Tag Archives: itunes

TuneUp for iTunes Review

Is your iTunes library filled with tracks identified by “Track 01,” “Track 02,” “Various Artists,” “Unknown Album” and so on? Are your eyes sore from looking at all those missing covers when using Cover Flow? If so, then read on; TuneUp might just be the tool you need to clean up your music library once and for all.

TuneUp Logo

TuneUp is a music library management tool for iTunes that will help you identify all your un/mislabeled tracks, find missing cover art and even tell you when your favorite band is playing in town.

Once installed, TuneUp will launch itself any time you run iTunes and dock to the right of the iTunes window. Once there, a simple drag-and-drop from iTunes will get the magic going. TuneUp creates an acoustic fingerprint of each track and uses that to compare it against a massive online database of songs. It then uses this information to fill out the ID3 tags in your iTunes music collection.

So, how good is it? In short, it’s very good – almost magical. Users of the Shazam iPhone app already know what I’m talking about. That said, it’s not automatic, it won’t find every song and it won’t get every song right.

I ran an initial test with 101 unidentified songs. Nine minutes later it was done. On this first test:

  • 30 tracks were not found.
  • It tends to identify songs as part of compilation albums and not the original albums (this has been fixed in a more recent version).
  • It misidentified “Desert Rose (Melodic Club Mix)” as “Desert Roses & Arabian Rhythms” instead of “Desert Rose (Club Mix)” by Sting, from the album Desert Rose.
  • It correctly identified a number of obscure Venezuelan songs.
  • It identified a remix of Don’t Stop by No Doubt as a “Thunderclap” from a sound effects album.

After this initial test, I contacted the TuneUp crew to see what was up.

It turns out TuneUp creates its acoustic fingerprint using the first ten seconds of each song. This can lead to some interesting errors. In the case of the No Doubt song, that particular remix begins with a thunderclap sound – no amount of magic could identify it correctly. Shazam (on the iPhone) uses whatever part of the song you’re currently listening to, so the matches tend to be more accurate. It’d be nice if TuneUp randomized the part of the track it uses for fingerprinting. During informal tests, I found Shazam would give more satisfactory results than TuneUp; unfortunately, Shazam does not integrate with iTunes.

I was told a new version of TuneUp was available that gives you the option to avoid compilation albums so I downloaded it and ran even more tests.

This time I threw 442 tracks at it. It couldn’t find 46 and misidentified about ten.

Once again, it worked quite well, correctly identifying and cleaning most of my tracks. Even though it has a very good undo function, you can’t really use the Save All function. I felt much more comfortable making sure each song had been correctly identified (which of course makes the process a whole lot longer).

Of note during the extended test:

  • It correctly identified obscure groups like the Tufts Belzebubs (Go Jumbos!) and Venezuelan folk songs like “Alcaraván Compañero” (which I highly recommend you listen to).
  • Cat Stevens’s “Peace Train” from the album “Remember Cat Stevens” (according to Shazam) was id’d as belonging to the album “1971 – Das Jahr und seine 20 Songs.” I found that TuneUp’s database has a preference for non-US albums.
  • Several tracks were identified as another track from the same album. This is likely a problem with the database. Among these: “One Fine Day” id’d as “His So Fine” and “Who’s Got My Back” id’d as “Don’t Stop Dancing” by Creed.
  • “Azul” from Cristian Castro was id’d as “Azul Gris” – a different song from a different album by the same artist.
  • Sometimes, the images offered as album art were flagged as non-compatible. Picking a different image from the drop-down list usually cleared this issue.

All in all, TuneUp is an excellent power tool for your iTunes library. Be prepared to spend some time with your music library, though. In the end, you’ll be glad you did and your music library will be much more useful (and all that new album art? it looks great on your iPod!).

If I gave out stars on this blog, I’d give TuneUp 4 out of 5.

Available for Mac and Windows. Go give it a try… meanwhile, I’ve got 1700 tracks with no album information that need cleaning.

(Disclaimer: TuneUp gave me a Gold subscription so that I could perform this review. The free version is limited to cleaning 100 tracks.)

Is Apple Playing Games with iTunes Rentals?

Last night, my friends decided to rent Superbad since some of them had not seen the movie already. They looked for it on Comcast OnDemand but it was no longer available. I knew I’d rented it in HD from the AppleTV store, cheaper than Comcast, so we looked there as well. I was surprised to find that Apple now only has Superbad for sale at $14.99 and no HD version.

Where is the rental version of this movie? Why has it suddenly disappeared? The AppleTV was even kind enough to remind me I’d already rented this movie when I tried to buy it.

I wonder what’s going on here… is Apple carrying out some kind of experiment? Do you know of any other movies removed from the rental pool?

Chime in with your comment. We’re now using the Disqus system for your convenience.

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[image found via http://scenescreen.wordpress.com/2007/12/03/he-said-she-said-superbad/]

Rethinking Ratings

Summary: An analysis of current television ratings methods, why they’re inappropriate for the timeless internet and digital video recorder era, and suggestions for improving them.

Traditional television ratings reports let TV executives and analysts study the behavior of a particular show or series, displaying the number of viewers each show had, broken down by demographic targets. This allows the television industry to determine which show won a particular time slot (e.g., Friday 9pm to 10pm), how it performed among a particular demographic (e.g., Males 18-34yrs) and how it has evolved (in the case of serials) over time (e.g., Are there more or less people watching it).

But what happens when viewers can watch any show at any time? When viewers don’t have to choose one show over another on a rival network? What happens when you can’t tell for sure who your viewers are?

The internet and TiVos give the viewer unprecedented freedom over when, where and what to watch. Soon it won’t be possible to tell for sure how many people are watching any given show, using traditional ratings tools such as AGB/Nielsen‘s Peoplemeters. Programming executives won’t have to worry about what the rival networks are showing at the same time as their new hit show. And ratings analysts won’t be able to track a new series’ behavior by simply looking at how each episode did on its air date.

Two hit shows going head-to-head on rival networks? Not a problem: watch one and record the other for later viewing (or get it from the Net). Missed last week’s premiere episode? No problem there either: watch it online, download it off bit torrent or pay for it on iTunes. Some of this you can easily track, but some you can’t.

Analysts will need to track each episode over time and then track the series as a whole. A VERY SIMPLIFIED graphic might look something like this, with a running total for each episode over the time of the series:

VERY simple ratings graphic

Any viewer can watch any episode from its air date to the end of the series (and beyond). This allows viewers to catch-up after the series has started or to catch any episode they may have missed. Of course, the whole concept of missing an episode disappears in the TiVo/Internet model. But in addition to tracking how many times a particular episode was watched or downloaded, you should also be tracking what’s happening with the rest of the show’s internet presence. Are viewers reading the characters’ blogs? Are they discussing the show in the forums? Are they setting up fan websites? Linking to the Myspace profiles? Uploading mashups of show clips? Not only must you track the show’s behavior over time and over several distribution methods, but you must also track and measure the user experience surrounding the show.

And finally, how do you solve the demographic problem: if you don’t know who your viewers are, how do you target them? The answer is both simple and complex. I believe that traditional demographic targets are on the way out. Social networks and special interest groups are the new targets… and these are much easier to track via the Internet than the old ones. You may not be able to tell whether a particular viewer is male or female, young or old, wealthy or not, but you can tell what news s/he reads, what games s/he plays and which people s/he hangs out with (to a certain degree, of course). One minor detail… you can’t (or shouldn’t) add apples and oranges. Traditional television ratings data categorizes viewers by demographic targets such as age, sex, location and income (because someone takes the time to visit each household in the sample and verify this information). And whereas traditional ratings analysis has always relied on a sample set of data subjects, internet traffic and behavior analysis has always examined the whole dataset. Eventually it shouldn’t be too hard to homogenize both sets of data, either by linking traditional television viewers to their online behaviors, or simply by expanding their interviews to include enough data to categorize them.

Currently, Google and YouTube limit their video data to a traditional web-traffic analysis mindset: most viewed, most recent, most subscribed. Coming from an Internet world, they fail to see the need (or maybe even the possibility) of better, more detailed reports (Yes, it could also be that they keep these reports hidden from the outside world).

As for me, I’d love to know how the most watched videos on YouTube evolved over time. Have they peaked? Are they growing? Who watches them? How about a Google Finance like chart, linking views to blog/news mentions? Which video has been linked-to the most (this one is actually on YouTube)? Which videos have been dugg and how many diggs did they get? Actually… I’d just love to work there and get it done myself!