Author: tunequest

Clair de Lune, visualized

Here’s a pretty neat visualization of Debussy’s best known work, Clair de Lune from the Suite Bergamasque. It was composed in 1903 and is quite famous.

Though I generally prefer string arrangements of this piece, this solo piano version is more faithful to the author’s original intent. Plus it’s easier to visualize and still sounds pretty cool. Enjoy.

This music and video was put together by Stephen Malinowski and his Music Animation Machine. You can find more performances on his YouTube page and more information on his website.

A tunequest wordle

Wordle is a neat little tool I discovered over the summer. It takes blocks of text and turns it into a picture highlighting the most commonly used words. More common words appear in larger type. You can input text by copy and paste, by pointing it toward a rss feed, or by giving it a del.icio.us username to analyze. When it is finished, it presents you with an attractive graphic that you can stylize by changing the layout, font and color scheme.

With a few moments to spare this afternoon, I decided to run my iTunes Library through it, to see what words appear most frequently in my song titles. I did this by exporting my library to a tab-separated text file, opening it in a spreadsheet and copying all the song titles into Wordle. In total, it was more than 58 thousand words and it took several moments for my iMac to paste all of them into the Java applet, but I was quite surprise to see Wordle itself chew through the list without any delay.

Below are is the result:

A lot of generic music terms, like ‘title’, ‘mix’ and ‘theme’ as well as a good representation of classical music. If I really wanted a qualitative look, I could always edit out those common words, but for now I think what I have is pretty cool.

Get your own Wordle.

Side note: Frequent readers may have noticed that my writing frequency has slowed to barely a trickle as of late. The reason being that I’ve re-entered college to pursue a second bachelors degree I’m changing fields. So between a full time job and ten credit hours at a school an hour’s drive away, I find myself with very little time for hobbyist writing. That said, I’m unwilling to completely abandon the site. My output may be at a trickle, but it’ll still there. So keep that feed reader fired up for the occasional return of tunequest.

The Simpsons – Songs in the Key of Springfield

Released in the spring of 1997, Songs in the Key of Springfield is the first album to feature music that appeared on The Simpsons. The songs span from seasons 2 through 7, with the earliest song being Tony Bennett’s Capitol City from Danicin’ Homer and the latest being In-A-Gadda-Da-Vida sung by church-goers from Bart Sells His Soul.

(Though technically a version of the Itchy & Scratchy theme appeared in the first season episode Krusty Gets Busted, the recording included on the CD is from 2nd season’s Itchy & Scratchy & Marge. Similarly, the Treehouse of Horror theme used on the CD is from season 5, but a version of it was used in season 2.)

At nearly an hour long, the album distills some of the best and funniest music of the franchise’s history. Classics such as the Stonecutters’ song, the Monorail Song, the Oh, Streetcar! and Stop the Planet of the Apes musicals, and Tito Puente’s tale of uptempo retribution: Señor Burns never fail to get me singing along.

The highlights for me however, are the nine excellent renditions of the Simpsons’ theme sprinkled throughout the album like toppings on a donut. These versions take their inspiration from the topic of an episode and rework, sometimes radically, the show’s familiar exit music into various styles and tributes. My favorites, just for their sheer creativity, are the Big Band, Afro-Cuban and Australian versions, as well as the “Dragnet” homage. Big kudos to show composer Alf Clausen for those.

There’s not a bad song on this record (though Lisa’s ‘Round Springfield jazz eulogy to Bleeding Gums Murphy can be grating). While listen to Songs in the Key of Springfield, long-time fans will wax nostalgic for the show’s finest days, from when The Simpsons truely was the best thing on T.V. This music goes a long way to cementing that reputation.

Rating:
Tunequest rating: 8.8

Surprisingly though, the album appears to be out of print and not available for digital download. Amazon offers the CD via third party sellers as well as bundled with Go Simpsonic (the sequel album). iTunes doesn’t offer it at all.

The Stonecutters’ Song, which explains all of history’s great mysteries:

Smart Playlist Idea: Eldest Tunes (that need attention)

This is a fun little playlist.

In my last Smart Playlist example, I showed how to create a list of the most recently added songs that had not reached a certain play count. Today’s list takes the opposite tack: what are the oldest songs in the library that haven’t been played a certain number of times. I call it “Eldest Tunes” and it is a great way to give attention to songs in your library that have, for whatever reason, found themselves neglected. I’ve found it to be particularly good for reminding yourself just how much you loved those songs of yesteryear.

Before I continue though, I have to point out that if you started your music collection before July 2002, then this playlist works best if you’ve back-dated your library’s Date Added field.

Setting the playlist up is actually ridiculously easy. Here’s a screen shot of mine:

Playlist is tunequest

Songs must be in my master playlist. If you haven’t set up a master playlist, then you can leave this criterion out.

Play count is less than 4

This number is arbitrary and can be whatever you want. In my case, I want to find songs that have been played 0-3 times. When a song reaches 4 play counts, it can no longer appear on this list.

Last Played is not in the last 3 months

This is a recycling mechanism. If, like in my example above, you’ve set your threshold to 4 and then listen to a song that only has 1 play count, the count increases to 2 and the songs stays on the playlist.

When I first put this playlist together, I found that I was listening to the same songs over and over again for that reason. It was taking multiple listens to rise above the 4 threshold that I had set. So I added this rule that says once a song has been played it is “embargoed” for 3 months, giving other songs an opportunity to be heard.

Finally, the last two criteria:

This is the actual engine of the playlist. Limit your playlist to the number of songs you want, but make sure you select least recently added. That least recent part is why it’s important to have accurate info in your Date Added fields. iTunes uses the date in that field to determine which songs will go on this playlist and in what order.

In my case, the oldest songs that currently appear on my Eldest Tunes playlist are from Primus’ Pork Soda and Blind Melon’s debut. I originally bought them over the summer 1993 (backdated in iTunes), but I’ve hardly listened to them in the past few years, so their play counts are low. But now that I’ve seen that they’ve been under-appreciated for so long, I can take steps to give them them proper consideration.

As for your playlists, I wish you happy listening!

Cumulative lifetime play counts

A rambling, self-indulgent, inconsequential post about habits, statistics, speculations, accumulation and missing data.

I can’t help but be disappointed that I can’t see lifetime stats for my music listening habits. In these days of play count-tracking programs like iTunes and websites like Last.fm, it’s easy to get caught up in the musical trends of your life. It’s especially interesting when you look at the numbers and discover that you perhaps don’t like a certain style of music as much as you thought you did or you find that you listen to a particular band much more than you would have guessed.

The problem is that your revelations are only going to be as good as the data you’ve collected. I’ve been a “serious music listener” for about 16 years, yet Last.fm has only been tracking my habits for three and my iTunes library only goes back six. I have ten years worth of listening that I will never have any way to quantify simply because the data was never collected.

Missing data, of course, skews results and in this case, snapshots of my habits are skewed in favor of recent years, especially when looking a cumulative lifetime stats. Using data from my library as it stands today, I put together a graph of my most popular years in music. I’ve been for the most part, a contemporary music listener, so the vast majority of my library contains music released from 1993-2008, adding new releases each year.

I calculated the total number of play counts received by all songs in my library that were released in a given year. Here’s the result:

itunes play counts by year

This graph shows the distribution of all my play counts generated since July of 2002 (when iTunes began recording them). We see a peak in 2001 and a general downward slope since.

My explanation for the shape of the graph is that, as years come and go and a music library grows, newer music receives more attention than older music. Familiar tunes give way to new acquisitions and explorations. However, those old tunes never entirely go away; they continue to co-exist with the new ones. As the years pile up, each one’s presence is diluted among the rest and it becomes and increasingly uphill struggle to for the songs of a new year to reach parity with those of the past.

So in this particular graph, I attribute the 2001 peak to the simple coincidence that the songs from 2001-early 2003 were in high rotation at the time that iTunes started tracking play stats. As a result, the initial rate of change for those songs was quite high. And even though the rate at which those songs get played has decreased (exponentially) over time, the songs from other years still have to compete with them for attention, so we find a general trend decreasing cumulative play counts.

Average Play Count by Year

Further evidence of this idea can be seen in the average play count for the songs of each year. There’s a bump in the 2003-2004 area, reflecting the idea that older songs tend to accumulate more play counts over time.

I can’t help but wonder what that play count graph would look like if iTunes had been released in the early 1990s? How much cumulative lifetime play would we see throughout the years?

Of course, there’s no way to figure that out. That information is trapped in the fog of memory, stored in transitory listenings of cassette and compact disc. But while that individual play counts may be lost forever, it might not be impossible to make a decent educated cumulative guess.

I’ll start with the premise that from the years 1993-2001, I averaged a mere 10 songs per day between school bus rides, studying, hanging out, commuting and partying from early high school, through college and my entry into the workforce. That’s probably a conservative estimate, considering the general lengths of my bus rides and commutes. Heck, I’ve managed to generate nearly as many plays in the past 6 months, and I’ve lately been slacking on my music listening in favor of podcasts and audiobooks. But 10 is a good number, so I’ll stick with it.

So, at 10 songs per day, that’s 3650 plays per year. Consider the state of my collection in those early years. Throughout high school and into college, I managed to add records to my library at an average rate of one per week. If iTunes had been around at the time, play counts by now would be heavily concentrated in those early additions, with the highest concentrations being in the earliest records I bought.

By the end of the first year, my estimated 3650 plays would be spread among a mere 500ish songs, an average of 7.3 per songs. By the end of the next year, another 3650 plays would be spread out among about 1000 songs, 3.6 per song. Except that I expect that drop off in older songs to be exponential, not linear.

After some more conjecture and guess work, I extrapolated the accumulation of play counts over the years. After some number-crunching, I had a graph that looks like this:

cumulative play counts by year adjusted

The blue line is the same as above, showing the cumulative distribution of play counts by year of release in my iTunes library. The green line represents what the graph would look like if my estimated historical plays were added to the existing totals.

What does this totally unscientific, made up graph tell me? Basically what I already suspected: that I’d have to stop listening to my older tunes altogether and for a long time if I ever wanted current tunes to “catch up.” Of course, in the time it would take to do that, future tunes would be at a deficit. So really, while it’s a somewhat nice visualization, in reality it will have no bearing on my future plans.

Standard Deviation of the Years in my iTunes Library

I spent part of the past weekend doing some basic statistical analysis of my iTunes Library. I’ve been collecting music for 16ish years now, so I decided to see what kind of historical trends I could find.

One task I assigned myself was to look at the variety of the time span of the releases in my collection. Now I don’t have to do any fancy calculations to tell you that the vast majority of the songs in my library date to the same 16 year period that I’ve been collecting for. Indeed, if you line up all the songs in my library in chronological order by release date, the Median year is 1998. That is to say that half the music in my library was released before or during 1998 and the other half was released during or after that year.

The next step I took was to look at the variety of the release years for each calendar year that I’ve been collecting. I did that by segmenting my library by each year since 1993 using iTunes’ Date Added field, then calculating the standard deviation of the Year field for every song on that list. The lower the result, the more “consistent” that year’s additions were. The higher the number, the greater the eclecticism in that year’s acquisitions.

The results are plotted in this graph:

The green line is the standard deviation for my library as a whole.

In the 90s, I was pretty much an “alternative rock” junkie, so the span of years is pretty narrow overall. But see the bump from 2000-2002? That was late college and my hipster days, when I really had all the time in the world to haunt record shops, variety stores and Usenet groups in attempts to explore the most obscure nonsense. I mean, Morton Subotnik and film scores to Godzilla movies. That kind of nonsense.

It’s cool though, I also discovered Can and Neu! during that same time.

Ratatat – LP3: Expansive and diverse sounds

In the past, I’ve taken issue with the tendency in some circles to lump Ratatat’s music in with that of the 8-bit crowd. I can understand the temptation, what with the band’s programmed, electronic beats, screaming guitars and ample keyboarding. But while their tones may sometimes sound similar to those produced by the Nintendo Entertainment System, their origins are much more organic.

So it surprised me to see that Ratatat appears to be overtly embracing the 8-bit sound while simultaneously diversifiying its non-electronic sound on its latest record, the straight-forwardly titled LP3. This record is a virtual homage to the keyboard. Indeed, the album cover features three of them. The effect is that just about any sound that can be produced by playing the keys finds its way onto this record somewhere. Indeed, one of the lead tracks, Mirando, mixes the bright and clean upper register of a grand piano with the laser beam-like sounds of an 8-bit system near its crescendo.

Don’t fret though, the duo haven’t thrown their guitars away. In fact, Ratatat seems to be well on their way to finding world peace and ultimate truth, the wailing guitar, Wyld Stallyns way. But even there, the stringed instruments shows some surprising variety. Again, the cacophonous Mirando mixes Ratatat’s thrashing riffs and slide guitars with the interjection of a banjo.

Other standout tracks include the disc’s opener, Shiller, which spends most of its time as a contemplative, baroque-style dirge before exploding into a high-flying spaced-out waltz. From there, LP3 hits overdrive with Falcon Jab further demonstrating the band’s new-found commitment to diversity. The guitars talk Peter Frampton style, the percussion is accented by shakers, and the keys of a harpsichord and baby grand trade expressions.

Mi Viejo has a distinct world-music flare, like a caravan moving up and down over the crests of sand dunes. Likewise, Mumtaz Khan shows a distinct Middle Eastern flavor, like what you might expect to find in a Turkish nightclub. Meanwhile Shempi, another highlight, is a wurlitzer-powered merry-go-round spinning through hyperspace. Gypsy Threat takes on the atmosphere of a Scooby Doo chase through an abandoned carnival.

Of the thirteen songs presented here, there’s only one that could arguably be referred to as a “typical” Ratatat song. With its mid-tempo beats and harpsichord melodies, Dura would almost feel at home as the backing track for one of Ratatat’s infamous remixes if it weren’t such a compelling track on its own.

With three albums under their belt, Ratatat has consistently shown themselves to be on the top of their game. But that game keeps expanding, with each successive album adding a new layers of complexity and textures to the band’s modus operandi. LP3 shows that whatever sights they set for themselves, they’ll reach them with gusto.

Note, the Amazon MP3 store offers a exclusive bonus track: Shempi [E*Rock Remix].

Mirando video: