In Which Spotify Leads Me to iTunes Home Sharing

The story of how iTunes and Spotify co-exist as a near-perfect music listening environment.

itunes + spotify = happy music

So I was recently sent an invitation to join the “exclusive” ranks of American Spotify users. I had been hearing about the free all-you-can-stream music service for quite some time, but never gave it much serious thought. Maybe it was my years being stranded in a slow-Internet hell, but I’ve never held streaming services is especially high regard. Besides, with a music library as large as mine, I am usually hard-pressed to find many songs that I want to listen to at any given moment that are not already stored in iTunes. And I’ve invested so much effort into that little jukebox that, at the press of a button, I could literally listen to music for 55 days straight and never hear the same song twice.

Despite that, I signed up for Spotify anyway, just to see what all the fuss was about. My initial impression is that the rave reviews are justified. The program is simple to use: search for song->press play. And though the collection of available songs is quite extensive, it is not quite up to the bold marketing claim of “every song ever”. There have been a handful of (admittedly obscure) tracks and artists that I’ve been disappointed not to find.

Rarities aside, if you want to hear it, you can find it. Around here, Spotify has quickly replaced YouTube as the go-to source for one-off indulgences or for finding songs that come up in discussions but aren’t in my iTunes library.

The real power of Spotify is in its immediacy. Virtually any song can be found in a matter of seconds, which makes it a superb platform for exploration. I no longer have to buy or bootleg an album just to see if I’ll like it. I can listen to it in its entirety as many times as I want. And I’ve already spent a considerable amount of time cruising the full catalogs of artists I hadn’t thought about in a while. I had no idea PJ Harvey had so many albums.

A lot of deep cuts, b-sides and compilation appearances are also available. So if you like to dig into the music of your favorite artists, Spotify is a great way to do so. All-in-all, a big thumbs up for Spotify from me. I can see myself getting a lot of use from the service in the future.

A few other observations:

  • Albums often have the wrong year associated with them. Nine Inch Nail’s Head Like A Hole single/EP was released in 1990, not 2011. And there are plenty more examples where the date is more recent than it should be. So many so, I don’t trust the dates until I’ve seen them confirmed by an outside source. I don’t know where Spotify gets its data from, but this needs to be fixed.
  • Tricksy Spotify pauses commercials if you mute the computer, so there’s no avoiding them.
  • However, songs are not paused if the computer is muted. This is disappointing, especially after seeing the behavior with the ads.
  • The ads themselves cover the usual self-promotion gamut (encouraging the exploration of Spotify’s full range of services), as well as promotions for new content (artists, albums, etc). I don’t mind the promos, but the content ads can be a little jarring. It’s quite disconcerting to have your mellow Yo La Tengo marathon interrupted by boisterous ads for Thug Rapper Whozit or Popstar Whatserface. If the ads were more relevant to my interests (say, based on my listening habits), I wouldn’t really mind them. But as it stands I just find them annoying. Neither the artist nor record companies nor I receive any benefit from having Top 40 content directed at me.

So what does all this have to do with iTunes?

The thing is, I like Spotify. And I can see how it has the potential to be a massive disruptive influence on people’s music listening habits. The murder-obsessed tech media has been calling it “an iTunes killer” for some time, even before it was available in the States.

And it certainly had an effect here; it made me realize how much I missed being able to listen to music at my computer. Crazy thought isn’t it? I love music, but for the longest time, technological limitation has made it difficult for me to listen to it in the places I most often am.

Since the day iTunes came out, my library has been too large to fit on the internal drives of any computer I’ve ever owned. As drives got bigger, so did my library. Thus the library has been forced to live on an external drive connected to my iMac for its entire existence.

I, however, rarely do any actual work on my iMac. I’m more frequently found using a laptop from the comfort of a couch, bed or dining table. These days, the iMac mostly acts as a server and information hub/data manager.

My library is a rather complicated thing. The shear size of it necessitates a rigorous organizational structure (taking full advantage of multiple smart playlists based on play counts, play dates, star ratings, etc) to be used effectively. And traditionally, the problem has been that it’s hard to listen to music in places other than the iMac without corrupting the integrity of the library.

This effectively meant that my music library was isolated from the other computers in the house. All my vast quantities of tunes inaccessible save for some workarounds of various success:

  • Grabbing files over the network directly and/or using a third-party music player program. Advantage: playing music from the laptop itself. Disadvantages: no interaction with the library.
  • An iPod. Advantage: Since iPods are extensions of the iTunes library, I didn’t have to worry about breaking my organization. Disadvantage: requires headphones, limited music selections, metadata/playlists only updated at sync time.
  • iTunes Sharing/LAN streaming. Advantage: Access to the full library from any computer in the house. Disadvantage: does not update song metadata, so I could listen to songs, but my playlists would not get updated or refreshed.
  • Airfoil, essentially pushing music from the iMac to other computers. Advantages: music is sourced from the main library and can be played simultaneously on multiple computers. Disadvantages: cumbersome to set up, difficult to control.

This is the environment I was operating in when I discovered Spotify. Not a group of ideal solutions there.

Spotify’s ease of use reminded me that I wanted to easily play my music on a laptop. But as much as I like Spotify, using it exclusively is out of the question. For one, there are songs in my library that aren’t on Spotify. It also doesn’t share/sync local files (that aren’t in its database) between computers, so the app wouldn’t actually solve my multi-Mac music dilemma. Ultimately though, what’s the point of having a meticulously maintained, tagged and organized music collection sitting in the next room if I’m just going to stream it all over the Internet? Besides, there’s no guarantee on Spotify’s lifespan; its service could change or be shutdown completely in the future. My local library will remain usable even if Apple goes out of business tomorrow.

Also, no smart playlists. And I loves me some smart playlists.

While Spotify isn’t a solution for me, it did inspire me to look for others. My first inclination was to look at iTunes LAN streaming, which allows the client laptop to access and control the music. I’d seen some solutions to the metadata problem in the past, but none of them ever worked for me. I hoped that there had been more recent developments.

Enter Home Sharing

Fortunately, one of the first search results I found pointed out the oft-overlooked preference in iTunes to allow Home Sharing to update play counts. Until this point, I had never considered Home Sharing. When Apple introduced the feature in iTunes 9 two years ago, it was pitched as a way for family members to share music among their computers. Since I’m only one guy, I didn’t think, like parental controls, that the feature would be of much use to me.

So I ignored it.

My mistake. Home Sharing is the next best thing to having my master iTunes library synced up between iMac and laptops. From my laptop, I can load the shared library, play songs and the metadata is updated when finished, which keeps my organizational schemes intact. Plus, I can use Home Sharing to move actual files between the master library and a satellite library, which helps improve my management workflow. The only thing I can’t do is edit the song or any playlists on the remote library. But that’s a limitation I can live with.

This is such a big breakthrough for me, that I don’t think I’ve been this excited since the invention of smart playlists. I’m just ashamed it took me two years to figure it out.

From the comfort of my couch, I can, within the span of seconds, be listening to any song in my collection. And for everything else, there’s Spotify.

App: TonePad- Tenori-On for the rest of us

Allow me to share with you one of my favorite apps in all of Apple’s App Store: TonePad. You see, I’ve been intrigued by the elusive (and expensive) “visual music composition device” known as Tenori-on since I first heard about it a couple years ago. And since I don’t have the time to make the most of a thousand dollar diversionary investment like the Tenori-on, only an intrigue it has remained.

Which is why I took notice when I first read about TonePad, an app for iPhone/iPod Touch that replicates a part of the Tenori-on concept. And since downloading it, I can’t stop making minimalistic, dreamy tunes with it.

Usage is straightforward and simple. On launching the app, the user is presented with a 16 x 16 grid of dots, where the rows represent the beats and the columns represent 16 tones, with higher pitches at the top of the grid. Press a dot to activate that particular note and each time the measure loops to that beat, a tone is played. For visual feedback, each dot pulses as it is played. Combine dots into chords and melodies, and voila, you’re making music.

The tones themselves are pleasant, with a small reverb applied, making it hard to create a “bad” song. Sure, swiping a finger across the interface may not make for the most compelling of compositions, but it certainly doesn’t create the mess that mashing a keyboard or piano does.

As fun as TonePad is though, it does suffer from some limitations. For one, the composition options are fixed. The tempo, time signature and tone are set to a default, and on a default they must stay. You can’t make the loop any faster or slower, or change the number of beats in the measure or change the basic sound of the tone (or make it another sound entirely). Also, you’re limited to working within just the one loop. It would be pretty nice to be able to set up a loop and have it continue to play as you put together another loop to layer on top (and it would be especially nice to do it with different base tones). Finally, and this one can’t really be helped, but the dots are small enough that they can be troublesome to accurately press. There have been a number of times when I wanted to turn one off and ended up turning the neighbors on.

But hey, I’m not really complaining. TonePad is both fun and free and a worthy app to carry in one’s pocket.

Enjoy some TonePad improvisation from yours truly:

On the Web:

Smart Playlist Idea: The Anniversary Playlist

Ever wonder what you were listening to three, four, five or even ten years ago? Or maybe you want to look back and wonder “have I really been listening to this album for that long?”.

Enter The Anniversary Playlist.

By setting two simple Date Added parameters in a Smart Playlist, you can make a self-updating playlist of all the music you were listening to a given number of years ago. It makes a great little time machine.

Here’s one to get you started: 5th Anniversary Tunes.

Anniversary Playlist
click for full size

No matter how far back you want to go, you only need the Date Added selectors and a little simple math.

First selector: Date added is in the last XX months
This criteria includes every song you’ve added to your iTunes library in a given number of months. Since we’re talking years here, we need to multiply the number years by 12 to get the number of months. 5 years = 60 months. But since we want to have a slice that’s slightly older than 5 years, we add 1. So all music that was added in the last 61 months is added to the playlist.

Second selector: Date added is not in the last (XX – 1) months
But we don’t really want our playlist to include all the music that’s been added in the past 61 months, so we use this criteria to exclude everything that’s newer than 5 years old. This leaves us looking at a window of exactly one month from 5 years ago. As each day passes, the window moves and older songs drop away and are replaced with the more recently added.

Looking at my library, I see a number of songs from March 2004. It seems that it’s now been five years since I discovered Elbow (which makes me wonder the aforementioned “has it really been that long?”) as well as filled out my Stereolab singles collection. Also, Tortoise needs a new album. It’s been five years since the last one.

To adjust the window, simply change the number of months back to look. One year ago would be 13 and 12 months, six would be 73 and 72, and so on.

Smart Playlist Idea: My Favorite Nostalgia

I’ve been having so much fun with a new iTunes Smart Playlist for the past month that I’ve just got to share it. The basic premise is to relive my entire musical history in rough chronological segments, with the goal of drifting through the highlights of the various eras of my musical life. Contemporaneous songs are grouped and played near each other, creating a nostalgic soundtrack to your life.

It’s proving to be a fun trip down memory lane as I associate particular songs with particular moments, like that 10th Grade state science fair trip (powered by grunge supergroup Mad Season), Christmas vacations, the first mp3 I ever downloaded, that first year of college (where my early pop-rock leanings begin to mix with my discovery of electronic music), that first Stereolab track and other sundry milestones.

As I’ve been working my way through the playlist for about a month, each day ha brought great tunes and great memories.

Before we get going, some initial statements about the playlist though:

  • For best effect, you should have a fairly significant musical history. Mine stretches back seventeen years and just going through “the best” songs it’s taken me about a month to listen through the first six of them. The slowness is part of the journey for me though.
  • Chances are, if you have a long music history, you also have a rather large library. Part of the fun that I’m having with this project is the anticipation as I wonder what song will be played next. So, while a large library isn’t necessary for this project, it will be more fun if you have a large pool to draw from.
  • I keep bringing it up, but yes, this works best when the songs in your iTunes Library have the proper Date Added: the date you actually acquired the music, not just the date you added it to your iTunes Library. It’s the backbone of the playlist, really. If you’ve been building your iTunes Library since it came out in 2001, you should be good. If you’ve been collecting music longer than iTunes has been around, see how to back-date your songs. It’s tedious, but worth it.
  • A considerable portion of the songs in your library should be rated. The goal of this playlist is not to listen to every song in your library, but only your favorites from past to present. The star rating is how we filter all the best tunes.

That all said, how do we create this wondrous playlist? It’s actually a very simple couple of selectors.

Making the Smart Playlist

Start a new Smart Playlist and add the following criteria:

nostalgia smart playlist selectors
click to enlarge

My Rating: 5 stars
This makes sure only your favorite songs enter the playlist.

Last Played is Before {today’s date}
This selector initializes the playlist. Any song played before this date is eligible for inclusion. And songs are automatically removed from the playlist after you listen to them.

(Optional) Playlist is masterPlaylist
I use a master playlist to make sure my tunes are on the up and up. It filters my library so that only songs that are properly tagged, with correct date added, etc are included in other playlists. If you don’t have or want a master playlist, you can leave this selector out.

Limit to XX items selected by Least Recently Added
This is what really makes the Smart Playlist work. The number you use for XX really depends on the size of your library and how large a “slice of history” you want to listen to at any given moment. I keep mine between 50 (when I’m listening in iTunes) and 100 (when I’m out with my iPod for the day). The Least Recently Added selector adds the earliest songs (according to Date Added) to the playlist and automatically replaces played songs with the next earliest ones.

Using the Playlist

The most effective way to use this playlist is via iTunes’ Party Shuffle Up Next feature (Party Shuffle is no longer part of the latest versions of iTunes). What I like about this method is that as songs are removed and replaced from your nostalgia playlist, the new songs become immediately available to Upnext, allowing for some really smooth musical transitions. The downside is that you’re chained to iTunes for all your listening.

But you can take that playlist on the road via iPod (or iPhone). It works just like any other playlist. Keep in mind though, that for the true “river of time” experience, try not to listen to the entire playlist in a single sitting when mobile. This has the effect of creating a hard break in the listening by completely clearing out all the oldest unplayed tunes, then replacing them with the next batch of songs. It divides the experience into chunks rather than the “smooth river” that I find so appealing. My solution is to listen to, at most, half the playlist during any given session. That way, when go to update the playlist, the new songs are intermingled with the unplayed ones.


Well there it is, the most fun I’ve had with a playlist in quite a long time. Hopefully, your nostalgic adventure will be as rewarding as mine has been.

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 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.

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, 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 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
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.