There was an interesting panel on Commercial Music Discovery and Recommendation, which I found a little bit discouraging. The message seemed to be that academic research and corporate development are different things and shouldn’t be confused. Elias said something to the effect that given the choice between 10x more data and an algorithm that was a few percent better, he’d take the data. Brian said that companies do what they do pretty well and academics should focus on doing things that companies can’t. Anthony didn’t foresee developments in MIR affecting him very much, predicting instead that user interface was the area that could improve the online music experience the most.

For more thorough coverage of the conference, take a look at some of the other ISMIR attendes’ blogs. Google blog search pointed me to a number of posts (in pagerank order): Paul Lamere (one of many), Elias Pampalk, Michael Good, Justin Donaldson, Jeremy ?, Kris West, Luke Barrington, Matthias Mauch, and Karin Dressler (in German).

The other good news is that there’s a new and improved search page. Hopefully it’s easier to understand what’s going on, I’ve even added a FAQ. If you look hard enough, you might also notice a new feature I’ve introduced: similarity browsing. For each clip that was autotagged, I computed a similarity value between its autotag vector and all of the others, finding the thirty or so nearest neighbors. You can follow this web of similarity around to some fun stuff, even though it’s not a huge collection of music. Here’s a random starting place.

This similarity is a semantic similarity, as Doug Turnbull likes to call it. The the clips might not have an exactly similar sound, the rhythms might not match, or they might differ in an instrument or two, but for the most part you would describe them with the same sorts of words. If you want to know what kinds of words we’re talking about, take a look at the autotagging results. Have a look and let me know what you think (you can leave a link to any exceptional results you find in the comments).

The swim was pretty rough. I took a calculated risk and didn’t worry too much about training for the swim. It takes a lot of pool time to shave a minute or two off of a mile swim, which doesn’t matter so much in a two and a half hour race. As a result, I was passed by people from at least four different waves. It didn’t help that the current was much weaker this year than last (start time 7:15am), that there were big tentacly red jellyfish in the Hudson, or that my un-wetsuited jersey unzipped at some point. I only realized that my jersey had turned into a parachute as I got out of the water, at which point I also noticed the stinging pain in my nipples.

The bike was better than the swim. My glutes didn’t die and I managed to put up a pretty decent time without aero bars, even though it seemed like everyone else was riding a cervelo. They would pass me on the downhills, but I would pass them right back on the climbs, which there were quite a few of, as I’d learned last year. My glutes did start to heat up towards the end of the ride, but they kept working the whole time, perhaps because of the hill repeats I’d done in training or the extra effort I went through to properly fuel during the race.

On the run, I thought I was way behind. I was told around mile 2 that it was 9:30am, which I thought meant that my time was already 2:45. In reality, it meant that my time was 2:15, which I didn’t realize until around mile 5, when I passed another Columbian who had started in the wave before me. I felt fine running across the finish line, but as soon as I stopped running I nearly passed out. After I finished, Joanne told me the athlete-tracking text message system said I finished in 2:41 with a 28 minute swim. But my time kept improving after I’d finished, my official time was 2:37:19 with a 24 minute swim, the discrepancy being attributable to a late start. This is 2 minutes worse than last year, but the heat and humidity set the field back even more and I moved up from 95th to 52nd in my age group. Graham did quite well, even with an injured leg and his friend Matt Worstell finished 4th in our age group again this year.

For my reference later, there are lots of stats online from this year and last, some pictures taken by professionals, some pictures taken by my mom, and a video of me crossing the finish line.

We have designed a web-based game, MajorMiner, that makes collecting descriptions of musical excerpts fun, easy, useful, and objective. Participants describe 10 second clips of songs and score points when their descriptions match those of other participants. The rules were designed to encourage players to be thorough and the clip length was chosen to make judgments objective and specific. To analyze the data, we measured the degree to which binary classifiers could be trained to spot popular tags. We also compared the performance of clip classifiers trained with MajorMiner’s tag data to those trained with social tag data from a popular website. On the top 25 tags from each source, MajorMiner’s tags were classified correctly 67.2% of the time, while the social tags were classified correctly 62.6% of the time.

1. Clip-Tag classification: determine whether each tag applies to each clip or not
2. Clip retrieval: for each tag, rank the clips by their relevance
3. Tag retrieval: for each clip, rank the tags by their relevance

There are only a few days left until the submission deadline, but if you want to throw something together, more submissions would be great. In case you’re wondering, the main contributors to the design of this task have been Kris West, Thierry Bertin-Mahieux, Doug Turnbull, and Greg Tsoumakas. Mert Bay is running things at IMIRSEL.

We describe a system for separating multiple sources from a two-channel recording based on interaural cues and known characteristics of the source signals. We combine a probabilistic model of the observed interaural level and phase differences with a prior model of the source statistics and derive an EM algorithm for finding the maximum likelihood parameters of the joint model. The system is able to separate more sound sources than there are observed channels. In simulated reverberant mixtures of three speakers the proposed algorithm gives a signal-to-noise ratio improvement of 2.1 dB over a baseline algorithm using only interaural cues.

Multiple-instance learning algorithms train classifiers from lightly supervised data, i.e. labeled collections of items, rather than labeled items. We compare the multiple-instance learners mi-SVM and MILES on the task of classifying 10-second song clips. These classifiers are trained on tags at the track, album, and artist levels, or granularities, that have been derived from tags at the clip granularity, allowing us to test the effectiveness of the learners at recovering the clip labeling in the training set and predicting the clip labeling for a held-out test set. We find that mi-SVM is better than a control at the recovery task on training clips, with an average classification accuracy as high as 87% over 43 tags; on test clips, it is comparable to the control with an average classification accuracy of up to 68%. MILES performed adequately on the recovery task, but poorly on the test clips.

In a sentence, it’s a story about a baseball, a guy from the bronx, America during the cold war, and waste. The first chapter is most of the story about the baseball, and it’s excellent. Either it was turned into a novella after Underworld was published or it was a novella that Underworld was built around. It describes in astonishing detail the home run hit by Bobby Thompson to win the national league pennant in 1951. I couldn’t put it down.

That first chapter exemplifies my impression of the book, that it is extremely well-constructed. The characters are interesting and deep, and the descriptions are rich if sometimes over the top. It’s a solid novel, you see where it’s going and it takes you there with the inevitability of sunrise after dusk, the methodicalness of a marathoner.

This inevitability, however, eventually became ponderousness and the backwards timeline ended up detracting from the novel. Delillo fell victim to his own writing talent. The beginning sections of the book were so well written and so engrossing, I wanted to know what happened next. But instead, I had to read 700 pages of why it happened, the backstory. There’s something of a reprieve in the epilogue, set in the present, but it’s not much of a payoff. While it is interesting to see the years of a character’s life peeled away, it certainly wasn’t as engrossing as it might have been.

So I ordered a new Dell desktop with ubuntu pre-installed. It’s up and running now and all of my data is transfered over. It’s got a pentium dual core, 2 gb of ram, and a 320 gb hard drive so it can serve as my general compute server as well. And it only cost about $400 with tax and shipping. The old machine has served me well, but will have to be retired, although the hard drive lives on (for now) in an external enclosure. Hopefully this one will last me eight years as well.

As he describes it, the IMF and Wold Bank provide loans to developing countries to bring them into the US’s sphere of influence. These loans pay for infrastructure projects like dams, improvements to electrical grids, highways, etc. These projects are forecast (by an economic hit man) to spur massive, sustained GDP growth, which would allow the country to pay off a big loan. When the country gets the loan, it immediately uses it to pay American contracting firms like Bechtel to actually build the project. The local elite-run utilities get their upgrades and the growth forecast turns out to be much too optimistic, leaving the country with an unsustainable debt that must be paid back in favors. Countries and rulers amenable to such a system are termed “friendly”, rulers who resist always manage to be on the plane that crashes in the rain forest. The whole story sounds believable to me and agrees with the Latin American history he describes.