I want to listen to “whimsical,” “delicate,” “innocent,” “sparkling” music. Who cares what genre it is?

AllMediaGuide began a project called Tapestry some time ago, an application of their vast mood/situation/genre dataset. It is an ideal solution for browsing music, and its integration into desktop software would be hugely rewarding. It’s possible to simulate Tapestry with foobar2000 and some elbow grease, but the results are not as robust as they could be.

Again, if you already know what you’re looking for, it would be difficult to find it through these channels; but this is what search is for.

We also need to consider the less objective and more personal reasons that music becomes relevant in specific contexts, analogously to the way in which CDs become disordered on one’s shelf. I, for instance, usually have about 20 albums littering the top of my receiver and speakers. These include, roughly, (a) stuff I just bought, (b) stuff I just listened to, and (c) stuff I haven’t bothered putting away because I know I’ll listen to it again soon.

The main obstacle to browsing in this way is a prevalent shortcoming whose symptoms are far-reaching: the fact that music players “think” in terms of songs, not in terms of albums—or even in terms of artists, for that matter: My music software doesn’t know that these 38 songs are all by Electrelane; it just knows that their artist metadata is alphabetically adjacent. Sure, you can sort iTunes libraries by data such as “last listened” and “added”; and you can use CoverFlow to simulate a pale approximation of a flesh-and-blood record collection; but the only way you can sort albums or artists is alphabetically. I’ve written about the ways in which MP3Toys addresses this problem, and it remains a commendable pioneer in music management, but its difficulties (a steep learning curve, a buggy interface, a rapid release schedule) outweigh its advantages.

Browsing your own music library is a very impersonal experience, despite enormous potential for personalization. Rich info visualization “toys” such as Last.fm Extra Stats and LastGraph are seen as novelties, but would, in fact, be revolutionary as library browsing environments. There is nothing to prevent this from development, either; even users who are not plugged into Last.fm could have their listening history stored locally by their music software, which could then be used to generate small, cached, infinite-resolution SVG histograms, browsable by zooming, panning, and clicking. Far from being cumbersome and CPU-intensive, it would actually be rather elegant.

Continuing on the theme of chronology, what about a simple calendar charts view, with varying granularity by day, week, month, quarter? Presentationally, these charts could even be made easily to resemble vertical stacks of CDs, with spine art generated from a cropped cover image and overlaid text. This would arguably be eye-candy, of course, but just imagine how it would “feel” to see your music this way. If there’s one thing Apple’s been consistently right about, it’s that functionality is not at odds with a pleasant user experience, but rather that they are meant to be mutually supportive.

When I was helping to establish some playstamp tagging standards with the foobar community in 2005, it was jokingly suggested that when songs are played they be tagged with the current weather. Despite the sarcasm, I couldn’t help but think, “What a great idea!” I know my listening habits are affected by the weather, and I can’t imagine other people are not the same. There is, after all, a frequently-used “rainy day” tag at Last.fm.

What about a histogram based not on play count, but on hotness values over time? What about artist similarity webs based not on Last.fm data, but on proximity of play times within your personal history? What about taking lessons from the DONTCLICK.IT project, BumpTop, and the pile metaphor for unprecedentedly fluid user interfaces?

All the ingredients are there. Everyone is rapidly moving towards an exclusively digital music collection, and the technology is embarrassingly outmoded. Music has become a major component of computing, at levels once reserved for word processing and gaming. Our relationship with our digital music collections is poised for reinvention, a looming difficulty that has been made invisible by custom and habit. Digital music management is hell, and users have complacently accepted this.

The obvious solution at this point is Songbird. Songbird’s media views (present since 0.5) allow more easily than ever for custom browsing environments. Previously the only way to alter your music browsing environment was to switch programs entirely; besides which, nearly all available programs simply mimic the well-known disk/directory views or iTunes’ browser pane view (which is just a glorification of a disk/directory view anyway). Songbird, on the other hand, boasts an unprecedented extensibility, coupled with media attention, ease-of-use, and the Mozilla platform, for which people have been developing extensions for ages (in computer years).

The prospects are thrilling and the potential for innovation is virtually limitless. Promisingly, there are some glimpses of where things might be headed for Songbird media views: Catalogue View demonstrates a novel visual presentation of your library, though it doesn’t do much in the way of organizational presentation; and Metrics Media Page is the beginnings of the kind of infovis view that could be (but currently isn’t) adapted to allow for actual navigation. Nevertheless, I have a bad feeling that this opportunity will be missed, as the status quo continues to obfuscate these possibilities.

Update: Part II

So, 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.

• Random 10: Maybe my favorite query. This chooses ten albums at random and, in a clever twist of which I’m pretty proud, appends a short description of the album to the end of the title in the tree, using one adjectivial ‘tone’ tag and one nounal ’style’ tag as taken from allmusic (the hard work pays off!), e.g., “Sprawling Post-Rock/Experimental” for Mogwai’s Ten Rapid. This last bit is helpful as I don’t even recognize much of the stuff in my library.
• Never Heard: A given. This chooses ten random albums on the condition that they contain at least one song that hasn’t been played. Unfortunately there’s no way to filter for albums that have, say, at least half of their tracks unplayed, or even a fixed number of unplayed tracks for that matter, so the title of this query is a bit misleading. And, because I’m even more likely not to be familiar with these, descriptor phrases are also included in the display of this tree (and in none of the remaining trees).
• Recently Added: Fifteen albums chosen for having a track that has recently been played for the first time. Again, somewhat misleading, since very old albums that happen to have a recently debuted song will appear on this list, when I’d really like to restrict it to recently added albums. If only foobar could keep track of when stuff is added to the library. Helpful for reminding me of new stuff I’ve acquired.
• Recently Listened: Like ‘Recently Added,’ but narrowed to albums whose last play is at least a month from their first play (or, more specifically, whose last play was not in the same month as their first play). This, ideally, will display old(ish) albums that I’ve found myself listening to lately. And, like ‘Recently Added,’ useful in its results — if I chose to hear it recently, I’m likely to want to hear it again.
• Incoming: A live reflection of the contents of my incoming folder. This would be great ’cause then I wouldn’t have to remember to open Windows explorer and drag new stuff into foobar to add it to the library, it would all just flow in seamlessly, even the .zips I download (since foobar can read archives). Unfortunately Playlist Tree is still in beta and this feature seems to be broken at the moment. Once it’s resolved, though, the circle will be closed and everything will be perfect and the skies will open up and we will know the face of God.

My only remaining frustration is that playcount data basically has to be written to the files, something that cannot be worked around by write-protecting the files as I had previously been led to believe. My anticipated work-around will involve getting uTorrent to copy all downloads into another directory, one that it’s okay for foobar to mess around with, keeping seeded torrents in some quarantined directory that I never really touch.

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).

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 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…

allmusic does a pretty decent job of providing many (sometimes too many) adjectives it calls “styles,” “moods,” and “themes” for artists and individual albums. These adjectives can be semi-automatically added to mp3s on a per-album basis with tagging program The GodFather and a special patch. The tag fields “styles,” “tones,” and “situation” are created and the values are separated by commas. Using foobar2000’s masstagger to split the fields and Columns UI’s Albumlist Panel to sort your collection by these fields, you can produce something like this:

Here my music is sorted by tones, which is a much more natural way to find what I’m really in the mood to hear. Some of the tones allmusic assigns can be ambiguous or even useless (what does “crunchy” mean and why does it only apply to White Light/White Heat?), but the benefit of this system is that every album contains multiple values in its tones tags, and so White Light/White Heat can also be found under “Brittle,” “Confrontational,” “Harsh,” “Cerebral,” “Ominous,” “Fiery,” “Intense”…only to name a few. But if you focus on browsing only the tones with a medium degree of popularity, you’ll find what you really want to hear much faster. And you’ll find yourself listening to way more of the second half of the alphabet than you have been.

I didn’t know it as I was applying this system to my foobar2000 config, but it’s an example of what’s called flat hierarchy. I can’t explain it better than this post on beelerspace, which is a pretty wonderful blog in general.

Now here’s the really exciting part. There is also a database search component for foobar2000 that features a “search for same” option. For instance, it allows you to right-click on a song in your playlist and generate a new playlist of all tracks in your database with the same artist, album, year, etc. So imagine this: it’s Sunday afternoon and you’d like to make up a playlist, but you’re not sure what you want on it. You do know that you want to hear White Light/White Heat, so you load that album, right-click on one of the tracks and generate a playlist with matching values in the tones tag. Now you have a playlist with tons of stuff that sounds like White Light/White Heat!

Unfortunately it’s not that simple. Currently the database search component only allows you to match one value or all values in your search. Because White Light/White Heat has 21 tones values, matching just one of those tones is pretty easy; in my collection that search returns 1,895 tracks. Matching all 21 tones, on the other hand, narrows the search all the way down to just this album. In the future the database search might have the capability to match a user-specified number of values, in which case matching three or four tones might return a comfortable ten or fifteen similar albums. When that happens, you won’t be able to justify not using foobar2000 much longer.